Welcome to sourcepredict’s documentation!¶
Homepage: github.com/maxibor/sourcepredict
Introduction¶
Prediction/source tracking of metagenomic samples source using machine learning
SourcePredict (github.com/maxibor/sourcepredict) is a Python package distributed through Conda, to classify and predict the origin of metagenomic samples, given a reference dataset of known origins, a problem also known as source tracking.DNA shotgun sequencing of human, animal, and environmental samples has opened up new doors to explore the diversity of life in these different environments, a field known as metagenomics.One aspect of metagenomics is investigating the community composition of organisms within a sequencing sample with tools known as taxonomic classifiers, such as Kraken.
In cases where the origin of a metagenomic sample, its source, is unknown, it is often part of the research question to predict and/or confirm the source. For example, in microbial archaelogy, it is sometimes necessary to rely on metagenomics to validate the source of paleofaeces. Using samples of known sources, a reference dataset can be established with the taxonomic composition of the samples, i.e. the organisms identified in the samples as features, and the sources of the samples as class labels. With this reference dataset, a machine learning algorithm can be trained to predict the source of unknown samples (sinks) from their taxonomic composition.Other tools used to perform the prediction of a sample source already exist, such as SourceTracker sourcetracker, which employs Gibbs sampling.However, the Sourcepredict results are easier interpreted since the samples are embedded in a human observable low-dimensional space. This embedding is performed by a dimension reduction algorithm followed by K-Nearest-Neighbours (KNN) classification.
Installation¶
SourcePredict can be installed using the conda package manager:
$ conda install -c conda-forge -c maxibor sourcepredict
Usage¶
Running sourcepredict on the test dataset¶
$ wget https://raw.githubusercontent.com/maxibor/sourcepredict/master/data/test/dog_test_sink_sample.csv -O dog_example.csv
$ wget https://raw.githubusercontent.com/maxibor/sourcepredict/master/data/modern_gut_microbiomes_labels.csv -O sp_labels.csv
$ wget https://raw.githubusercontent.com/maxibor/sourcepredict/master/data/modern_gut_microbiomes_sources.csv -O sp_sources.csv
$ sourcepredict -s sp_sources.csv -l sp_labels.csv dog_example.csv
Command line interface¶
$ sourcepredict -h
usage: SourcePredict v0.4 [-h] [-a ALPHA] [-s SOURCES] [-l LABELS]
[-n NORMALIZATION] [-dt DISTANCE] [-r TAX_RANK]
[-me METHOD] [-kne NEIGHBORS] [-kw WEIGHTS]
[-e EMBED] [-di DIM] [-o OUTPUT] [-se SEED]
[-k KFOLD] [-t THREADS]
sink_table
==========================================================
SourcePredict v0.4
Coprolite source classification
Author: Maxime Borry
Contact: <borry[at]shh.mpg.de>
Homepage & Documentation: github.com/maxibor/sourcepredict
==========================================================
positional arguments:
sink_table path to sink TAXID count table in csv format
optional arguments:
-h, --help show this help message and exit
-a ALPHA Proportion of sink sample in unknown. Default = 0.1
-s SOURCES Path to source csv file. Default =
data/modern_gut_microbiomes_sources.csv
-l LABELS Path to labels csv file. Default =
data/modern_gut_microbiomes_labels.csv
-n NORMALIZATION Normalization method (RLE | Subsample | GMPR | None).
Default = GMPR
-dt DISTANCE Distance method. (unweighted_unifrac | weighted_unifrac)
Default = weighted_unifrac
-r TAX_RANK Taxonomic rank to use for Unifrac distances. Default =
species
-me METHOD Embedding Method. TSNE, MDS, or UMAP. Default = TSNE
-kne NEIGHBORS Numbers of neigbors if KNN ML classication (integer or
'all'). Default = 0 (chosen by CV)
-kw WEIGHTS Sample weight function for KNN prediction (distance |
uniform). Default = distance.
-e EMBED Output embedding csv file. Default = None
-di DIM Number of dimensions to retain for dimension reduction.
Default = 2
-o OUTPUT Output file basename. Default =
<sample_basename>.sourcepredict.csv
-se SEED Seed for random generator. Default = 42
-k KFOLD Number of fold for K-fold cross validation in parameter
optimization. Default = 5
-t THREADS Number of threads for parallel processing. Default = 2
Command line arguments¶
sink_table¶
Sink TAXID count table in csv file format
Example sink count table file
+-------+----------+----------+
| TAXID | SINK_1 | SINK_2 |
+-------+----------+----------+
| 283 | 5 | 2 |
+-------+----------+----------+
| 143 | 25 | 48 |
+-------+----------+----------+
-alpha¶
Proportion of alpha of sink sample in unknown. Default = 0.1
$$\alpha \in [0,1]$$
Example:
-alpha 0.1
-s SOURCES¶
Path to source csv (training) file with samples in columns, and TAXIDs in rows. Default = data/sourcepredict/modern_gut_microbiomes_sources.csv
Example:
-s data/sourcepredict/modern_gut_microbiomes_sources.csv
Example source file :
+-------+----------+----------+
| TAXID | SAMPLE_1 | SAMPLE_2 |
+-------+----------+----------+
| 467 | 18 | 24 |
+-------+----------+----------+
| 786 | 3 | 90 |
+-------+----------+----------+
-l LABELS¶
Path to labels csv file of sources.
Default = data/modern_gut_microbiomes_labels.csv
Example:
-l data/modern_gut_microbiomes_labels.csv
Example source file :
+----------+--------+
| | labels |
+----------+--------+
| SAMPLE_1 | Dog |
+----------+--------+
| SAMPLE_2 | Human |
+----------+--------+
-n NORMALIZATION¶
Normalization method. One of RLE, CLR, Subsample, or GMPR. Default = GMPR
-dt DISTANCE¶
Distance method. One of unweighted_unifrac, weighted_unifrac. Default = weighted_unifrac
Example:
-dt weighted_unifrac
-kne NEIGHBORS¶
Numbers of neigbors for KNN classication. Default = 0 (chosen by CV). Either an integer, or ‘all’
Example:
-kne 30
-kne all
Setting the number of neighbors to 0 will let Sourcepredict choose the optimal number of neighbors for classification. If set toall, the KNN algorithm will use all the training samples. For source proportion estimation, setting-kneto all will give better estimations.See example 2 for illustration.
–kw WEIGHTS¶
Sample weight function for KNN prediction (distance | uniform). Default = distance.
Choose to give a uniform or distance based weights to neighbor samples in KNN algorithm.
Distance base weights will work better for classification while uniform weigths will work better for source proportion estimation.See example 2 for illustration.
-e EMBED¶
File for saving embedding coordinates in csv format. Default = None
Example:
-e embed_coord.csv
-o OUTPUT¶
Sourcepredict Output file basename. Default = <sample_basename>.sourcepredict.csv
Example:
-o my_output
-k KFOLD¶
Number of fold for K-fold cross validation in parameter optimization. Default = 5
Example:
-k 5
Choice of the taxonomic classifier¶
Different taxonomic classifiers will give different results, because of different algorithms, and different databases.
In order to produce correct results with Sourcepredict, the taxonomic classifier and the database used to produce the source TAXID count table must be the same as the one used to produce the sink TAXID count table.
Because Sourcepredict relies on machine learning, at least 10 samples per sources are required, but more source samples will lead to a better prediction by Sourcepredict.
Therefore, running all these samples through a taxonomic classifier ahead of Sourcepredict requires a non-negligeable computational time.
Hence the choice of the taxonomic classifier is a balance between precision, and computational time.
While this documentation doesn’t intent to be a benchmark of taxonomic classifiers, the author of Sourcepredict has had decent results with Kraken2 and recommends it for its good compromise between precision and runtime.
The example source and sink data provided with Sourcepredict were generated with Kraken2.
If you already have kraken report formatted results (from Kraken, KrakenUniq, Kraken2, Centrifuge, …), you can use the kraken_parse.py script to convert a kraken report to a column of a TAXID count table.
Output¶
SourcePredict result file¶
File: *.sourcepredict.csv
This csv file contains the predicted proportion of each source in each sample.
Like in any classification problem, the predicted source is the greatest proportion.
Example:
+------------------+----------------------+
| | ERR1915662 |
+------------------+----------------------+
| Canis_familiaris | 0.9449678590674971 |
+------------------+----------------------+
| Homo_sapiens | 0.027033026106258438 |
+------------------+----------------------+
| Soil | 0.014110223165444446 |
+------------------+----------------------+
| unknown | 0.013888891660799834 |
+------------------+----------------------+
While in this example it is pretty clear that the ERR1915662 sample is likely a dog, you may face situations where it will be less obvious. Looking at the embedding can therefore be useful to decide from which source(s) the sink sample is made up of.
Embedding csv file¶
This csv file contains the embedding of training in test samples in lower dimensions by TSNE or UMAP
Example:
+-----------------+------------+-------------+--------------+-----------------+
| | PC1 | PC2 | labels | name |
+-----------------+------------+-------------+--------------+-----------------+
| SRR1175007 | -28.858526 | 0.59231776 | Homo_sapiens | SRR1175007 |
+-----------------+------------+-------------+--------------+-----------------+
| SRR042182 | -22.14415 | -0.47057405 | Homo_sapiens | SRR042182 |
+-----------------+------------+-------------+--------------+-----------------+
| SRR061154 | -30.210106 | -2.0323594 | Homo_sapiens | SRR061154 |
+-----------------+------------+-------------+--------------+-----------------+
| SRR061499 | -25.546652 | 0.27987793 | Homo_sapiens | SRR061499 |
+-----------------+------------+-------------+--------------+-----------------+
| SRR063469 | -22.88011 | 1.1526666 | Homo_sapiens | SRR063469 |
+-----------------+------------+-------------+--------------+-----------------+
| SRR062324 | -25.50832 | -0.25076494 | Homo_sapiens | SRR062324 |
+-----------------+------------+-------------+--------------+-----------------+
| SRR1179037 | -28.779644 | 0.1385772 | Homo_sapiens | SRR1179037 |
+-----------------+------------+-------------+--------------+-----------------+
| SRR061236 | -29.470839 | -0.8973783 | Homo_sapiens | SRR061236 |
+-----------------+------------+-------------+--------------+-----------------+
| SRR061456 | -28.31991 | -0.9834692 | Homo_sapiens | SRR061456 |
+-----------------+------------+-------------+--------------+-----------------+
| SRR1761669 | 4.1411834 | 14.485897 | Homo_sapiens | SRR1761669 |
+-----------------+------------+-------------+--------------+-----------------+
| SRR1761668 | 1.7706155 | 13.6566925 | Homo_sapiens | SRR1761668 |
+-----------------+------------+-------------+--------------+-----------------+
| SRR1761675 | 3.2434833 | 16.020077 | Homo_sapiens | SRR1761675 |
+-----------------+------------+-------------+--------------+-----------------+
| SRR3578625 | 24.127249 | 17.996181 | Soil | SRR3578625 |
+-----------------+------------+-------------+--------------+-----------------+
| ERR1939165 | 28.738718 | 19.882471 | Soil | ERR1939165 |
+-----------------+------------+-------------+--------------+-----------------+
| SRR3578645 | 24.138885 | 17.998867 | Soil | SRR3578645 |
+-----------------+------------+-------------+--------------+-----------------+
| ERR1915662 | -14.770308 | -30.94284 | sink | ERR1915662 |
+-----------------+------------+-------------+--------------+-----------------+
See the example usage of Sourcepredict for a example of how to plot it.
Methods¶
Starting with a numerical organism count matrix (samples as columns, organisms as rows, obtained by a taxonomic classifier) of merged references and sinks datasets, samples are first normalized relative to each other, to correct for uneven sequencing depth using the GMPR method (default). After normalization, Sourcepredict performs a two-step prediction algorithm. First, it predicts the proportion of unknown sources, i.e. which are not represented in the reference dataset. Second it predicts the proportion of each known source of the reference dataset in the sink samples.
Organisms are represented by their taxonomic identifiers (TAXID).
Prediction of unknown sources proportion¶
Let \(S_i \in \{S_1, .., S_n\}\) be a sample from the normalized
sinks dataset \(D_{sink}\),
\(o_{j}^{\ i} \in \{o_{1}^{\ i},.., o_{n_o^{\ i}}^{\ i}\}\) be an
organism in \(S_i\), and \(n_o^{\ i}\) be the total number of
organisms in \(S_i\), with \(o_{j}^{\ i} \in \mathbb{Z}+\).
Let \(m\) be the mean number of samples per class in the reference
dataset, such that \(m = \frac{1}{O}\sum_{i=1}^{O}S_i\).
For each \(S_i\) sample, I define \(||m||\) estimated samples
\(U_k^{S_i} \in \{U_1^{S_i}, ..,U_{||m||}^{S_i}\}\) to add to the
reference dataset to account for the unknown source proportion in a
test sample.
Separately for each \(S_i\), a proportion denoted \(\alpha \in [0,1]\) (default = \(0.1\)) of each of the \(o_{j}^{\ i}\) organism of \(S_i\) is added to each \(U_k^{S_i}\) samples such that \(U_k^{S_i}(o_j^{\ i}) = \alpha \cdot x_{i \ j}\) , where \(x_{i \ j}\) is sampled from a Gaussian distribution \(\mathcal{N}\big(S_i(o_j^{\ i}), 0.01)\).
The \(||m||\) \(U_k^{S_i}\) samples are then added to the reference dataset \(D_{ref}\), and labeled as unknown, to create a new reference dataset denoted \({}^{unk}D_{ref}\).
To predict the proportion of unknown sources, a Bray-Curtis pairwise dissimilarity matrix of all \(S_i\) and
\(U_k^{S_i}\) samples is computed using scikit-bio. This distance
matrix is then embedded in two dimensions (default) with the
scikit-bio implementation of PCoA.
This sample embedding is divided into three subsets:
\({}^{unk}D_{train}\) (\(64\%\)), \({}^{unk}D_{test}\)
(\(20\%\)), and \({}^{unk}D_{validation}\)(\(16\%\)).
The scikit-learn implementation of KNN algorithm is then trained on
\({}^{unk}D_{train}\), and the training accuracy is computed with
\({}^{unk}D_{test}\).
This trained KNN model is then corrected for probability estimation of
the unknown proportion using the scikit-learn implementation of
Platt’s scaling method with \({}^{unk}D_{validation}\).
The proportion of unknown sources in \(S_i\), \(p_u \in [0,1]\) is then estimated using this trained and corrected KNN model.
Ultimately, this process is repeated independantly for each sink sample \(S_i\) of \(D_{sink}\).
Prediction of known source proportion¶
First, only organism TAXIDs corresponding to the species taxonomic level are retained using the ETE toolkit. A weighted Unifrac (default) pairwise distance matrix is then computed on the merged and normalized training dataset \(D_{ref}\) and test dataset \(D_{sink}\) with scikit-bio.
This distance matrix is then embedded in two dimensions (default) using the scikit-learn implementation of t-SNE.
The 2-dimensional embedding is then split back to training \({}^{tsne}D_{ref}\) and testing dataset \({}^{tsne}D_{sink}\).
The training dataset \({}^{tsne}D_{ref}\) is further divided into
three subsets: \({}^{tsne}D_{train}\) (\(64\%\)),
\({}^{tsne}D_{test}\) (\(20\%\)), and
\({}^{tsne}D_{validation}\) (\(16\%\)).
The KNN algorithm is then trained on the train subset, with a five
(default) cross validation to look for the optimum number of
K-neighbors. Finally, the training accuracy is then computed with
\({}^{tsne}D_{test}\).
The proportion \(p_{c_s} \in [0,1]\) of each of the \(n_s\) sources \(c_s \in \{c_{1},\ ..,\ c_{n_s}\}\) in each sample \(S_i\) is then estimated using this second trained and corrected KNN model.
Combining unknown and source proportion¶
Then for each sample \(S_i\) of the test dataset \(D_{sink}\), the predicted unknown proportion \(p_{u}\) is then combined with the predicted proportion \(p_{c_s}\) for each of the \(n_s\) sources \(c_s\) of the training dataset such that \(\sum_{c_s=1}^{n_s} s_c + p_u = 1\) where \(s_c = p_{c_s} \cdot p_u\).
Finally, a summary table gathering the estimated sources proportions is
returned as a csv file, as well as the t-SNE embedding sample
coordinates.
Custom sources¶
Different taxonomic classifers will give different results, and the taxonomic classifier used to produce the source TAXID count table must be the same as the one used to produce the sink TAXID count table.
While there are many available taxonomic classifiers available to produce the source and sink TAXID table, the Sourcepredict author provide a simple pipeline to generate the source and sink TAXID table.
This pipeline is written using Nextflow, and handles the dependancies using conda. Briefly, this pipelines will firt trim and clip the sequencing files with AdapterRemoval before performing the taxonomic classification with Kraken2.
Pipeline installation¶
$ conda install -c bioconda nextflow
$ nextflow pull maxibor/kraken-nf
Running the pipeline¶
See the README of maxibor/kraken-nf
Adding new sources to an existing source file¶
[1]:
import pandas as pd
import numpy as np
[24]:
def add_new_data(old_data, new_data, old_labels, label):
"""
Update the sourcepredict learning table
INPUT:
old_data(str): path to csv file of existing sourcepredict source data table
new_data(str): path to csv file of new TAXID count table, with TAXID as 1st column
old_labels(str): path to sourcepredict csv file of labels
label(str): scientific name of new sample's specie. Example: 'Sus_scrofa'
OUTPUT:
merged(pd.DataFrame): merged old and new source data table for sourcepredict
labels(pd.DataFrame): updated labels data table
"""
old = pd.read_csv(old_data, index_col=0)
old = old.drop(['labels'], axis = 0)
new = pd.read_csv(new_data)
merged = pd.merge(left=old, right=new, how='outer', on='TAXID')
merged = merged.fillna(0)
old_labels = pd.read_csv(old_labels, index_col=0)
new_labels = pd.DataFrame([label]*(new.shape[1]-1), new.columns[1:])
new_labels.columns=['labels']
labels = old_labels.append(new_labels)
return(merged, labels)
[30]:
labs = add_new_data(old_data=old_data, new_data=new_data, old_labels=old_labels, label=label)[1]
[31]:
labs.to_csv("new_sources.csv")
Sourcepredict example 1: Gut host species prediction¶
[1]:
import pandas as pd
import pandas_ml as pdml
In this example, we will use Sourcepredict and Sourcetracker2 applied to the example dataset provided in the Sourcepredict directory.
The example datasets contains the following samples: - Homo sapiens gut microbiome (1, 2, 3, 4, 5, 6) - Canis familiaris gut microbiome (1) - Soil microbiome (1, 2, 3)
Preparing the data¶
[2]:
cnt = pd.read_csv("../data/modern_gut_microbiomes_sources.csv", index_col=0)
labels = pd.read_csv("../data/modern_gut_microbiomes_labels.csv", index_col=0)
This is a TAXID count table containing the samples as columns headers, and the TAXID as row indices
[3]:
cnt.head()
[3]:
| SRR1175007 | SRR042182 | SRR061154 | SRR061499 | SRR063469 | SRR062324 | SRR1179037 | SRR061236 | SRR061456 | SRR642021 | ... | mgm4477903_3 | mgm4477807_3 | mgm4477874_3 | mgm4477904_3 | mgm4477804_3 | mgm4477873_3 | ERR1939166 | SRR3578625 | ERR1939165 | SRR3578645 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TAXID | |||||||||||||||||||||
| 0 | 3528337.0 | 11563613.0 | 10084261.0 | 20054993.0 | 8747525.0 | 12116517.0 | 4191329.0 | 13992760.0 | 14825759.0 | 11083673.0 | ... | 6169203.0 | 8820851.0 | 5713837.0 | 10238500.0 | 5055930.0 | 10380594.0 | 13391896.0 | 1553.0 | 14802198.0 | 736.0 |
| 6 | 0.0 | 78.0 | 0.0 | 127.0 | 0.0 | 79.0 | 0.0 | 0.0 | 0.0 | 172.0 | ... | 68.0 | 247.0 | 211.0 | 156.0 | 147.0 | 383.0 | 1353.0 | 0.0 | 1522.0 | 0.0 |
| 7 | 0.0 | 78.0 | 0.0 | 127.0 | 0.0 | 79.0 | 0.0 | 0.0 | 0.0 | 172.0 | ... | 68.0 | 247.0 | 211.0 | 156.0 | 147.0 | 383.0 | 1353.0 | 0.0 | 1522.0 | 0.0 |
| 9 | 0.0 | 129.0 | 0.0 | 153.0 | 0.0 | 151.0 | 0.0 | 165.0 | 96.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 77.0 | 0.0 | 65.0 | 0.0 |
| 10 | 0.0 | 160.0 | 0.0 | 193.0 | 0.0 | 99.0 | 0.0 | 55.0 | 249.0 | 238.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 263.0 | 0.0 | 466.0 | 0.0 |
5 rows × 432 columns
The labels file contains the mapping of samples names with their actual origin (sources)
[4]:
labels.head()
[4]:
| labels | |
|---|---|
| SRR1175007 | Homo_sapiens |
| SRR042182 | Homo_sapiens |
| SRR061154 | Homo_sapiens |
| SRR061499 | Homo_sapiens |
| SRR063469 | Homo_sapiens |
We will divide the source in training (95%) and testing (5%) dataset
[5]:
cnt_train = cnt.sample(frac=0.95, axis=1)
cnt_test = cnt.drop(cnt_train.columns, axis=1)
We also have to subset the labels file to only the training dataset
[6]:
train_labels = labels.loc[cnt_train.columns,:]
test_labels = labels.loc[cnt_test.columns,:]
Sourcepredict¶
Last but not least, we must export the files to csv to run sourcepredict
[7]:
cnt_train.to_csv("gut_species_sources.csv")
cnt_test.to_csv("gut_species_sinks.csv")
train_labels.to_csv("gut_species_labels.csv")
We’ll now launch sourcepredict with the GMPR normalization method, and the t-SNE embedding, on 6 cores.
[8]:
%%time
!sourcepredict -s gut_species_sources.csv \
-l gut_species_labels.csv \
-n GMPR \
-m TSNE \
-e example_embedding.csv \
-t 6 gut_species_sinks.csv
Step 1: Checking for unknown proportion
== Sample: SRR1175007 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR1175007
known:98.68%
unknown:1.32%
== Sample: SRR061236 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR061236
known:85.01%
unknown:14.99%
== Sample: SRR063471 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR063471
known:98.4%
unknown:1.6%
== Sample: SRR1930132 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR1930132
known:98.48%
unknown:1.52%
== Sample: SRR1930133 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 0.99
----------------------
- Sample: SRR1930133
known:98.49%
unknown:1.51%
== Sample: SRR7658586 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR7658586
known:79.65%
unknown:20.35%
== Sample: SRR7658645 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 0.99
----------------------
- Sample: SRR7658645
known:26.88%
unknown:73.12%
== Sample: SRR7658584 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 0.98
----------------------
- Sample: SRR7658584
known:85.78%
unknown:14.22%
== Sample: SRR7658607 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 0.99
----------------------
- Sample: SRR7658607
known:98.74%
unknown:1.26%
== Sample: SRR7658597 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 0.98
----------------------
- Sample: SRR7658597
known:99.1%
unknown:0.9%
== Sample: SRR5898944 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5898944
known:98.48%
unknown:1.52%
== Sample: ERR1914439 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: ERR1914439
known:98.48%
unknown:1.52%
== Sample: ERR1915140 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: ERR1915140
known:98.48%
unknown:1.52%
== Sample: ERR1914041 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: ERR1914041
known:98.48%
unknown:1.52%
== Sample: ERR1915022 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: ERR1915022
known:98.48%
unknown:1.52%
== Sample: ERR1915826 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: ERR1915826
known:98.48%
unknown:1.52%
== Sample: ERR1913400 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: ERR1913400
known:98.48%
unknown:1.52%
== Sample: ERR1915765 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: ERR1915765
known:98.48%
unknown:1.52%
== Sample: ERR1915225 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: ERR1915225
known:98.48%
unknown:1.52%
== Sample: mgm4477874_3 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mgm4477874_3
known:72.37%
unknown:27.63%
== Sample: ERR1939166 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 0.97
----------------------
- Sample: ERR1939166
known:47.44%
unknown:52.56%
== Sample: ERR1939165 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 0.97
----------------------
- Sample: ERR1939165
known:55.27%
unknown:44.73%
Step 2: Checking for source proportion
Computing weighted_unifrac distance on species rank
TSNE embedding in 2 dimensions
KNN machine learning
Performing 5 fold cross validation on 6 cores...
Trained KNN classifier with 10 neighbors
-> Testing Accuracy: 0.99
----------------------
- Sample: SRR1175007
Canis_familiaris:1.81%
Homo_sapiens:96.72%
Soil:1.47%
- Sample: SRR061236
Canis_familiaris:1.81%
Homo_sapiens:96.72%
Soil:1.47%
- Sample: SRR063471
Canis_familiaris:1.81%
Homo_sapiens:96.72%
Soil:1.47%
- Sample: SRR1930132
Canis_familiaris:1.81%
Homo_sapiens:96.72%
Soil:1.47%
- Sample: SRR1930133
Canis_familiaris:1.81%
Homo_sapiens:96.72%
Soil:1.47%
- Sample: SRR7658586
Canis_familiaris:1.81%
Homo_sapiens:96.72%
Soil:1.47%
- Sample: SRR7658645
Canis_familiaris:1.81%
Homo_sapiens:96.72%
Soil:1.47%
- Sample: SRR7658584
Canis_familiaris:1.81%
Homo_sapiens:96.72%
Soil:1.47%
- Sample: SRR7658607
Canis_familiaris:1.81%
Homo_sapiens:96.72%
Soil:1.47%
- Sample: SRR7658597
Canis_familiaris:1.81%
Homo_sapiens:96.72%
Soil:1.47%
- Sample: SRR5898944
Canis_familiaris:1.81%
Homo_sapiens:96.72%
Soil:1.47%
- Sample: ERR1914439
Canis_familiaris:94.25%
Homo_sapiens:4.28%
Soil:1.47%
- Sample: ERR1915140
Canis_familiaris:94.25%
Homo_sapiens:4.28%
Soil:1.47%
- Sample: ERR1914041
Canis_familiaris:94.25%
Homo_sapiens:4.28%
Soil:1.47%
- Sample: ERR1915022
Canis_familiaris:94.25%
Homo_sapiens:4.28%
Soil:1.47%
- Sample: ERR1915826
Canis_familiaris:94.25%
Homo_sapiens:4.28%
Soil:1.47%
- Sample: ERR1913400
Canis_familiaris:94.25%
Homo_sapiens:4.28%
Soil:1.47%
- Sample: ERR1915765
Canis_familiaris:94.25%
Homo_sapiens:4.28%
Soil:1.47%
- Sample: ERR1915225
Canis_familiaris:94.25%
Homo_sapiens:4.28%
Soil:1.47%
- Sample: mgm4477874_3
Canis_familiaris:2.51%
Homo_sapiens:5.95%
Soil:91.53%
- Sample: ERR1939166
Canis_familiaris:2.51%
Homo_sapiens:5.95%
Soil:91.53%
- Sample: ERR1939165
Canis_familiaris:2.51%
Homo_sapiens:5.95%
Soil:91.53%
Sourcepredict result written to gut_species_sinks.sourcepredict.csv
Embedding coordinates written to example_embedding.csv
CPU times: user 3.64 s, sys: 828 ms, total: 4.47 s
Wall time: 4min 2s
Two files were generated by Sourcepredict: - gut_species_sinks.sourcepredict.csv which contains the proportions of each source
[9]:
sourcepred = pd.read_csv("gut_species_sinks.sourcepredict.csv", index_col=0)
[10]:
sourcepred
[10]:
| SRR1175007 | SRR061236 | SRR063471 | SRR1930132 | SRR1930133 | SRR7658586 | SRR7658645 | SRR7658584 | SRR7658607 | SRR7658597 | ... | ERR1915140 | ERR1914041 | ERR1915022 | ERR1915826 | ERR1913400 | ERR1915765 | ERR1915225 | mgm4477874_3 | ERR1939166 | ERR1939165 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Canis_familiaris | 0.017814 | 0.015347 | 0.017765 | 0.017779 | 0.017781 | 0.014379 | 0.004853 | 0.015486 | 0.017825 | 0.017891 | ... | 0.928216 | 0.928216 | 0.928216 | 0.928216 | 0.928216 | 0.928216 | 0.928216 | 0.018200 | 0.011931 | 0.013898 |
| Homo_sapiens | 0.954443 | 0.822254 | 0.951788 | 0.952581 | 0.952644 | 0.770401 | 0.260037 | 0.829699 | 0.955022 | 0.958548 | ... | 0.042128 | 0.042128 | 0.042128 | 0.042128 | 0.042128 | 0.042128 | 0.042128 | 0.043078 | 0.028241 | 0.032896 |
| Soil | 0.014516 | 0.012506 | 0.014476 | 0.014488 | 0.014489 | 0.011717 | 0.003955 | 0.012619 | 0.014525 | 0.014579 | ... | 0.014504 | 0.014504 | 0.014504 | 0.014504 | 0.014504 | 0.014504 | 0.014504 | 0.662431 | 0.434275 | 0.505859 |
| unknown | 0.013226 | 0.149893 | 0.015971 | 0.015152 | 0.015086 | 0.203503 | 0.731155 | 0.142196 | 0.012627 | 0.008983 | ... | 0.015152 | 0.015152 | 0.015152 | 0.015152 | 0.015152 | 0.015152 | 0.015152 | 0.276292 | 0.525554 | 0.447347 |
4 rows × 22 columns
Let’s check which organism was predicted for each samples, and compare it with the true source
[11]:
comparison = sourcepred.idxmax().to_frame(name="prediction").merge(test_labels, left_index=True, right_index=True)
cm = pdml.ConfusionMatrix(y_true=comparison['labels'],y_pred=comparison['prediction'])
/Users/borry/miniconda3/lib/python3.6/site-packages/pandas/core/indexing.py:1494: FutureWarning:
Passing list-likes to .loc or [] with any missing label will raise
KeyError in the future, you can use .reindex() as an alternative.
See the documentation here:
https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike
return self._getitem_tuple(key)
Let’s look at the confusion matrix
[30]:
cm.to_dataframe()
[30]:
| Predicted | Canis_familiaris | Homo_sapiens | Soil | unknown |
|---|---|---|---|---|
| Actual | ||||
| Canis_familiaris | 8 | 0 | 0 | 0 |
| Homo_sapiens | 0 | 10 | 0 | 1 |
| Soil | 0 | 0 | 2 | 1 |
| unknown | 0 | 0 | 0 | 0 |
Finally, let’s compute the accuracy
[31]:
round(cm.stats()['overall']['Accuracy'],2)
[31]:
0.91
91% of the sink samples were correctly predicted !
- The second file generated by sourcepredict is
example_embedding.csvwhich contains the embedding coordinates of all samples (sources and sinks)
[14]:
embed = pd.read_csv("example_embedding.csv", index_col=0)
embed.head()
[14]:
| PC1 | PC2 | labels | name | |
|---|---|---|---|---|
| SRR1761672 | -12.706208 | -7.860738 | Homo_sapiens | SRR1761672 |
| SRR061456 | -4.520492 | 24.795073 | Homo_sapiens | SRR061456 |
| SRR1761718 | -20.427488 | -6.425568 | Homo_sapiens | SRR1761718 |
| SRR7658589 | -23.176891 | -2.985772 | Homo_sapiens | SRR7658589 |
| ERR1914932 | 28.669333 | 12.863045 | Canis_familiaris | ERR1914932 |
We can plot this embedding, using for example, plotnine, which implements the grammar of graphics in Python
[15]:
from plotnine import *
import warnings
warnings.filterwarnings('ignore')
[16]:
ggplot(data = embed, mapping = aes(x="PC1",y="PC2", color="labels")) + geom_point() + theme_classic()
[16]:
<ggplot: (-9223372029842878797)>
We can see on this plot where the sink samples were embedded
Sourcetracker2¶
“SourceTracker is designed to predict the source of microbial communities in a set of input samples” and is generally consired as the gold standard method to do so. The version 2 is a rewrite of the original Sourcetracker in Python.
We’ll reuse the same training and test files, but we need to reformat them a bit for sourcetracker: - In sourcetracker, the source (training) and sink (file) TAXIDs count table is a single file - The metadata file is slightly different
[17]:
cnt.to_csv("gut_species_taxid.csv", sep="\t", index_label="TAXID")
[18]:
test_labels['SourceSink'] = ['sink']*test_labels.shape[0]
[19]:
train_labels['SourceSink'] = ['source']*train_labels.shape[0]
[20]:
metadata = train_labels.append(test_labels).rename(columns = {"labels":"Env"})[['SourceSink','Env']]
metadata.head()
[20]:
| SourceSink | Env | |
|---|---|---|
| SRR1761672 | source | Homo_sapiens |
| SRR061456 | source | Homo_sapiens |
| SRR1761718 | source | Homo_sapiens |
| SRR7658589 | source | Homo_sapiens |
| ERR1914932 | source | Canis_familiaris |
[21]:
metadata.to_csv("st_gut_species_metadata.csv", sep="\t", index_label='#SampleID')
Finally, we need to convert the TAXIDs count table to biom format
[22]:
!biom convert -i gut_species_taxid.csv -o gut_species_taxid.biom --table-type="Taxon table" --to-json
Soucetracker launch command:
sourcetracker2 gibbs -i gut_species_taxid.biom -m st_gut_species_metadata.csv -o gut_species --jobs 6(Sourcetracker2 was run on a Linux remote server because of issues running it on MacOS)
[32]:
st_pred = pd.read_csv("gut_species/mixing_proportions.txt", sep = "\t", index_col=0)
st_pred.head()
[32]:
| Canis_familiaris | Homo_sapiens | Soil | Unknown | |
|---|---|---|---|---|
| #SampleID | ||||
| SRR1175007 | 0.0170 | 0.9609 | 0.0063 | 0.0158 |
| SRR061236 | 0.0358 | 0.9365 | 0.0074 | 0.0203 |
| SRR063471 | 0.0121 | 0.9724 | 0.0032 | 0.0123 |
| SRR1930132 | 0.1466 | 0.3761 | 0.4477 | 0.0296 |
| SRR1930133 | 0.1182 | 0.5082 | 0.3507 | 0.0229 |
[34]:
st_comparison = st_pred.idxmax(axis=1).to_frame(name="prediction")
st_comparison.head()
[34]:
| prediction | |
|---|---|
| #SampleID | |
| SRR1175007 | Homo_sapiens |
| SRR061236 | Homo_sapiens |
| SRR063471 | Homo_sapiens |
| SRR1930132 | Soil |
| SRR1930133 | Homo_sapiens |
Let’s compare the SourceTracker prediction with the true source
[35]:
comparison2 = st_comparison.merge(test_labels, left_index=True, right_index=True)
comparison2
[35]:
| prediction | labels | SourceSink | |
|---|---|---|---|
| SRR1175007 | Homo_sapiens | Homo_sapiens | sink |
| SRR061236 | Homo_sapiens | Homo_sapiens | sink |
| SRR063471 | Homo_sapiens | Homo_sapiens | sink |
| SRR1930132 | Soil | Homo_sapiens | sink |
| SRR1930133 | Homo_sapiens | Homo_sapiens | sink |
| SRR7658586 | Homo_sapiens | Homo_sapiens | sink |
| SRR7658645 | Homo_sapiens | Homo_sapiens | sink |
| SRR7658584 | Soil | Homo_sapiens | sink |
| SRR7658607 | Homo_sapiens | Homo_sapiens | sink |
| SRR7658597 | Homo_sapiens | Homo_sapiens | sink |
| SRR5898944 | Homo_sapiens | Homo_sapiens | sink |
| ERR1914439 | Canis_familiaris | Canis_familiaris | sink |
| ERR1915140 | Soil | Canis_familiaris | sink |
| ERR1914041 | Canis_familiaris | Canis_familiaris | sink |
| ERR1915022 | Canis_familiaris | Canis_familiaris | sink |
| ERR1915826 | Canis_familiaris | Canis_familiaris | sink |
| ERR1913400 | Canis_familiaris | Canis_familiaris | sink |
| ERR1915765 | Canis_familiaris | Canis_familiaris | sink |
| ERR1915225 | Canis_familiaris | Canis_familiaris | sink |
| mgm4477874_3 | Soil | Soil | sink |
| ERR1939166 | Soil | Soil | sink |
| ERR1939165 | Soil | Soil | sink |
Computing the accuracy
[36]:
cm2 = pdml.ConfusionMatrix(y_true=comparison2["labels"], y_pred=comparison2["prediction"])
cm2.to_dataframe()
[36]:
| Predicted | Canis_familiaris | Homo_sapiens | Soil |
|---|---|---|---|
| Actual | |||
| Canis_familiaris | 7 | 0 | 1 |
| Homo_sapiens | 0 | 9 | 2 |
| Soil | 0 | 0 | 3 |
[38]:
acc2 = round(cm2.stats()['overall']['Accuracy'],2)
[38]:
0.86
Here, Sourcetracker only managed to predict 86% of the sink samples origin correctly
Conclusion¶
On this dataset, we’ve seen that Sourcepredict performs similary or even better than Sourcetracker on predicting accurately the source species
Sourcepredict example2: Estimating source proportions¶
For this example, we’ll reuse the dog, human, and soil dataset.
But unlike example1, here we will mix samples from different sources and estimate the mixing proportions with Sourcepredict and Sourcetracker2
Preparing mixed samples¶
[1]:
import pandas as pd
from plotnine import *
import numpy as np
[2]:
cnt = pd.read_csv("../data/modern_gut_microbiomes_sources.csv", index_col=0)
labels = pd.read_csv("../data/modern_gut_microbiomes_labels.csv",index_col=0)
As in example 1, we’ll first split the dataset into training (95%) and testing(5%)
[3]:
cnt_train = cnt.sample(frac=0.95, axis=1, random_state=42)
cnt_test = cnt.drop(cnt_train.columns, axis=1)
train_labels = labels.loc[cnt_train.columns,:]
test_labels = labels.loc[cnt_test.columns,:]
[4]:
test_labels['labels'].value_counts()
[4]:
Homo_sapiens 13
Canis_familiaris 8
Soil 1
Name: labels, dtype: int64
[5]:
cnt_test.head()
[5]:
| SRR059440 | SRR1930140 | SRR1761708 | SRR1761664 | SRR1761667 | SRR1761674 | SRR7658684 | SRR7658622 | SRR7658689 | SRR7658619 | ... | SRR5898940 | ERR1914224 | ERR1915611 | ERR1915293 | ERR1914207 | ERR1915420 | ERR1916218 | ERR1913675 | ERR1914667 | SRR3578645 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TAXID | |||||||||||||||||||||
| 0 | 19805534.0 | 7267728.0 | 18530434.0 | 2460493.0 | 3324349.0 | 2835521.0 | 5565044.0 | 18783402.0 | 6319253.0 | 18641694.0 | ... | 1658949.0 | 3442424.0 | 1529589.0 | 1765224.0 | 1815426.0 | 1364019.0 | 1558043.0 | 1617964.0 | 1557538.0 | 736.0 |
| 6 | 0.0 | 0.0 | 85.0 | 0.0 | 0.0 | 0.0 | 0.0 | 542.0 | 0.0 | 217.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 7 | 0.0 | 0.0 | 85.0 | 0.0 | 0.0 | 0.0 | 0.0 | 542.0 | 0.0 | 217.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 9 | 239.0 | 88.0 | 115.0 | 55.0 | 189.0 | 91.0 | 72.0 | 100.0 | 152.0 | 209.0 | ... | 0.0 | 51.0 | 69.0 | 60.0 | 56.0 | 106.0 | 51.0 | 69.0 | 62.0 | 0.0 |
| 10 | 163.0 | 177.0 | 112.0 | 76.0 | 164.0 | 109.0 | 220.0 | 84.0 | 180.0 | 175.0 | ... | 0.0 | 202.0 | 80.0 | 67.0 | 73.0 | 0.0 | 80.0 | 77.0 | 110.0 | 0.0 |
5 rows × 22 columns
We then create a function to randomly select a sample from each source (dog as \(s_{dog}\) and human as \(s_{human}\)), and combine such as the new sample \(s_{mixed} = p1*s_{dog} + p1*s_{human}\)
[6]:
def create_mixed_sample(cnt, labels, p1, samp_name, seed):
rand_dog = labels.query('labels == "Canis_familiaris"').sample(1, random_state = seed).index[0]
rand_human = labels.query('labels == "Homo_sapiens"').sample(1, random_state = seed).index[0]
dog_samp = cnt[rand_dog]*p1
human_samp = cnt[rand_human]*(1-p1)
comb = dog_samp + human_samp
comb = comb.rename(samp_name)
meta = pd.DataFrame({'human_sample':[rand_human],'dog_sample':[rand_dog], 'human_prop':[(1-p1)], 'dog_prop':[p1]}, index=[samp_name])
return(comb, meta)
We run this function for a range of mixed proportions (0 to 100%, by 10%), 3 time for each mix
[7]:
mixed_samp = []
mixed_meta = []
nb = 1
for i in range(3):
for p1 in np.arange(0,1.1,0.1):
s = create_mixed_sample(cnt=cnt_test, labels=test_labels, p1=p1, samp_name=f"mixed_sample_{nb}", seed = int(100*p1))
mixed_samp.append(s[0])
mixed_meta.append(s[1])
nb += 1
[8]:
mixed_samples = pd.concat(mixed_samp, axis=1, keys=[s.name for s in mixed_samp]).astype(int)
mixed_samples.head()
[8]:
| mixed_sample_1 | mixed_sample_2 | mixed_sample_3 | mixed_sample_4 | mixed_sample_5 | mixed_sample_6 | mixed_sample_7 | mixed_sample_8 | mixed_sample_9 | mixed_sample_10 | ... | mixed_sample_24 | mixed_sample_25 | mixed_sample_26 | mixed_sample_27 | mixed_sample_28 | mixed_sample_29 | mixed_sample_30 | mixed_sample_31 | mixed_sample_32 | mixed_sample_33 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TAXID | |||||||||||||||||||||
| 0 | 5565044 | 2390966 | 15338330 | 14408501 | 7983985 | 5100662 | 8740624 | 3251030 | 3418809 | 2103402 | ... | 2390966 | 15338330 | 14408501 | 7983985 | 5100662 | 8740624 | 3251030 | 3418809 | 2103402 | 1529589 |
| 6 | 0 | 0 | 433 | 0 | 66 | 26 | 0 | 0 | 0 | 0 | ... | 0 | 433 | 0 | 66 | 26 | 0 | 0 | 0 | 0 | 0 |
| 7 | 0 | 0 | 433 | 0 | 66 | 26 | 0 | 0 | 0 | 0 | ... | 0 | 433 | 0 | 66 | 26 | 0 | 0 | 0 | 0 | 0 |
| 9 | 72 | 55 | 90 | 184 | 98 | 141 | 159 | 74 | 78 | 70 | ... | 55 | 90 | 184 | 98 | 141 | 159 | 74 | 78 | 70 | 69 |
| 10 | 220 | 75 | 83 | 136 | 276 | 117 | 65 | 109 | 194 | 89 | ... | 75 | 83 | 136 | 276 | 117 | 65 | 109 | 194 | 89 | 80 |
5 rows × 33 columns
[9]:
mixed_metadata = pd.concat(mixed_meta)
mixed_metadata
[9]:
| human_sample | dog_sample | human_prop | dog_prop | |
|---|---|---|---|---|
| mixed_sample_1 | SRR7658684 | ERR1913675 | 1.0 | 0.0 |
| mixed_sample_2 | SRR1761664 | ERR1915293 | 0.9 | 0.1 |
| mixed_sample_3 | SRR7658622 | ERR1916218 | 0.8 | 0.2 |
| mixed_sample_4 | SRR059440 | ERR1914207 | 0.7 | 0.3 |
| mixed_sample_5 | SRR7658624 | ERR1914667 | 0.6 | 0.4 |
| mixed_sample_6 | SRR7658608 | ERR1915420 | 0.5 | 0.5 |
| mixed_sample_7 | SRR059440 | ERR1915420 | 0.4 | 0.6 |
| mixed_sample_8 | SRR1930140 | ERR1915611 | 0.3 | 0.7 |
| mixed_sample_9 | SRR1761667 | ERR1914224 | 0.2 | 0.8 |
| mixed_sample_10 | SRR1930140 | ERR1915611 | 0.1 | 0.9 |
| mixed_sample_11 | SRR7658624 | ERR1915611 | 0.0 | 1.0 |
| mixed_sample_12 | SRR7658684 | ERR1913675 | 1.0 | 0.0 |
| mixed_sample_13 | SRR1761664 | ERR1915293 | 0.9 | 0.1 |
| mixed_sample_14 | SRR7658622 | ERR1916218 | 0.8 | 0.2 |
| mixed_sample_15 | SRR059440 | ERR1914207 | 0.7 | 0.3 |
| mixed_sample_16 | SRR7658624 | ERR1914667 | 0.6 | 0.4 |
| mixed_sample_17 | SRR7658608 | ERR1915420 | 0.5 | 0.5 |
| mixed_sample_18 | SRR059440 | ERR1915420 | 0.4 | 0.6 |
| mixed_sample_19 | SRR1930140 | ERR1915611 | 0.3 | 0.7 |
| mixed_sample_20 | SRR1761667 | ERR1914224 | 0.2 | 0.8 |
| mixed_sample_21 | SRR1930140 | ERR1915611 | 0.1 | 0.9 |
| mixed_sample_22 | SRR7658624 | ERR1915611 | 0.0 | 1.0 |
| mixed_sample_23 | SRR7658684 | ERR1913675 | 1.0 | 0.0 |
| mixed_sample_24 | SRR1761664 | ERR1915293 | 0.9 | 0.1 |
| mixed_sample_25 | SRR7658622 | ERR1916218 | 0.8 | 0.2 |
| mixed_sample_26 | SRR059440 | ERR1914207 | 0.7 | 0.3 |
| mixed_sample_27 | SRR7658624 | ERR1914667 | 0.6 | 0.4 |
| mixed_sample_28 | SRR7658608 | ERR1915420 | 0.5 | 0.5 |
| mixed_sample_29 | SRR059440 | ERR1915420 | 0.4 | 0.6 |
| mixed_sample_30 | SRR1930140 | ERR1915611 | 0.3 | 0.7 |
| mixed_sample_31 | SRR1761667 | ERR1914224 | 0.2 | 0.8 |
| mixed_sample_32 | SRR1930140 | ERR1915611 | 0.1 | 0.9 |
| mixed_sample_33 | SRR7658624 | ERR1915611 | 0.0 | 1.0 |
Now we can export the new “test” (sink) table to csv for sourcepredict
[10]:
mixed_samples.to_csv('mixed_samples_cnt.csv')
As well as the source count and labels table for the sources
[11]:
train_labels.to_csv('train_labels.csv')
cnt_train.to_csv('sources_cnt.csv')
Sourcepredict¶
With KNN machine learning¶
For running Sourcepredict, we’ll change two parameters from their default values: - -me The default method used by Sourcepredict is T-SNE which a non-linear type of embedding, i.e. the distance between points doesn’t reflext their actual distance in the original dimensions, to achieve a better clustering, which is good for source prediction. Because here we’re more interested in source proportion estimation, rather than source prediction, we’ll choose a Multi Dimensional Scaling (MDS) which
is a type of linear embedding, where the distance between points in the lower dimension match more the distances in the embedding in lower dimension, which is better for source proportion estimation. - -kne which is the number of neighbors in KNN algorithm: we use all neighbors to reflect a more global contribution of samples to the proportion estimation, instead of only the immediate neighbors. This will affect negatively the source prediction, but give better source proportion estimations
[12]:
%%time
!python ../sourcepredict -s sources_cnt.csv \
-l train_labels.csv \
-kne all\
-me mds \
-e mixed_embedding.csv \
-t 6 \
mixed_samples_cnt.csv
/Users/borry/miniconda3/envs/sourcepredict/lib/python3.7/site-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.
warnings.warn(msg, category=DeprecationWarning)
Step 1: Checking for unknown proportion
== Sample: mixed_sample_1 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_1
known:98.48%
unknown:1.52%
== Sample: mixed_sample_2 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_2
known:98.48%
unknown:1.52%
== Sample: mixed_sample_3 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 0.98
----------------------
- Sample: mixed_sample_3
known:98.49%
unknown:1.51%
== Sample: mixed_sample_4 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_4
known:98.49%
unknown:1.51%
== Sample: mixed_sample_5 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_5
known:98.48%
unknown:1.52%
== Sample: mixed_sample_6 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_6
known:98.48%
unknown:1.52%
== Sample: mixed_sample_7 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_7
known:98.48%
unknown:1.52%
== Sample: mixed_sample_8 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_8
known:98.48%
unknown:1.52%
== Sample: mixed_sample_9 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_9
known:98.48%
unknown:1.52%
== Sample: mixed_sample_10 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_10
known:98.48%
unknown:1.52%
== Sample: mixed_sample_11 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_11
known:98.48%
unknown:1.52%
== Sample: mixed_sample_12 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_12
known:98.48%
unknown:1.52%
== Sample: mixed_sample_13 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_13
known:98.48%
unknown:1.52%
== Sample: mixed_sample_14 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 0.98
----------------------
- Sample: mixed_sample_14
known:98.49%
unknown:1.51%
== Sample: mixed_sample_15 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_15
known:98.49%
unknown:1.51%
== Sample: mixed_sample_16 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_16
known:98.48%
unknown:1.52%
== Sample: mixed_sample_17 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_17
known:98.48%
unknown:1.52%
== Sample: mixed_sample_18 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_18
known:98.48%
unknown:1.52%
== Sample: mixed_sample_19 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_19
known:98.48%
unknown:1.52%
== Sample: mixed_sample_20 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_20
known:98.48%
unknown:1.52%
== Sample: mixed_sample_21 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_21
known:98.48%
unknown:1.52%
== Sample: mixed_sample_22 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_22
known:98.48%
unknown:1.52%
== Sample: mixed_sample_23 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_23
known:98.48%
unknown:1.52%
== Sample: mixed_sample_24 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_24
known:98.48%
unknown:1.52%
== Sample: mixed_sample_25 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 0.98
----------------------
- Sample: mixed_sample_25
known:98.49%
unknown:1.51%
== Sample: mixed_sample_26 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_26
known:98.49%
unknown:1.51%
== Sample: mixed_sample_27 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_27
known:98.48%
unknown:1.52%
== Sample: mixed_sample_28 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_28
known:98.48%
unknown:1.52%
== Sample: mixed_sample_29 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_29
known:98.48%
unknown:1.52%
== Sample: mixed_sample_30 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_30
known:98.48%
unknown:1.52%
== Sample: mixed_sample_31 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_31
known:98.48%
unknown:1.52%
== Sample: mixed_sample_32 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_32
known:98.48%
unknown:1.52%
== Sample: mixed_sample_33 ==
Adding unknown
Normalizing (GMPR)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: mixed_sample_33
known:98.48%
unknown:1.52%
Step 2: Checking for source proportion
Computing weighted_unifrac distance on species rank
MDS embedding in 2 dimensions
KNN machine learning
Trained KNN classifier with 262 neighbors
-> Testing Accuracy: 0.91
----------------------
- Sample: mixed_sample_1
Canis_familiaris:26.67%
Homo_sapiens:72.03%
Soil:1.3%
- Sample: mixed_sample_2
Canis_familiaris:22.23%
Homo_sapiens:76.47%
Soil:1.3%
- Sample: mixed_sample_3
Canis_familiaris:19.26%
Homo_sapiens:78.12%
Soil:2.62%
- Sample: mixed_sample_4
Canis_familiaris:25.91%
Homo_sapiens:72.03%
Soil:2.06%
- Sample: mixed_sample_5
Canis_familiaris:23.01%
Homo_sapiens:73.37%
Soil:3.62%
- Sample: mixed_sample_6
Canis_familiaris:22.79%
Homo_sapiens:75.82%
Soil:1.39%
- Sample: mixed_sample_7
Canis_familiaris:25.65%
Homo_sapiens:72.46%
Soil:1.89%
- Sample: mixed_sample_8
Canis_familiaris:48.84%
Homo_sapiens:49.91%
Soil:1.24%
- Sample: mixed_sample_9
Canis_familiaris:33.12%
Homo_sapiens:65.14%
Soil:1.75%
- Sample: mixed_sample_10
Canis_familiaris:62.75%
Homo_sapiens:36.18%
Soil:1.06%
- Sample: mixed_sample_11
Canis_familiaris:80.93%
Homo_sapiens:18.45%
Soil:0.61%
- Sample: mixed_sample_12
Canis_familiaris:26.67%
Homo_sapiens:72.03%
Soil:1.3%
- Sample: mixed_sample_13
Canis_familiaris:22.23%
Homo_sapiens:76.47%
Soil:1.3%
- Sample: mixed_sample_14
Canis_familiaris:19.26%
Homo_sapiens:78.12%
Soil:2.62%
- Sample: mixed_sample_15
Canis_familiaris:25.91%
Homo_sapiens:72.03%
Soil:2.06%
- Sample: mixed_sample_16
Canis_familiaris:23.01%
Homo_sapiens:73.37%
Soil:3.62%
- Sample: mixed_sample_17
Canis_familiaris:22.79%
Homo_sapiens:75.82%
Soil:1.39%
- Sample: mixed_sample_18
Canis_familiaris:25.65%
Homo_sapiens:72.46%
Soil:1.89%
- Sample: mixed_sample_19
Canis_familiaris:48.84%
Homo_sapiens:49.91%
Soil:1.24%
- Sample: mixed_sample_20
Canis_familiaris:33.12%
Homo_sapiens:65.14%
Soil:1.75%
- Sample: mixed_sample_21
Canis_familiaris:62.75%
Homo_sapiens:36.18%
Soil:1.06%
- Sample: mixed_sample_22
Canis_familiaris:80.93%
Homo_sapiens:18.45%
Soil:0.61%
- Sample: mixed_sample_23
Canis_familiaris:26.67%
Homo_sapiens:72.03%
Soil:1.3%
- Sample: mixed_sample_24
Canis_familiaris:22.23%
Homo_sapiens:76.47%
Soil:1.3%
- Sample: mixed_sample_25
Canis_familiaris:19.26%
Homo_sapiens:78.12%
Soil:2.62%
- Sample: mixed_sample_26
Canis_familiaris:25.91%
Homo_sapiens:72.03%
Soil:2.06%
- Sample: mixed_sample_27
Canis_familiaris:23.01%
Homo_sapiens:73.37%
Soil:3.62%
- Sample: mixed_sample_28
Canis_familiaris:22.79%
Homo_sapiens:75.82%
Soil:1.39%
- Sample: mixed_sample_29
Canis_familiaris:25.65%
Homo_sapiens:72.46%
Soil:1.89%
- Sample: mixed_sample_30
Canis_familiaris:48.84%
Homo_sapiens:49.91%
Soil:1.24%
- Sample: mixed_sample_31
Canis_familiaris:33.12%
Homo_sapiens:65.14%
Soil:1.75%
- Sample: mixed_sample_32
Canis_familiaris:62.75%
Homo_sapiens:36.18%
Soil:1.06%
- Sample: mixed_sample_33
Canis_familiaris:80.93%
Homo_sapiens:18.45%
Soil:0.61%
Sourcepredict result written to mixed_samples_cnt.sourcepredict.csv
Embedding coordinates written to mixed_embedding.csv
CPU times: user 4.27 s, sys: 1.14 s, total: 5.41 s
Wall time: 5min 46s
Reading Sourcepredict KNN results
[13]:
sp_ebd = pd.read_csv("mixed_embedding.csv", index_col=0)
[14]:
sp_ebd.head()
[14]:
| PC1 | PC2 | labels | name | |
|---|---|---|---|---|
| mgm4477874_3 | 8.822395 | -4.957090 | Soil | mgm4477874_3 |
| SRR1761709 | 3.896029 | 6.258361 | Homo_sapiens | SRR1761709 |
| SRR7658685 | -1.151347 | 5.457706 | Homo_sapiens | SRR7658685 |
| SRR059395 | -0.889409 | -7.682652 | Homo_sapiens | SRR059395 |
| ERR1915122 | -3.533856 | -2.673234 | Canis_familiaris | ERR1915122 |
[15]:
import warnings
warnings.filterwarnings('ignore')
[16]:
ggplot(data = sp_ebd, mapping = aes(x='PC1',y='PC2')) + geom_point(aes(color='labels')) + theme_classic()
[16]:
<ggplot: (297174606)>
[17]:
sp_pred = pd.read_csv("mixed_samples_cnt.sourcepredict.csv", index_col=0)
[18]:
sp_pred.T.head()
[18]:
| Canis_familiaris | Homo_sapiens | Soil | unknown | |
|---|---|---|---|---|
| mixed_sample_1 | 0.262665 | 0.709352 | 0.012832 | 0.015152 |
| mixed_sample_2 | 0.218900 | 0.753113 | 0.012836 | 0.015152 |
| mixed_sample_3 | 0.189678 | 0.769471 | 0.025776 | 0.015075 |
| mixed_sample_4 | 0.255186 | 0.709368 | 0.020298 | 0.015148 |
| mixed_sample_5 | 0.226581 | 0.722568 | 0.035699 | 0.015152 |
[19]:
mixed_metadata.head()
[19]:
| human_sample | dog_sample | human_prop | dog_prop | |
|---|---|---|---|---|
| mixed_sample_1 | SRR7658684 | ERR1913675 | 1.0 | 0.0 |
| mixed_sample_2 | SRR1761664 | ERR1915293 | 0.9 | 0.1 |
| mixed_sample_3 | SRR7658622 | ERR1916218 | 0.8 | 0.2 |
| mixed_sample_4 | SRR059440 | ERR1914207 | 0.7 | 0.3 |
| mixed_sample_5 | SRR7658624 | ERR1914667 | 0.6 | 0.4 |
[20]:
sp_res = sp_pred.T.merge(mixed_metadata, left_index=True, right_index=True)
[21]:
from sklearn.metrics import r2_score, mean_squared_error
[22]:
mse_sp = round(mean_squared_error(y_pred=sp_res['Homo_sapiens'], y_true=sp_res['human_prop']),2)
r2_sp = round(r2_score(y_pred=sp_res['Homo_sapiens'], y_true=sp_res['human_prop']),2)
[23]:
p = ggplot(data = sp_res, mapping=aes(x='human_prop',y='Homo_sapiens')) + geom_point()
p += labs(title = f"Homo sapiens proportions predicted by Soucepredict - $MSE = {mse_sp}$ - $R^2 = {r2_sp}$", x='actual', y='predicted')
p += theme_classic()
p += coord_cartesian(xlim=[0,1], ylim=[0,1])
p += geom_abline(intercept=0, slope=1, color = "red", alpha=0.2, linetype = 'dashed')
p
[23]:
<ggplot: (-9223372036557649955)>
On this plot, the dotted red line represents what a perfect proportion estimation would give
[24]:
sp_res_hist = (sp_res['human_prop'].append(sp_res['Homo_sapiens']).to_frame(name='Homo_sapiens_prop'))
sp_res_hist['source'] = (['actual']*sp_res.shape[0]+['predicted']*sp_res.shape[0])
[25]:
p = ggplot(data = sp_res_hist, mapping=aes(x='Homo_sapiens_prop')) + geom_density(aes(fill='source'), alpha=0.3)
p += labs(title = 'Distribution of Homo sapiens predicted proportions by Sourcepredict')
p += scale_fill_discrete(name="Homo sapiens proportion")
p += theme_classic()
p
[25]:
<ggplot: (-9223372036557649934)>
This plot shows the actual and predicted by Sourcepredict distribution of Human proportions. What we are interested in is the overlap between the two colors: the higher it is, the more the estimated Human proportion is accurate.
Sourcetracker2¶
Preparing count table
[26]:
cnt_train.merge(mixed_samples, right_index=True, left_index=True).to_csv("st_mixed_count.csv" , sep="\t", index_label="TAXID")
[27]:
!biom convert -i st_mixed_count.csv -o st_mixed_count.biom --table-type="Taxon table" --to-json
Preparing metadata
[28]:
train_labels['SourceSink'] = ['source']*train_labels.shape[0]
[29]:
mixed_metadata['labels'] = ['-']*mixed_metadata.shape[0]
mixed_metadata['SourceSink'] = ['sink']*mixed_metadata.shape[0]
[30]:
st_labels = train_labels.append(mixed_metadata[['labels', 'SourceSink']])
[31]:
st_labels = st_labels.rename(columns={'labels':'Env'})[['SourceSink','Env']]
[32]:
st_labels.to_csv("st_mixed_labels.csv", sep="\t", index_label='#SampleID')
Running Sourcetracker2
sourcetracker2 gibbs -i st_mixed_count.biom -m st_mixed_labels.csv -o mixed_prop --jobs 6(Sourcetracker2 was run on a Linux remote server because of issues running it on MacOS)
Sourcetracker2 results
[33]:
st_pred = pd.read_csv("_assets/mixed_prop/mixing_proportions.txt", sep="\t", index_col=0)
[34]:
st_res = st_pred.merge(mixed_metadata, left_index=True, right_index=True)
[35]:
mse_st = round(mean_squared_error(y_pred=st_res['Homo_sapiens'], y_true=st_res['human_prop']),2)
r2_st = round(r2_score(y_pred=st_res['Homo_sapiens'], y_true=st_res['human_prop']),2)
[36]:
p = ggplot(data = st_res, mapping=aes(x='human_prop',y='Homo_sapiens')) + geom_point()
p += labs(title = f"Homo sapiens proportions predicted by Soucepretracker2 - $MSE = {mse_st}$ - $R^2 = {r2_st}$", x='actual', y='predicted')
p += theme_classic()
p += coord_cartesian(xlim=[0,1], ylim=[0,1])
p += geom_abline(intercept=0, slope=1, color = "red", alpha=0.2, linetype = 'dashed')
p
[36]:
<ggplot: (297644629)>
On this plot, the dotted red line represents what a perfect proportion estimation would give.
[37]:
st_res_hist = (st_res['human_prop'].append(st_res['Homo_sapiens']).to_frame(name='Homo_sapiens_prop'))
st_res_hist['source'] = (['actual']*st_res.shape[0]+['predicted']*st_res.shape[0])
[38]:
p = ggplot(data = st_res_hist, mapping=aes(x='Homo_sapiens_prop')) + geom_density(aes(fill='source'), alpha=0.4)
p += labs(title = 'Distribution of Homo sapiens predicted proportions by Sourcetracker2')
p += scale_fill_discrete(name="Homo sapiens proportion")
p += theme_classic()
p
[38]:
<ggplot: (297182405)>
This plot shows the actual and predicted by Sourcepredict distribution of Human proportions. What we are interested in is the overlap between the two colors: the higher it is, the more the estimated Human proportion is accurate.
Conclusion¶
For source proportion estimation in samples of mixed sources, Sourcepredict, especially when using it with
-kne all neighbors, performs similarly, or slightly better than Sourcetracker2.However, Sourcepredict wasn’t designed for source prediction in mind, as opposed to source proportion estimation. Therefore, for source proportion estimation, we still recommend using Sourcetracker2, even if Sourcepredict can perform similarly.
Sourcepredict example3: Segregating patients with or without Clostridium difficile infection (CDI) on the basis of 16s microbiome¶
Source Article: Domestic canines do not display evidence of gut microbial dysbiosis in the presence of Clostridioides (Clostridium) difficile, despite cellular susceptibility to its toxins 10.1016/j.anaerobe.2019.03.017
Healthy human dataset: PRJNA386260
CDI human dataset: PRJNA307992
[1]:
import pandas as pd
import numpy as np
from plotnine import *
from ete3 import NCBITaxa
import multiprocessing
from functools import partial
import seaborn as sns
[2]:
ncbi = NCBITaxa()
Downloading data¶
[3]:
cdi_color = "#E7CE1A"
healthy_color = "grey"
[4]:
tax_level = ['genus','species']
[5]:
import multiprocessing
import subprocess
def dl(file, outdir):
cmd = f"wget {file} -P {outdir}"
print(cmd)
try:
subprocess.check_output(cmd, shell=True)
except subprocess.CalledProcessError:
print(f"Error downloading {file}")
def dl_multi(allfiles, outdir, process):
dl_fun = partial(dl, outdir=outdir)
with multiprocessing.Pool(process) as p:
p.map(dl_fun, allfiles)
[6]:
healthy_meta = pd.read_csv("healthy/PRJNA386260_metadata.txt", sep="\t", index_col='run_accession')
CDI_meta = pd.read_csv("CDI/PRJNA307992_metadata.txt", sep="\t", index_col='run_accession')
[7]:
healthy_meta['labels'] = ['healthy']*healthy_meta.shape[0]
CDI_meta['labels'] = ['CDI']*CDI_meta.shape[0]
[8]:
healthy_fastqs = list(healthy_meta['fastq_ftp'].str.split(";", expand=True)[0]) + list(healthy_meta['fastq_ftp'].str.split(";", expand=True)[1])
[9]:
CDI_fastqs = list(CDI_meta['fastq_ftp'].str.split(";", expand=True)[0]) + list(CDI_meta['fastq_ftp'].str.split(";", expand=True)[1])
Uncomment to download files
[10]:
#dl_multi(allfiles=healthy_fastqs, outdir="./healthy/", process=4)
#dl_multi(allfiles=CDI_fastqs, outdir="./CDI/", process=4)
Utility functions¶
Removing outlier samples (less than 10 species) and species present in less than 10 samples
[11]:
def remove_outlier(df, n=10):
return(df.loc[df.nunique(axis=1) > n, df.nunique(axis=0) > n])
Removing TAXID not in NCBI taxonomy
[12]:
def remove_not_taxo(df):
"""
df(pandas DataFrame) with TAXID in index, and samples in columns
"""
valid_ranks = {k:v for (k,v) in zip(ncbi.get_rank(df.index).keys(), ncbi.get_rank(df.index).values()) if v != 'no rank'}
return(df.loc[valid_ranks.keys(),:])
Normalization methods
[13]:
def gmpr_size_factor(col, ar):
"""Generate GMPR size factor
Args:
col (int): columm index of the numpy array
ar (numpy array): numpy array of TAXID counts,
colums as Samples, Rows as TAXIDs
Returns:
float: GMPR size factor per column
"""
pr = np.apply_along_axis(lambda x: np.divide(ar[:, col], x), 0, ar)
pr[np.isinf(pr)] = np.nan
pr[pr == 0] = np.nan
pr_median = np.nanmedian(pr, axis=0)
return(np.exp(np.mean(np.log(pr_median))))
def GMPR_normalize(df, process=4):
"""Compute GMPR normalization
Global Mean of Pairwise Ratios
Chen, L., Reeve, J., Zhang, L., Huang, S., Wang, X., & Chen, J. (2018).
GMPR: A robust normalization method for zero-inflated count data
with application to microbiome sequencing data.
PeerJ, 6, e4600.
Args:
df (pandas Dataframe): TAXID count dataframe,
colums as Samples, Rows as TAXIDs
process (int): number of process for parallelization
"""
ar = np.asarray(df)
gmpr_sf_partial = partial(gmpr_size_factor, ar=ar)
with multiprocessing.Pool(process) as p:
sf = p.map(gmpr_sf_partial, list(range(np.shape(ar)[1])))
return(pd.DataFrame(np.divide(ar, sf), index=df.index, columns=df.columns))
[14]:
def RLE_normalize(pd_dataframe):
"""Normalize with Relative Log Expression
Args:
pd_dataframe (pandas DataFrame): TAXID count dataframe,
colums as Samples, Rows as TAXIDs
Returns:
pandas DataFrame: RLE Normalized datafrane. Colums as Samples, Rows as TAXIDs
Example:
>>> RLE_normalize(pd.DataFrame)
"""
step1 = pd_dataframe.apply(np.log, 0)
step2 = step1.apply(np.average, 1)
step3 = step2[step2.replace([np.inf, -np.inf], np.nan).notnull()]
step4_1 = step1[step1.replace(
[np.inf, -np.inf], np.nan).notnull().all(axis=1)]
step4 = step4_1.subtract(step3, 0)
step5 = step4.apply(np.median, 0)
step6 = step5.apply(np.exp)
step7 = pd_dataframe.divide(step6, 1).apply(round, 1)
return(step7)
[15]:
def subsample_normalize_pd(pd_dataframe):
"""Normalize with Subsampling
Args:
pd_dataframe (pandas DataFrame): TAXID count dataframe,
colums as Samples, Rows as TAXIDs
Returns:
pandas DataFrame: Subsample Normalized dataframe. Colums as Samples, Rows as TAXIDs
"""
def subsample_normalize(serie, omax):
"""Subsample normalization column wise
imin: minimum of input range
imax: maximum of input range
omin: minimum of output range
omax: maximum of output range
x in [imin, imax]
f(x) in [omin, omax]
x - imin
f(x) = ------------ x(omax - omin) + omin
imax - imin
Args:
serie (pandas Series): Indivudal Sample Column
omax (int): maximum of output range
Returns:
pandas Series: normalized pandas Series
"""
imin = min(serie)
imax = max(serie)
omin = 0
if imax > 0:
newserie = serie.apply(lambda x: (
(x - imin)/(imax - imin)*(omax-omin)+omin))
else:
newserie = serie
return(newserie)
step1 = pd_dataframe.apply(max, 1)
themax = max(step1)
step2 = pd_dataframe.apply(
subsample_normalize, axis=0, args=(themax,))
step3 = step2.apply(np.floor, axis=1)
return(step3)
PLS-DA with sklearn
[16]:
class plsda:
def __init__(self, X,Y, labels):
"""
X(pd DataFrame) normalized feature matrix with samples in index, and features in columns
Y(np 1D array) binary response variable encoding the grouping for each sample
labels(named pd Series) of group label for each sample
"""
from sklearn.cross_decomposition import PLSRegression
self.plsr = PLSRegression(n_components=2)
self.plsr.fit(X, Y)
self.scores = pd.DataFrame(self.plsr.x_scores_, index=X.index, columns=['DIM1','DIM2'])
self.scores = self.scores.join(labels['labels'])
self.weights = pd.DataFrame(self.plsr.x_weights_, index=X.columns, columns=['DIM1','DIM2']).sort_values('DIM1', ascending=False)
self.weights['name'] = ncbi.get_taxid_translator(self.weights.index).values()
self.top_weights = self.weights.head(20).append(self.weights.tail(20))
self.top_weights['name'] = pd.Categorical(self.top_weights['name'], categories=self.top_weights['name'])
mds with sklearn
[17]:
class mds:
def __init__(self, X, labels, metric='braycurtis'):
"""
X(pd DataFrame) normalized feature matrix with samples in index, and features in columns
labels(named pd Series) of group label for each sample
"""
from sklearn.metrics import pairwise_distances
from sklearn.manifold import MDS
dist = pairwise_distances(X, metric=metric)
self.mds = MDS(n_components=2, dissimilarity='precomputed')
self.mds.fit(X=dist)
self.embedding = pd.DataFrame(self.mds.embedding_, columns=['DIM1','DIM2'], index=X.index)
self.embedding = self.embedding.join(labels)
Reading the results of the dada2-nf pipeline¶
1- Species level¶
[18]:
healthy_otu_s = pd.read_csv("/projects1/users/borry/30_dada2-nf/results_healthy/merged/dada2_otu_table.csv", index_col=0)
CDI_otu_s = pd.read_csv("/projects1/users/borry/30_dada2-nf/results_CDI/merged/dada2_otu_table.csv", index_col=0)
[19]:
healthy_otu_s = healthy_otu_s.drop([0], axis=0)
CDI_otu_s = CDI_otu_s.drop([0], axis=0)
[20]:
all_otu_s = healthy_otu_s.merge(CDI_otu_s, left_index=True, right_index=True)
all_otu_s = remove_outlier(all_otu_s)
all_otu_s.shape
[20]:
(103, 305)
[21]:
all_otu_s.head()
[21]:
| SRR5578998 | SRR5579099 | SRR5579045 | SRR5578981 | SRR5579095 | SRR5579054 | SRR5578909 | SRR5578907 | SRR5578965 | SRR5579021 | ... | SRR3102417 | SRR3102498 | SRR3102362 | SRR3102525 | SRR3102486 | SRR3102556 | SRR3102547 | SRR3102572 | SRR3102515 | SRR3102517 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 199 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 820 | 745.0 | 725.0 | 973.0 | 708.0 | 710.0 | 507.0 | 483.0 | 223.0 | 43.0 | 313.0 | ... | 0.0 | 6319.0 | 1663.0 | 0.0 | 0.0 | 0.0 | 0.0 | 350.0 | 2235.0 | 31.0 |
| 821 | 1103.0 | 742.0 | 2126.0 | 0.0 | 1502.0 | 1193.0 | 1316.0 | 52.0 | 462.0 | 947.0 | ... | 0.0 | 8.0 | 4899.0 | 0.0 | 0.0 | 0.0 | 7318.0 | 833.0 | 0.0 | 0.0 |
| 824 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 851 | 0.0 | 0.0 | 0.0 | 4.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 868.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2190.0 | 0.0 | 0.0 | 0.0 |
5 rows × 305 columns
2- Genus Level¶
[22]:
healthy_otu_g = pd.read_csv("/projects1/users/borry/30_dada2-nf/results_healthy_genus/merged/dada2_otu_table.csv", index_col=0)
cdi_otu_g = pd.read_csv("/projects1/users/borry/30_dada2-nf/results_CDI_genus/merged/dada2_otu_table.csv", index_col=0)
[23]:
healthy_otu_g = healthy_otu_g.drop([0], axis=0)
cdi_otu_g = cdi_otu_g.drop([0], axis=0)
[24]:
all_otu_g = healthy_otu_g.merge(cdi_otu_g, left_index=True, right_index=True)
all_otu_g = remove_outlier(all_otu_g)
print(all_otu_g.shape)
all_otu_g.head()
(114, 405)
[24]:
| SRR5578998 | SRR5579099 | SRR5579045 | SRR5578981 | SRR5579095 | SRR5579054 | SRR5578909 | SRR5578907 | SRR5578965 | SRR5579115 | ... | SRR3102507 | SRR3102439 | SRR3102474 | SRR3102487 | SRR3102547 | SRR3102572 | SRR3102381 | SRR3102515 | SRR3102405 | SRR3102517 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 194 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 2.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 286 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 17.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 469 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 17.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 9.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 482 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.0 | 0.0 | 0.0 | 0.0 |
| 544 | 0.0 | 0.0 | 1203.0 | 0.0 | 0.0 | 0.0 | 0.0 | 182.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
5 rows × 405 columns
Normalizing dataframes¶
[25]:
X_s = subsample_normalize_pd(all_otu_s).T
[26]:
X_g = GMPR_normalize(all_otu_g, 4).dropna(axis=1).T
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/ipykernel_launcher.py:10: RuntimeWarning: divide by zero encountered in true_divide
# Remove the CWD from sys.path while we load stuff.
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/ipykernel_launcher.py:10: RuntimeWarning: invalid value encountered in true_divide
# Remove the CWD from sys.path while we load stuff.
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/ipykernel_launcher.py:10: RuntimeWarning: invalid value encountered in true_divide
# Remove the CWD from sys.path while we load stuff.
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/ipykernel_launcher.py:10: RuntimeWarning: divide by zero encountered in true_divide
# Remove the CWD from sys.path while we load stuff.
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/ipykernel_launcher.py:10: RuntimeWarning: divide by zero encountered in true_divide
# Remove the CWD from sys.path while we load stuff.
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/ipykernel_launcher.py:10: RuntimeWarning: invalid value encountered in true_divide
# Remove the CWD from sys.path while we load stuff.
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/ipykernel_launcher.py:10: RuntimeWarning: divide by zero encountered in true_divide
# Remove the CWD from sys.path while we load stuff.
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/ipykernel_launcher.py:10: RuntimeWarning: invalid value encountered in true_divide
# Remove the CWD from sys.path while we load stuff.
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered
overwrite_input=overwrite_input)
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered
overwrite_input=overwrite_input)
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered
overwrite_input=overwrite_input)
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered
overwrite_input=overwrite_input)
Creating labels dataframe and response variable array¶
[27]:
labels = healthy_meta['labels'].to_frame().append(CDI_meta['labels'].to_frame())
[28]:
labels_s = labels.loc[X_s.index, :]
Y_s = np.where(labels_s['labels']=='healthy', '1','0')
[29]:
labels_g = labels.loc[X_g.index, :]
Y_g = np.where(labels_g['labels']=='healthy', '1','0')
Exploring the dataset¶
[30]:
import seaborn as sns
[31]:
plsda_s = plsda(X_s, Y_s, labels_s)
[32]:
plsda_s.scores
[32]:
| DIM1 | DIM2 | labels | |
|---|---|---|---|
| SRR5578998 | 0.935718 | 0.126756 | healthy |
| SRR5579099 | 2.614041 | 0.075360 | healthy |
| SRR5579045 | -0.424088 | 0.029255 | healthy |
| SRR5578981 | 1.068008 | 0.688370 | healthy |
| SRR5579095 | -0.458098 | 0.082241 | healthy |
| ... | ... | ... | ... |
| SRR3102556 | -5.292148 | -2.756433 | CDI |
| SRR3102547 | -1.763199 | 0.052028 | CDI |
| SRR3102572 | -1.139385 | -1.983172 | CDI |
| SRR3102515 | -3.229330 | -3.165894 | CDI |
| SRR3102517 | -2.452133 | -0.887940 | CDI |
305 rows × 3 columns
[33]:
g = ggplot(plsda_s.scores, aes(x='DIM1',y='DIM2', color='labels'))
g += geom_point()
g += scale_color_manual(name='Status',values = {"CDI":cdi_color, "healthy":healthy_color})
g += theme_classic()
g += theme(plot_background=element_blank(),
panel_background=element_blank(),
legend_background=element_blank(),
axis_line=element_line(color='black'),
legend_text=element_text(color='black', weight='bold'),
axis_text=element_text(color='black', weight='bold'),
axis_title=element_text(color='black', weight='bold'),
legend_title=element_text(color='black', weight='bold'))
g.save('results/PLS-DA.png', format='png', dpi=300, transparent=True)
g
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/plotnine/ggplot.py:729: PlotnineWarning: Saving 6.4 x 4.8 in image.
from_inches(height, units), units), PlotnineWarning)
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/plotnine/ggplot.py:730: PlotnineWarning: Filename: results/PLS-DA.png
warn('Filename: {}'.format(filename), PlotnineWarning)
[33]:
<ggplot: (-9223363254321219135)>
The separation appears clearly in the first latent variable (DIM1)
[34]:
g = ggplot(plsda_s.top_weights, aes(x='name',y='DIM1', fill='DIM1'))
g += geom_bar(stat='identity', width=0.7)
g += coord_flip()
g += scale_fill_gradient(name = 'weight', low=healthy_color, high=cdi_color)
g += xlab('species')
g += ylab('weight in PC1')
g += theme_classic()
g += theme(plot_background=element_blank(),
panel_background=element_blank(),
legend_background=element_blank(),
axis_line=element_line(color='black'),
legend_text=element_text(color='black', weight='bold'),
axis_text=element_text(color='black', weight='bold'),
axis_title=element_text(color='black', weight='bold'),
legend_title=element_text(color='black', weight='bold'))
g.save('results/weight_CDI.png', format='png', dpi=300, transparent=True)
g
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/plotnine/ggplot.py:729: PlotnineWarning: Saving 6.4 x 4.8 in image.
from_inches(height, units), units), PlotnineWarning)
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/plotnine/ggplot.py:730: PlotnineWarning: Filename: results/weight_CDI.png
warn('Filename: {}'.format(filename), PlotnineWarning)
[34]:
<ggplot: (8782533497590)>
[35]:
X_s_heatmap = X_s.merge(labels_s, left_index=True, right_index=True).sort_values('labels').drop('labels', axis=1)
X_s_heatmap.columns = ncbi.get_taxid_translator(X_s_heatmap.columns).values()
samp_colors = list(np.where(labels_s['labels'] == 'CDI', cdi_color,healthy_color))
sns.clustermap(X_s_heatmap.loc[:,plsda_s.top_weights['name']], row_colors=samp_colors, metric='braycurtis')
[35]:
<seaborn.matrix.ClusterGrid at 0x7fcd7ca32278>
[36]:
labels_s
[36]:
| labels | |
|---|---|
| SRR5578998 | healthy |
| SRR5579099 | healthy |
| SRR5579045 | healthy |
| SRR5578981 | healthy |
| SRR5579095 | healthy |
| ... | ... |
| SRR3102556 | CDI |
| SRR3102547 | CDI |
| SRR3102572 | CDI |
| SRR3102515 | CDI |
| SRR3102517 | CDI |
305 rows × 1 columns
[37]:
mds_s = mds(X_s, labels_s['labels'], metric='euclidean')
[38]:
g = ggplot(mds_s.embedding, aes(x='DIM1',y='DIM2', color='labels'))
g += geom_point()
g += scale_color_manual(name='Status',values = {"CDI":cdi_color, "healthy":healthy_color})
g += theme_classic()
g += theme(plot_background=element_blank(),
panel_background=element_blank(),
legend_background=element_blank(),
axis_line=element_line(color='black'),
legend_text=element_text(color='black', weight='bold'),
axis_text=element_text(color='black', weight='bold'),
axis_title=element_text(color='black', weight='bold'),
legend_title=element_text(color='black', weight='bold'))
g.save('results/PCoA.png', format='png', dpi=300, transparent=True)
g
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/plotnine/ggplot.py:729: PlotnineWarning: Saving 6.4 x 4.8 in image.
from_inches(height, units), units), PlotnineWarning)
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/plotnine/ggplot.py:730: PlotnineWarning: Filename: results/PCoA.png
warn('Filename: {}'.format(filename), PlotnineWarning)
[38]:
<ggplot: (-9223363254388601077)>
[39]:
plsda_g = plsda(X_g, Y_g, labels_g)
[40]:
g = ggplot(plsda_g.scores, aes(x='DIM1',y='DIM2', color='labels'))
g += geom_point()
g += scale_color_manual(name='Status',values = {"CDI":cdi_color, "healthy":healthy_color})
g += theme_classic()
g += theme(plot_background=element_blank(),
panel_background=element_blank(),
legend_background=element_blank(),
axis_line=element_line(color='black'),
legend_text=element_text(color='black', weight='bold'),
axis_text=element_text(color='black', weight='bold'),
axis_title=element_text(color='black', weight='bold'),
legend_title=element_text(color='black', weight='bold'))
g.save('results/PLS-DA_genus.png', format='png', dpi=300, transparent=True)
g
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/plotnine/ggplot.py:729: PlotnineWarning: Saving 6.4 x 4.8 in image.
from_inches(height, units), units), PlotnineWarning)
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/plotnine/ggplot.py:730: PlotnineWarning: Filename: results/PLS-DA_genus.png
warn('Filename: {}'.format(filename), PlotnineWarning)
[40]:
<ggplot: (8782466170155)>
[41]:
g = ggplot(plsda_g.top_weights, aes(x='name',y='DIM1', fill='DIM1'))
g += geom_bar(stat='identity', width=0.7)
g += coord_flip()
g += scale_fill_gradient(name = 'weight', low=healthy_color, high=cdi_color)
g += xlab('Genus')
g += ylab('weight in PC1')
g += theme_classic()
g += theme(plot_background=element_blank(),
panel_background=element_blank(),
legend_background=element_blank(),
axis_line=element_line(color=healthy_color),
legend_text=element_text(color=healthy_color, weight='bold'),
axis_text=element_text(color=healthy_color, weight='bold'),
axis_title=element_text(color=healthy_color, weight='bold'),
legend_title=element_text(color=healthy_color, weight='bold'))
g.save('results/weight_CDI_genus.png', format='png', dpi=300, transparent=True)
g
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/plotnine/ggplot.py:729: PlotnineWarning: Saving 6.4 x 4.8 in image.
from_inches(height, units), units), PlotnineWarning)
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/plotnine/ggplot.py:730: PlotnineWarning: Filename: results/weight_CDI_genus.png
warn('Filename: {}'.format(filename), PlotnineWarning)
[41]:
<ggplot: (8782466099411)>
[42]:
X_g_heatmap = X_g.merge(labels_g, left_index=True, right_index=True).sort_values('labels').drop('labels', axis=1)
X_g_heatmap.columns = ncbi.get_taxid_translator(X_g_heatmap.columns).values()
samp_colors = list(np.where(labels_g['labels'] == 'CDI', cdi_color,healthy_color))
sns.clustermap(X_g_heatmap.loc[:,plsda_g.top_weights['name']], row_colors=samp_colors, metric='braycurtis')
[42]:
<seaborn.matrix.ClusterGrid at 0x7fcd3c30a198>
MDS
[43]:
mds_g = mds(X_g, labels_g)
[44]:
g = ggplot(mds_g.embedding, aes(x='DIM1',y='DIM2', color='labels'))
g += geom_point()
g += scale_color_manual(name='Status',values = {"CDI":cdi_color, "healthy":healthy_color})
g += theme_classic()
g += theme(plot_background=element_blank(),
panel_background=element_blank(),
legend_background=element_blank(),
axis_line=element_line(color='black'),
legend_text=element_text(color='black', weight='bold'),
axis_text=element_text(color='black', weight='bold'),
axis_title=element_text(color='black', weight='bold'),
legend_title=element_text(color='black', weight='bold'))
g.save('results/PCoA.png', format='png', dpi=300, transparent=True)
g
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/plotnine/ggplot.py:729: PlotnineWarning: Saving 6.4 x 4.8 in image.
from_inches(height, units), units), PlotnineWarning)
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/plotnine/ggplot.py:730: PlotnineWarning: Filename: results/PCoA.png
warn('Filename: {}'.format(filename), PlotnineWarning)
[44]:
<ggplot: (8782533528931)>
Pre-analysis conclusion:¶
Based on the clustering (Heatmap) and the MDS, there is a better separation of the two classed at the genus level.
Preparing data for sourcepredict¶
on species¶
[45]:
train_species = X_s.T.sample(frac=0.8, axis=1, random_state=2)
[46]:
test_species = X_s.T.drop(train_species.columns, axis=1)
[47]:
train_labels_species = labels_s.loc[train_species.columns,:]
[48]:
test_labels_species = labels_s.loc[test_species.columns,:]
[49]:
train_species.to_csv("source_species.csv")
test_species.to_csv("sink_species.csv")
train_labels_species.to_csv("source_labels_species.csv")
test_labels_species.to_csv("sink_labels_species.csv")
on genus¶
[50]:
train_genus = X_g.T.sample(frac=0.8, axis=1, random_state=2)
[51]:
test_genus = X_g.T.drop(train_genus.columns, axis=1)
[52]:
train_labels_genus = labels_g.loc[train_genus.columns,:]
[53]:
test_labels_genus = labels_g.loc[test_genus.columns,:]
[54]:
train_genus.to_csv("source_genus.csv")
test_genus.to_csv("sink_genus.csv")
train_labels_genus.to_csv("source_labels_genus.csv")
test_labels_genus.to_csv("sink_labels_genus.csv")
Running sourcepredict¶
on species¶
[55]:
%%time
! /projects1/users/borry/18_sourcepredict/sourcepredict -s source_species.csv -l source_labels_species.csv sink_species.csv -n None -me tsne -di 2 -t 6 -e embedding_species.csv
Step 1: Checking for unknown proportion
== Sample: SRR5578937 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578937
known:97.62%
unknown:2.38%
== Sample: SRR5578953 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578953
known:97.62%
unknown:2.38%
== Sample: SRR5578924 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578924
known:97.62%
unknown:2.38%
== Sample: SRR5578990 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578990
known:97.62%
unknown:2.38%
== Sample: SRR5579006 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579006
known:97.62%
unknown:2.38%
== Sample: SRR5579106 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579106
known:97.62%
unknown:2.38%
== Sample: SRR5579003 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579003
known:97.62%
unknown:2.38%
== Sample: SRR5579074 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579074
known:97.62%
unknown:2.38%
== Sample: SRR5578943 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578943
known:97.62%
unknown:2.38%
== Sample: SRR5579072 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579072
known:97.62%
unknown:2.38%
== Sample: SRR5579036 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579036
known:97.62%
unknown:2.38%
== Sample: SRR5578962 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578962
known:97.63%
unknown:2.37%
== Sample: SRR5578931 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578931
known:97.62%
unknown:2.38%
== Sample: SRR5579039 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579039
known:97.62%
unknown:2.38%
== Sample: SRR5579061 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579061
known:97.62%
unknown:2.38%
== Sample: SRR5578942 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578942
known:97.62%
unknown:2.38%
== Sample: SRR5578919 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578919
known:97.62%
unknown:2.38%
== Sample: SRR5578913 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578913
known:97.62%
unknown:2.38%
== Sample: SRR5578991 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578991
known:97.62%
unknown:2.38%
== Sample: SRR5579100 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579100
known:97.62%
unknown:2.38%
== Sample: SRR5579037 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579037
known:97.72%
unknown:2.28%
== Sample: SRR5579048 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579048
known:97.76%
unknown:2.24%
== Sample: SRR5578952 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578952
known:97.62%
unknown:2.38%
== Sample: SRR5578930 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578930
known:97.62%
unknown:2.38%
== Sample: SRR5579017 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579017
known:97.62%
unknown:2.38%
== Sample: SRR5579005 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579005
known:97.62%
unknown:2.38%
== Sample: SRR5579055 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579055
known:97.62%
unknown:2.38%
== Sample: SRR5579002 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579002
known:97.62%
unknown:2.38%
== Sample: SRR5578968 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578968
known:97.62%
unknown:2.38%
== Sample: SRR5579116 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579116
known:97.62%
unknown:2.38%
== Sample: SRR5578910 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578910
known:97.66%
unknown:2.34%
== Sample: SRR5578947 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578947
known:97.62%
unknown:2.38%
== Sample: SRR5579080 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579080
known:97.62%
unknown:2.38%
== Sample: SRR5579103 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579103
known:97.62%
unknown:2.38%
== Sample: SRR5579035 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579035
known:97.62%
unknown:2.38%
== Sample: SRR5579085 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579085
known:97.62%
unknown:2.38%
== Sample: SRR5579079 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579079
known:97.63%
unknown:2.37%
== Sample: SRR5579018 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579018
known:97.62%
unknown:2.38%
== Sample: SRR5579001 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579001
known:97.62%
unknown:2.38%
== Sample: SRR5578938 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578938
known:97.62%
unknown:2.38%
== Sample: SRR5579108 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579108
known:97.62%
unknown:2.38%
== Sample: SRR3102551 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102551
known:97.62%
unknown:2.38%
== Sample: SRR3102397 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102397
known:97.62%
unknown:2.38%
== Sample: SRR3102539 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102539
known:96.97%
unknown:3.03%
== Sample: SRR3102516 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102516
known:97.62%
unknown:2.38%
== Sample: SRR3102401 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102401
known:97.62%
unknown:2.38%
== Sample: SRR3102398 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102398
known:97.62%
unknown:2.38%
== Sample: SRR3102573 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102573
known:97.62%
unknown:2.38%
== Sample: SRR3102518 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102518
known:74.16%
unknown:25.84%
== Sample: SRR3102463 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102463
known:97.68%
unknown:2.32%
== Sample: SRR3102581 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102581
known:97.62%
unknown:2.38%
== Sample: SRR3102359 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102359
known:97.62%
unknown:2.38%
== Sample: SRR3102427 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102427
known:97.62%
unknown:2.38%
== Sample: SRR3102369 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102369
known:97.62%
unknown:2.38%
== Sample: SRR3102356 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102356
known:64.17%
unknown:35.83%
== Sample: SRR3102561 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102561
known:97.62%
unknown:2.38%
== Sample: SRR3102374 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102374
known:97.62%
unknown:2.38%
== Sample: SRR3102372 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102372
known:97.62%
unknown:2.38%
== Sample: SRR3102386 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102386
known:97.62%
unknown:2.38%
== Sample: SRR3102486 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102486
known:70.98%
unknown:29.02%
== Sample: SRR3102556 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102556
known:69.48%
unknown:30.52%
Step 2: Checking for source proportion
Computing weighted_unifrac distance on species rank
Warning: ``tree`` must be rooted.
There is a polytomy ar the root of this taxonomic tree.
Unifrac distances wont't work properly.
Computing Bray-Curtis distance instead.
TSNE embedding in 2 dimensions
KNN machine learning
Performing 5 fold cross validation on 6 cores...
Trained KNN classifier with 10 neighbors
-> Testing Accuracy: 0.9
----------------------
- Sample: SRR5578937
CDI:5.35%
healthy:94.65%
- Sample: SRR5578953
CDI:15.66%
healthy:84.34%
- Sample: SRR5578924
CDI:11.87%
healthy:88.13%
- Sample: SRR5578990
CDI:11.32%
healthy:88.68%
- Sample: SRR5579006
CDI:5.35%
healthy:94.65%
- Sample: SRR5579106
CDI:5.35%
healthy:94.65%
- Sample: SRR5579003
CDI:7.22%
healthy:92.78%
- Sample: SRR5579074
CDI:7.25%
healthy:92.75%
- Sample: SRR5578943
CDI:92.09%
healthy:7.91%
- Sample: SRR5579072
CDI:5.35%
healthy:94.65%
- Sample: SRR5579036
CDI:5.35%
healthy:94.65%
- Sample: SRR5578962
CDI:5.35%
healthy:94.65%
- Sample: SRR5578931
CDI:5.35%
healthy:94.65%
- Sample: SRR5579039
CDI:15.35%
healthy:84.65%
- Sample: SRR5579061
CDI:11.1%
healthy:88.9%
- Sample: SRR5578942
CDI:5.35%
healthy:94.65%
- Sample: SRR5578919
CDI:68.11%
healthy:31.89%
- Sample: SRR5578913
CDI:8.83%
healthy:91.17%
- Sample: SRR5578991
CDI:5.35%
healthy:94.65%
- Sample: SRR5579100
CDI:13.42%
healthy:86.58%
- Sample: SRR5579037
CDI:5.35%
healthy:94.65%
- Sample: SRR5579048
CDI:5.35%
healthy:94.65%
- Sample: SRR5578952
CDI:14.35%
healthy:85.65%
- Sample: SRR5578930
CDI:9.87%
healthy:90.13%
- Sample: SRR5579017
CDI:9.84%
healthy:90.16%
- Sample: SRR5579005
CDI:5.35%
healthy:94.65%
- Sample: SRR5579055
CDI:9.51%
healthy:90.49%
- Sample: SRR5579002
CDI:92.09%
healthy:7.91%
- Sample: SRR5578968
CDI:5.35%
healthy:94.65%
- Sample: SRR5579116
CDI:5.35%
healthy:94.65%
- Sample: SRR5578910
CDI:8.34%
healthy:91.66%
- Sample: SRR5578947
CDI:5.35%
healthy:94.65%
- Sample: SRR5579080
CDI:5.35%
healthy:94.65%
- Sample: SRR5579103
CDI:5.35%
healthy:94.65%
- Sample: SRR5579035
CDI:9.01%
healthy:90.99%
- Sample: SRR5579085
CDI:7.16%
healthy:92.84%
- Sample: SRR5579079
CDI:7.01%
healthy:92.99%
- Sample: SRR5579018
CDI:7.37%
healthy:92.63%
- Sample: SRR5579001
CDI:5.35%
healthy:94.65%
- Sample: SRR5578938
CDI:9.36%
healthy:90.64%
- Sample: SRR5579108
CDI:10.13%
healthy:89.87%
- Sample: SRR3102551
CDI:10.39%
healthy:89.61%
- Sample: SRR3102397
CDI:86.75%
healthy:13.25%
- Sample: SRR3102539
CDI:88.59%
healthy:11.41%
- Sample: SRR3102516
CDI:79.7%
healthy:20.3%
- Sample: SRR3102401
CDI:87.61%
healthy:12.39%
- Sample: SRR3102398
CDI:92.09%
healthy:7.91%
- Sample: SRR3102573
CDI:92.09%
healthy:7.91%
- Sample: SRR3102518
CDI:83.99%
healthy:16.01%
- Sample: SRR3102463
CDI:78.19%
healthy:21.81%
- Sample: SRR3102581
CDI:5.35%
healthy:94.65%
- Sample: SRR3102359
CDI:92.09%
healthy:7.91%
- Sample: SRR3102427
CDI:92.09%
healthy:7.91%
- Sample: SRR3102369
CDI:92.09%
healthy:7.91%
- Sample: SRR3102356
CDI:82.45%
healthy:17.55%
- Sample: SRR3102561
CDI:78.45%
healthy:21.55%
- Sample: SRR3102374
CDI:86.09%
healthy:13.91%
- Sample: SRR3102372
CDI:70.12%
healthy:29.88%
- Sample: SRR3102386
CDI:88.18%
healthy:11.82%
- Sample: SRR3102486
CDI:86.38%
healthy:13.62%
- Sample: SRR3102556
CDI:88.76%
healthy:11.24%
Sourcepredict result written to sink_species.sourcepredict.csv
Embedding coordinates written to embedding_species.csv
CPU times: user 896 ms, sys: 320 ms, total: 1.22 s
Wall time: 1min 1s
on genus¶
[56]:
%%time
! /projects1/users/borry/18_sourcepredict/sourcepredict -s source_genus.csv -l source_labels_genus.csv sink_genus.csv -r genus -n None -me tsne -di 2 -dt weighted_unifrac -t 6 -e embedding_genus.csv
Step 1: Checking for unknown proportion
== Sample: SRR5578953 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578953
known:97.37%
unknown:2.63%
== Sample: SRR5578924 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578924
known:97.37%
unknown:2.63%
== Sample: SRR5579094 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579094
known:97.37%
unknown:2.63%
== Sample: SRR5579106 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579106
known:97.37%
unknown:2.63%
== Sample: SRR5579030 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579030
known:97.37%
unknown:2.63%
== Sample: SRR5579113 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579113
known:97.37%
unknown:2.63%
== Sample: SRR5579077 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579077
known:97.43%
unknown:2.57%
== Sample: SRR5578962 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578962
known:97.37%
unknown:2.63%
== Sample: SRR5579050 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579050
known:97.37%
unknown:2.63%
== Sample: SRR5578957 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578957
known:97.37%
unknown:2.63%
== Sample: SRR5579028 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579028
known:97.37%
unknown:2.63%
== Sample: SRR5579071 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579071
known:97.38%
unknown:2.62%
== Sample: SRR5578993 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578993
known:97.37%
unknown:2.63%
== Sample: SRR5578963 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578963
known:97.37%
unknown:2.63%
== Sample: SRR5579100 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579100
known:97.49%
unknown:2.51%
== Sample: SRR5578906 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578906
known:97.37%
unknown:2.63%
== Sample: SRR5579112 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579112
known:97.37%
unknown:2.63%
== Sample: SRR5579013 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579013
known:97.37%
unknown:2.63%
== Sample: SRR5579042 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579042
known:97.37%
unknown:2.63%
== Sample: SRR5578912 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578912
known:97.37%
unknown:2.63%
== Sample: SRR5579002 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579002
known:97.37%
unknown:2.63%
== Sample: SRR5579089 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579089
known:97.37%
unknown:2.63%
== Sample: SRR5579087 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579087
known:97.37%
unknown:2.63%
== Sample: SRR5579102 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579102
known:97.37%
unknown:2.63%
== Sample: SRR5579049 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 0.99
----------------------
- Sample: SRR5579049
known:98.22%
unknown:1.78%
== Sample: SRR5579029 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579029
known:97.37%
unknown:2.63%
== Sample: SRR5578910 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578910
known:97.37%
unknown:2.63%
== Sample: SRR5579010 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579010
known:97.37%
unknown:2.63%
== Sample: SRR5579064 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579064
known:97.37%
unknown:2.63%
== Sample: SRR5579051 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579051
known:97.37%
unknown:2.63%
== Sample: SRR5578984 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578984
known:97.37%
unknown:2.63%
== Sample: SRR5578949 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578949
known:97.37%
unknown:2.63%
== Sample: SRR5578975 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578975
known:97.37%
unknown:2.63%
== Sample: SRR5578940 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578940
known:97.37%
unknown:2.63%
== Sample: SRR5578925 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578925
known:97.41%
unknown:2.59%
== Sample: SRR5579015 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5579015
known:97.37%
unknown:2.63%
== Sample: SRR5578927 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578927
known:97.49%
unknown:2.51%
== Sample: SRR5578923 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR5578923
known:97.37%
unknown:2.63%
== Sample: SRR3102557 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102557
known:97.37%
unknown:2.63%
== Sample: SRR3102424 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 0.99
----------------------
- Sample: SRR3102424
known:92.66%
unknown:7.34%
== Sample: SRR3102366 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102366
known:93.41%
unknown:6.59%
== Sample: SRR3102422 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 0.99
----------------------
- Sample: SRR3102422
known:89.06%
unknown:10.94%
== Sample: SRR3102462 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 0.98
----------------------
- Sample: SRR3102462
known:69.99%
unknown:30.01%
== Sample: SRR3102449 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102449
known:96.44%
unknown:3.56%
== Sample: SRR3102526 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102526
known:96.96%
unknown:3.04%
== Sample: SRR3102497 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102497
known:95.78%
unknown:4.22%
== Sample: SRR3102402 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102402
known:97.37%
unknown:2.63%
== Sample: SRR3102440 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102440
known:97.37%
unknown:2.63%
== Sample: SRR3102463 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102463
known:97.37%
unknown:2.63%
== Sample: SRR3102379 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102379
known:91.82%
unknown:8.18%
== Sample: SRR3102529 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102529
known:91.89%
unknown:8.11%
== Sample: SRR3102427 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102427
known:97.39%
unknown:2.61%
== Sample: SRR3102550 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102550
known:97.37%
unknown:2.63%
== Sample: SRR3102410 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 0.99
----------------------
- Sample: SRR3102410
known:94.87%
unknown:5.13%
== Sample: SRR3102376 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102376
known:92.88%
unknown:7.12%
== Sample: SRR3102533 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102533
known:97.39%
unknown:2.61%
== Sample: SRR3102489 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102489
known:95.74%
unknown:4.26%
== Sample: SRR3102490 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102490
known:97.37%
unknown:2.63%
== Sample: SRR3102580 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102580
known:97.37%
unknown:2.63%
== Sample: SRR3102375 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102375
known:97.37%
unknown:2.63%
== Sample: SRR3102483 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102483
known:97.37%
unknown:2.63%
== Sample: SRR3102535 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102535
known:96.31%
unknown:3.69%
== Sample: SRR3102446 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102446
known:97.41%
unknown:2.59%
== Sample: SRR3102527 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102527
known:97.37%
unknown:2.63%
== Sample: SRR3102409 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102409
known:97.37%
unknown:2.63%
== Sample: SRR3102362 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102362
known:97.37%
unknown:2.63%
== Sample: SRR3102487 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 0.99
----------------------
- Sample: SRR3102487
known:96.66%
unknown:3.34%
== Sample: SRR3102517 ==
Adding unknown
Normalizing (no normalization)
Computing Bray-Curtis distance
Performing MDS embedding in 2 dimensions
KNN machine learning
Training KNN classifier on 6 cores...
-> Testing Accuracy: 1.0
----------------------
- Sample: SRR3102517
known:97.37%
unknown:2.63%
Step 2: Checking for source proportion
Computing weighted_unifrac distance on genus rank
TSNE embedding in 2 dimensions
KNN machine learning
Performing 5 fold cross validation on 6 cores...
Trained KNN classifier with 10 neighbors
-> Testing Accuracy: 0.85
----------------------
- Sample: SRR5578953
CDI:2.9%
healthy:97.1%
- Sample: SRR5578924
CDI:1.71%
healthy:98.29%
- Sample: SRR5579094
CDI:1.71%
healthy:98.29%
- Sample: SRR5579106
CDI:1.71%
healthy:98.29%
- Sample: SRR5579030
CDI:1.71%
healthy:98.29%
- Sample: SRR5579113
CDI:1.71%
healthy:98.29%
- Sample: SRR5579077
CDI:13.41%
healthy:86.59%
- Sample: SRR5578962
CDI:1.71%
healthy:98.29%
- Sample: SRR5579050
CDI:2.26%
healthy:97.74%
- Sample: SRR5578957
CDI:2.82%
healthy:97.18%
- Sample: SRR5579028
CDI:1.71%
healthy:98.29%
- Sample: SRR5579071
CDI:2.77%
healthy:97.23%
- Sample: SRR5578993
CDI:2.78%
healthy:97.22%
- Sample: SRR5578963
CDI:1.71%
healthy:98.29%
- Sample: SRR5579100
CDI:6.98%
healthy:93.02%
- Sample: SRR5578906
CDI:3.1%
healthy:96.9%
- Sample: SRR5579112
CDI:2.95%
healthy:97.05%
- Sample: SRR5579013
CDI:1.71%
healthy:98.29%
- Sample: SRR5579042
CDI:1.71%
healthy:98.29%
- Sample: SRR5578912
CDI:95.31%
healthy:4.69%
- Sample: SRR5579002
CDI:1.71%
healthy:98.29%
- Sample: SRR5579089
CDI:1.71%
healthy:98.29%
- Sample: SRR5579087
CDI:88.23%
healthy:11.77%
- Sample: SRR5579102
CDI:1.71%
healthy:98.29%
- Sample: SRR5579049
CDI:9.53%
healthy:90.47%
- Sample: SRR5579029
CDI:1.71%
healthy:98.29%
- Sample: SRR5578910
CDI:1.71%
healthy:98.29%
- Sample: SRR5579010
CDI:13.19%
healthy:86.81%
- Sample: SRR5579064
CDI:2.48%
healthy:97.52%
- Sample: SRR5579051
CDI:1.71%
healthy:98.29%
- Sample: SRR5578984
CDI:44.06%
healthy:55.94%
- Sample: SRR5578949
CDI:3.11%
healthy:96.89%
- Sample: SRR5578975
CDI:1.71%
healthy:98.29%
- Sample: SRR5578940
CDI:1.71%
healthy:98.29%
- Sample: SRR5578925
CDI:2.55%
healthy:97.45%
- Sample: SRR5579015
CDI:1.71%
healthy:98.29%
- Sample: SRR5578927
CDI:2.93%
healthy:97.07%
- Sample: SRR5578923
CDI:4.14%
healthy:95.86%
- Sample: SRR3102557
CDI:95.31%
healthy:4.69%
- Sample: SRR3102424
CDI:92.07%
healthy:7.93%
- Sample: SRR3102366
CDI:95.31%
healthy:4.69%
- Sample: SRR3102422
CDI:90.09%
healthy:9.91%
- Sample: SRR3102462
CDI:93.16%
healthy:6.84%
- Sample: SRR3102449
CDI:92.87%
healthy:7.13%
- Sample: SRR3102526
CDI:95.31%
healthy:4.69%
- Sample: SRR3102497
CDI:95.31%
healthy:4.69%
- Sample: SRR3102402
CDI:95.31%
healthy:4.69%
- Sample: SRR3102440
CDI:95.31%
healthy:4.69%
- Sample: SRR3102463
CDI:95.31%
healthy:4.69%
- Sample: SRR3102379
CDI:94.18%
healthy:5.82%
- Sample: SRR3102529
CDI:93.06%
healthy:6.94%
- Sample: SRR3102427
CDI:55.04%
healthy:44.96%
- Sample: SRR3102550
CDI:95.31%
healthy:4.69%
- Sample: SRR3102410
CDI:95.31%
healthy:4.69%
- Sample: SRR3102376
CDI:2.71%
healthy:97.29%
- Sample: SRR3102533
CDI:93.57%
healthy:6.43%
- Sample: SRR3102489
CDI:75.7%
healthy:24.3%
- Sample: SRR3102490
CDI:95.31%
healthy:4.69%
- Sample: SRR3102580
CDI:95.31%
healthy:4.69%
- Sample: SRR3102375
CDI:95.31%
healthy:4.69%
- Sample: SRR3102483
CDI:45.14%
healthy:54.86%
- Sample: SRR3102535
CDI:92.53%
healthy:7.47%
- Sample: SRR3102446
CDI:95.31%
healthy:4.69%
- Sample: SRR3102527
CDI:95.31%
healthy:4.69%
- Sample: SRR3102409
CDI:95.31%
healthy:4.69%
- Sample: SRR3102362
CDI:95.31%
healthy:4.69%
- Sample: SRR3102487
CDI:95.31%
healthy:4.69%
- Sample: SRR3102517
CDI:95.31%
healthy:4.69%
Sourcepredict result written to sink_genus.sourcepredict.csv
Embedding coordinates written to embedding_genus.csv
CPU times: user 1.18 s, sys: 368 ms, total: 1.55 s
Wall time: 1min 19s
Reading Sourcepredict results¶
[57]:
from sklearn.metrics import accuracy_score
[58]:
pred_genus = pd.read_csv("sink_genus.sourcepredict.csv", index_col=0)
test_labels_genus = pd.read_csv("sink_labels_genus.csv", index_col=0)
[59]:
conf_table_genus = pred_genus.idxmax(axis=0).to_frame(name='predicted').merge(test_labels_genus, left_index=True, right_index=True)
[60]:
conf_table_genus = conf_table_genus.dropna()
[61]:
conf_table_genus.shape
[61]:
(68, 2)
[62]:
conf_table_genus.apply(pd.value_counts, axis=0)
[62]:
| predicted | labels | |
|---|---|---|
| healthy | 38 | 38 |
| CDI | 30 | 30 |
[63]:
acc_genus = accuracy_score(y_true=conf_table_genus['labels'], y_pred=conf_table_genus['predicted'])
print(f"Accuracy: {round(acc_genus,2)}")
Accuracy: 0.94
[64]:
from plotnine import *
[65]:
embed = pd.read_csv("embedding_genus.csv", index_col=0)
embed = embed.rename(columns={'labels':'type'})
embed['type'] = embed['type'].str.replace('CDI','source').replace('healthy','source')
embed = embed.join(labels_g['labels']).rename(columns={'labels':'actual'})
embed = embed.join(pd.Series(pred_genus.idxmax(), name='predicted'))
[66]:
g = ggplot(embed.query("type == 'source'"), aes(x='PC1',y='PC2', color='labels'))
g += geom_point(size=3, stroke=1, alpha=0.5)
g += scale_color_manual(name = 'Reference', values = {"CDI":cdi_color, "healthy":healthy_color})
g += xlab('DIM1')
g += ylab('DIM2')
g += theme_classic()
g += theme(plot_background=element_blank(),
panel_background=element_blank(),
legend_background=element_blank(),
axis_line=element_line(color=healthy_color),
legend_text=element_text(color='black', weight='bold'),
axis_text=element_text(color='black', weight='bold'),
axis_title=element_text(color='black', weight='bold'),
legend_title=element_text(color='black', weight='bold'))
g.save('results/train_embedding.png', format='png', dpi=300, transparent=True)
g
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/plotnine/ggplot.py:729: PlotnineWarning: Saving 6.4 x 4.8 in image.
from_inches(height, units), units), PlotnineWarning)
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/plotnine/ggplot.py:730: PlotnineWarning: Filename: results/train_embedding.png
warn('Filename: {}'.format(filename), PlotnineWarning)
[66]:
<ggplot: (8782530729613)>
[67]:
g = ggplot(embed.query("type == 'sink'"), aes(x='PC1',y='PC2', color='predicted'))
g += geom_point(size=4, shape='o', fill = 'black', stroke=2)
g += geom_point(data=embed.query("type == 'sink'"), mapping=aes(x='PC1',y='PC2', fill='actual'), size=3, color='black', stroke=1)
g += scale_color_manual(name = 'Reference', values = {"CDI":cdi_color, "healthy":healthy_color})
g += scale_fill_manual(name = 'Prediction', values = {"CDI":cdi_color, "healthy":healthy_color})
g += xlab('DIM1')
g += ylab('DIM2')
g += theme_classic()
g += theme(plot_background=element_blank(),
panel_background=element_blank(),
legend_background=element_blank(),
axis_line=element_line(color='black'),
legend_text=element_text(color='black', weight='bold'),
axis_text=element_text(color='black', weight='bold'),
axis_title=element_text(color='black', weight='bold'),
legend_title=element_text(color='black', weight='bold'))
g.save('results/test_embedding.png', format='png', dpi=300, transparent=True)
g
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/plotnine/ggplot.py:729: PlotnineWarning: Saving 6.4 x 4.8 in image.
from_inches(height, units), units), PlotnineWarning)
/projects1/users/borry/15_miniconda3/envs/sourcepredict/lib/python3.6/site-packages/plotnine/ggplot.py:730: PlotnineWarning: Filename: results/test_embedding.png
warn('Filename: {}'.format(filename), PlotnineWarning)
[67]:
<ggplot: (-9223363254324085430)>
Comparing with Sourcetracker 2¶
Generating the data for Sourcetracker 2
[68]:
X_g.T.to_csv("st_genus.txt", sep="\t", index_label="TAXID")
test_labels_genus[‘SourceSink’]= [‘sink’]*test_labels_genus.shape[0]
[69]:
train_labels_genus['SourceSink'] = ['source']*train_labels_genus.shape[0]
[70]:
st_metadata = train_labels_genus.append(test_labels_genus).rename(columns = {"labels":"Env"})[['SourceSink','Env']]
st_metadata['SourceSink'][train_labels_genus.index] = ['source']*train_labels_genus.shape[0]
st_metadata['SourceSink'][test_labels_genus.index] = ['sink']*test_labels_genus.shape[0]
st_metadata
[70]:
| SourceSink | Env | |
|---|---|---|
| SRR5579101 | source | healthy |
| SRR3102473 | source | CDI |
| SRR3102501 | source | CDI |
| SRR5578964 | source | healthy |
| SRR5579017 | source | healthy |
| ... | ... | ... |
| SRR3102527 | sink | CDI |
| SRR3102409 | sink | CDI |
| SRR3102362 | sink | CDI |
| SRR3102487 | sink | CDI |
| SRR3102517 | sink | CDI |
340 rows × 2 columns
[71]:
st_metadata.to_csv("st_genus_metadata.csv", sep="\t", index_label='#SampleID')
sourcetracker2 gibbs -i st_genus.biom -m st_genus_metadata.csv -o st_genus_out --jobs 6 --source_rarefaction_depth 0 --sink_rarefaction_depth 0
Reading sourcetracker results
[72]:
st_pred = pd.read_csv("st_genus_out/mixing_proportions.txt", sep = "\t", index_col=0)
[73]:
st2_pred = st_pred.idxmax(axis=1).to_frame(name='predicted').merge(test_labels_genus, left_index=True, right_index=True).rename(columns={'labels':'actual'})
[74]:
st2_pred.head()
[74]:
| predicted | actual | |
|---|---|---|
| SRR5578953 | healthy | healthy |
| SRR5578924 | healthy | healthy |
| SRR5579094 | healthy | healthy |
| SRR5579106 | healthy | healthy |
| SRR5579030 | healthy | healthy |
[75]:
st_acc_genus = accuracy_score(y_true=st2_pred['actual'], y_pred=st2_pred['predicted'])
print(f"Accuracy: {round(st_acc_genus,2)}")
Accuracy: 0.8