Compositional Tensor Factorization (CTF) for longitudinal or spatial repeat measures microbiome data.

Repeat measure experimental designs (e.g. time series) are a valid and powerful method to control for inter-individual variation. However, conventional dimensionality reduction methods can not account for the high-correlation of each subject to itself at a later time point. This inherent correlation structure can cause subject grouping to confound or even outweigh important phenotype groupings. To address this we will use Compositional Tensor Factorization (CTF) which we provide in the software package gemelli. CTF can account for repeated measures, compositionality, and sparsity in microbiome data.

To install the most up to date version of gemelli, run the following command

# pip (only supported for QIIME2 >= 2018.8)
pip install gemelli

In this tutorial we use gemelli to perform CTF on a time series dataset comparing Crohn’s and control subjects over a period of 25 weeks published in Vázquez-Baeza et al. First we will download the processed data originally from here. This data can be downloaded with the following links:

  • Table (table.qza) | download
  • Rarefied Table (rarefied-table.qza) | download
  • Sample Metadata (metadata.tsv) | download
  • Feature Metadata (taxonomy.qza) | download
  • Tree (sepp-insertion-tree.qza) | download

Note: This tutorial assumes you have installed QIIME2 using one of the procedures in the install documents. This tutorial also assumed you have installed, Qurro, DEICODE, and gemelli.

First, we will make a tutorial directory and download the data above and move the files to the IBD-2538/data directory:

mkdir IBD-2538
# move downloaded data here
mkdir IBD-2538/data
# make directory to store results
mkdir IBD-2538/core-metric-output

Next, we will demonstrate the issues with using conventional dimensionality reduction methods on time series data. To do this we will perform PCoA dimensionality reduction on weighted and unweighted UniFrac \beta-diversity distances. We will also run Aitchison Robust PCA with DEICODE which is built on the same framework as CTF but does not account for repeated measures.

qiime diversity beta-phylogenetic\
    --i-table  IBD-2538/data/rarefied-table.qza\
    --p-metric 'unweighted_unifrac'\
    --i-phylogeny IBD-2538/data/sepp-insertion-tree.qza\
    --o-distance-matrix IBD-2538/core-metric-output/unweighted-unifrac-distance.qza
qiime diversity beta-phylogenetic\
    --i-table  IBD-2538/data/rarefied-table.qza\
    --p-metric 'weighted_unifrac'\
    --i-phylogeny IBD-2538/data/sepp-insertion-tree.qza\
    --o-distance-matrix IBD-2538/core-metric-output/weighted-unifrac-distance.qza
qiime diversity pcoa\
    --i-distance-matrix IBD-2538/core-metric-output/unweighted-unifrac-distance.qza\
    --o-pcoa IBD-2538/core-metric-output/unweighted-unifrac-distance-pcoa.qza
qiime diversity pcoa\
    --i-distance-matrix IBD-2538/core-metric-output/weighted-unifrac-distance.qza\
    --o-pcoa IBD-2538/core-metric-output/weighted-unifrac-distance-pcoa.qza
qiime emperor plot\
    --i-pcoa IBD-2538/core-metric-output/unweighted-unifrac-distance-pcoa.qza\
    --m-metadata-file IBD-2538/data/metadata.tsv\
    --o-visualization IBD-2538/core-metric-output/unweighted-unifrac-distance-pcoa.qzv 
qiime emperor plot\
    --i-pcoa IBD-2538/core-metric-output/weighted-unifrac-distance-pcoa.qza\
    --m-metadata-file IBD-2538/data/metadata.tsv\
    --o-visualization IBD-2538/core-metric-output/weighted-unifrac-distance-pcoa.qzv  

Output:

Saved DistanceMatrix % Properties('phylogenetic') to: IBD-2538/core-metric-output/unweighted-unifrac-distance.qza
Saved DistanceMatrix % Properties('phylogenetic') to: IBD-2538/core-metric-output/weighted-unifrac-distance.qza
Saved PCoAResults to: IBD-2538/core-metric-output/unweighted-unifrac-distance-pcoa.qza
Saved PCoAResults to: IBD-2538/core-metric-output/weighted-unifrac-distance-pcoa.qza
Saved Visualization to: IBD-2538/core-metric-output/unweighted-unifrac-distance-pcoa.qzv
Saved Visualization to: IBD-2538/core-metric-output/weighted-unifrac-distance-pcoa.qzv
qiime deicode rpca\
    --i-table IBD-2538/data/table.qza\
    --o-biplot IBD-2538/core-metric-output/RPCA-biplot.qza\
    --o-distance-matrix IBD-2538/core-metric-output/RPCA-distance.qza
qiime emperor biplot\
    --i-biplot IBD-2538/core-metric-output/RPCA-biplot.qza \
    --m-sample-metadata-file IBD-2538/data/metadata.tsv \
    --m-feature-metadata-file IBD-2538/data/taxonomy.qza \
    --o-visualization IBD-2538/core-metric-output/RPCA-biplot.qzv  

Output:

Saved PCoAResults % Properties('biplot') to: IBD-2538/core-metric-output/RPCA-biplot.qza
Saved DistanceMatrix to: IBD-2538/core-metric-output/RPCA-distance.qza
Saved Visualization to: IBD-2538/core-metric-output/RPCA-biplot.qzv

Now we can visualize the sample groupings by host subject ID and IBD with Emperor. From this we can see the PCoA samples clearly separate by host subject ID which in some cases (e.g. UniFrac) can overwhelm the control (blue) v. Crohn’s disease (orange) sample groupings. Even in the case where the IBD grouping is not completely lost (e.g. RPCA) we can still see confounding groupings in the control (blue) groups by subject ID. This can complicate the interpretation of these analysis.

This confounding effect can also be observed in the statistics performed on pairwise \beta-diversity distances (e.g. PERMANOVA). For the purpose of exploring distance matrices, q2-longitudinal has many excellent methods to account for repeated measures data. You can find the q2-longitudinal tutorial here.

Compositional Tensor Factorization (CTF) Introduction

In order to account for the correlation among samples from the same subject we will employ compositional tensor factorization (CTF). CTF builds on the ability to account for compositionality and sparsity using the robust center log-ratio transform covered in the RPCA tutorial (found here) but restructures and factors the data as a tensor. Here we will run CTF through gemelli and explore/interpret the different results.

To run CTF we only need to run one command (gemelli ctf). The required input requirements are:

  1. table
    • The table is of type FeatureTable[Frequency] which is a table where the rows are features (e.g. ASVs/microbes), the columns are samples, and the entries are the number of sequences for each sample-feature pair.
  2. sample-metadata
    • This is a QIIME2 formatted metadata (e.g. tsv format) where the rows are samples matched to the (1) table and the columns are different sample data (e.g. time point).
  3. individual-id-column
    • This is the name of the column in the (2) metadata that indicates the individual subject/site (e.g. subject ID) that was sampled repeatedly.
  4. state-column
    • This is the name of the column in the (2) metadata that indicates the numeric repeated measure (e.g., Time in months/days) or non-numeric category (i.e. decade/body-site).
  5. output-dir
    • The desired location of the output. We will cover each output independently below.

There are also optional input parameters:

  • ( Optional ) feature-metadata-file
    • This is a metadata file (e.g. tsv, or FeatureTable[Taxonomy] .qza) where the rows are matched to the table features and the columns are feature metadata such as taxonomy, gene pathway, etc…

In this tutorial our individual-id-column is host_subject_id and our state-column is different time points denoted as timepoint in the sample metadata. Now we are ready to run CTF:

qiime gemelli ctf\
    --i-table  IBD-2538/data/table.qza  \
    --m-sample-metadata-file IBD-2538/data/metadata.tsv \
    --m-feature-metadata-file IBD-2538/data/taxonomy.qza \
    --p-state-column timepoint\
    --p-individual-id-column host_subject_id\
    --output-dir IBD-2538/ctf-results

Output:

Saved PCoAResults % Properties('biplot') to: IBD-2538/ctf-results/subject_biplot.qza
Saved PCoAResults % Properties('biplot') to: IBD-2538/ctf-results/state_biplot.qza
Saved DistanceMatrix to: IBD-2538/ctf-results/distance_matrix.qza
Saved SampleData[SampleTrajectory] to: IBD-2538/ctf-results/state_subject_ordination.qza
Saved FeatureData[FeatureTrajectory] to: IBD-2538/ctf-results/state_feature_ordination.qza

We will now cover the output files:

  • subject_biplot
  • state_biplot
  • distance_matrix
  • state_subject_ordination
  • state_feature_ordination

First, we will visualize the state_subject_ordination using q2-longitudinal. The input is the state_subject_ordination.qza, and the --p-individual-id-column will be subject_id, which is automatically assigned in the gemelli output.

qiime longitudinal volatility \
    --m-metadata-file IBD-2538/ctf-results/state_subject_ordination.qza \
    --p-state-column timepoint \
    --p-individual-id-column subject_id \
    --p-default-group-column ibd \
    --p-default-metric PC1 \
    --o-visualization IBD-2538/ctf-results/state_subject_ordination.qzv

Output:

Saved Visualization to: IBD-2538/ctf-results/state_subject_ordination.qzv

The y-axis in the subject trajectory is a PC axis like a conventional ordination (i.e. PCoA) and the x-axis is time.
The interpretation is also similar to a conventional ordination scatter plot – where the larger the distance is between subjects at each time point the greater the difference in their microbial communities. Here we can see that CTF can effectively show a difference between controls and Crohn’s subjects across time.

There is not a strong chnage over time in this example. However, we could explore the distance_matrix to test the differences by IBD by looking at pairwise distances with a Mixed Effects Model. How to use and evaluate the q2-longitudinal commands is covered in depth in thier tutorial here.

Now we will explore the subject_biplot which is an ordination where dots represent subjects not samples and arrows represent features (e.g. ASVs). First, we will need to aggregate the metadata by subject (i.e. collapsing the metadata of all samples from a given subject). This can be done by hand or using DataFrames in python (with pandas) or R like so:

import pandas as pd
from qiime2 import Metadata

# first we import the metdata into pandas
mf = pd.read_csv('IBD-2538/data/metadata.tsv', sep='\t',index_col=0)
# next we aggregate by subjects (i.e. 'host_subject_id') 
# and keep the first instance of 'diagnosis_full' by subject.
mf = mf.groupby('host_subject_id').agg({'ibd':'first','active_disease':'first'})
# now we save the metadata in QIIME2 format.
mf.index.name = '#SampleID'
mf.to_csv('IBD-2538/data/subject-metadata.tsv', sep='\t')

Now with the collapsed subject-metadata.tsv table we are ready to plot with emperor:

qiime emperor biplot\
    --i-biplot IBD-2538/ctf-results/subject_biplot.qza \
    --m-sample-metadata-file IBD-2538/data/subject-metadata.tsv \
    --m-feature-metadata-file IBD-2538/data/taxonomy.qza \
    --p-number-of-features 100\
    --o-visualization IBD-2538/ctf-results/subject_biplot.qzv

Output:

Saved Visualization to: IBD-2538/ctf-results/subject_biplot.qzv

From this visualization we can see that the Crohn’s subjects clearly separate from the healthy controls.

We can also see that the IBD grouping is separated entirely along the first PC (axis 1). We can now use Qurro to explore the feature loading partitions (arrows) in this biplot as a log-ratio of the original table counts. This allows us to relate these low-dimensional representations back to our original data. Additionally, log-ratios provide a nice set of data points for additional analysis such as LME models.

qiime qurro loading-plot\
    --i-table IBD-2538/data/table.qza\
    --i-ranks IBD-2538/ctf-results/subject_biplot.qza\
    --m-sample-metadata-file IBD-2538/data/metadata.tsv\
    --m-feature-metadata-file IBD-2538/data/taxonomy.qza\
    --o-visualization IBD-2538/ctf-results/qurro.qzv

Output:

Saved Visualization to: IBD-2538/ctf-results/qurro.qzv

From the Qurro output qurro.qzv we will simply choose the PC1 loadings above and below zero as the numerator (red ranks) and denominator (blue ranks) to create a log-ratio that differentiates the samples by IBD status. Log-ratios can also be chosen by taxonomy or sequence identifiers (see the Qurro tutorials here for more information). We can plot this log-ratio in Qurro with the x-axis as time and the color as IBD, which clearly shows nice separation between phenotypes.

We can further explore these phenotype differences by exporting the sample_plot_data.tsv from Qurro (marked in a orange box above) which will provide the selected log-ratio values for each sample. We can then merge this sample_plot_data with our sample metadata in python or R.

Note: Qurro will have an option to export all of the metadata or only the log-ratio data soon.

import pandas as pd

# import log-ratio data
metadata_one = pd.read_csv('IBD-2538/data/metadata.tsv',
                           sep='\t', index_col=0)
# import rest of the metadata
metadata_two = pd.read_csv('IBD-2538/ctf-results/sample_plot_data.tsv',
                           sep='\t', index_col=0)[['Current_Natural_Log_Ratio']]
# merge the data
log_ratio_metdata = pd.concat([metadata_two, metadata_one], axis=1)
# ensure no duplicate columns
log_ratio_metdata = log_ratio_metdata.dropna(subset=['Current_Natural_Log_Ratio'])
# export in QIIME2 format
log_ratio_metdata.index.name = '#SampleID'
log_ratio_metdata.to_csv('IBD-2538/ctf-results/merged_sample_plot_data.tsv', sep='\t')

As you can see above the metadata now has the added column of Current_Natural_Log_Ratio from Qurro. So now we will continue to explore this log-ratio by first plotting it explicitly over time with q2-longitudinal.

qiime longitudinal volatility \
    --m-metadata-file IBD-2538/ctf-results/merged_sample_plot_data.tsv\
    --p-state-column timepoint \
    --p-individual-id-column host_subject_id \
    --p-default-group-column ibd \
    --p-default-metric Current_Natural_Log_Ratio \
    --o-visualization IBD-2538/ctf-results/log_ratio_plot.qzv

Output:

Saved Visualization to: IBD-2538/ctf-results/log_ratio_plot.qzv

This demonstrates that we can recreate the separation by IBD that we saw in both the subject_biplot & state_subject_ordination, allowing us to associate specific taxa (in the numerator or denominator) with a particular phenotype.

We can test the statistical power of this log-ratio to differentiate samples by IBD status using a linear mixed effects (LME) through q2-longitudinal.

qiime longitudinal linear-mixed-effects\
    --m-metadata-file IBD-2538/ctf-results/merged_sample_plot_data.tsv\
    --p-state-column timepoint \
    --p-individual-id-column host_subject_id \
    --p-group-columns ibd \
    --p-metric Current_Natural_Log_Ratio \
    --o-visualization IBD-2538/ctf-results/lme_log_ratio.qzv

Output:

Saved Visualization to: IBD-2538/ctf-results/lme_log_ratio.qzv

From this LME model we can see that indeed the IBD grouping is significant across time.

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