ShannonMets

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** Blood metabolome predicts gut microbiome α-diversity in humans**

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# ShannonMets
[Blood metabolome predicts gut microbiome α-diversity in
humans](https://www.nature.com/articles/s41587-019-0233-9)
-------------------------------------------------------------------------------------------

DOI
https://doi.org/10.1038/s41587-019-0233-9

correspondence should be addressed to:  lhood@systemsbiology.org,
sgibbons@systemsbiology.org, or nathan.price@systemsbiology.org

The content is divided into two folders, one with the copies of
notebooks used to generate main figures (as well as Supplementary Figure
2, which was originally a main figure) and one with code that can be run
on the data to replicate our results.

## Sections

1. [Figure regeneration](#figuresnotebooks-folder)
2. [Reproduce Results](#replicationcode-folder)
3. [Request data](#requesting-data)
4. [Software Prerequisites](#software-prerequisites)
5. [Docker Example](#docker-example)

## FiguresNotebooks folder:

These files are for regenerating the main figures from the paper.
In order to run all these files, you will need to [request data](#requesting-data).

**LASSO_RIDGE_METABOLOMICS_ANALYSIS.ipynb** - code used to generate
analysis presented in figures 1 and 6

**FIGURE_2.ipynb** -  code to generate Figure 2

**HEATMAPS_FIGURE3_SUPP_FIGURE2_SUPP_FIGURE6.ipynb** -  Code to generate
Figure 3, Supplementary figures 2 & 6

**Regression_analysis_Figure_4and5.ipynb** - code used to generate
analysis presented in figures 4 and 5

**RF_Classification_Mets_and_Clinical_Labs.ipynb**  - code used to
generate analysis in figure 6 and supplementary figure 3

**Clinical_Labs_Proteomics_Regression_Analysis** - code used to generate
analysis in figure 6C, performance of clinical labs and proteomics in
predicting Shannon diversity in the discovery and validation cohorts

**Validation_compositional_microbiome-3 (1).csv**   microbiome -
composition median abundance data for validation cohort

**corr_df (1).csv** -   correlation coefficients between metabolites and
microbiome in discovery cohort

**corr_df_validation-2 (1).csv** -  correlation coefficients between
metabolites and microbiome in validation cohort

**corr_pval (1).csv**   - p-values of correlation between metabolites
and microbiome in discovery cohort

**corr_pval_validation-2 (1).csv** -    p-values of correlation between
metabolites and microbiome in validation cohort

**discovery_medians.csv** - microbiome composition median abundance data
for discovery cohort

## ReplicationCode folder:

The **ReplicationCode** folder contains notebooks that will recreate the analysis described in the paper.
In order to run all these files, you will need to [request data](#requesting-data).

### Metabolomics_Shannon.ipynb

Code used to predict Shannon diversity, as well as PD whole tree and
Chao1, from plasma metabolomics. The code generates an R2 and extracts
mean beta-coefficients from each of the ten cross-validations. RUN this
code before proceeding to the Classification_Analysis or OLS_Regression,
since these codes rely on .CSV files with the LASSO identified
metabolites.

Run order: 1

#### Needed input files: 
  - second_genome_2.csv 
  - data_discovery.csv
  
#### Generated output files: 
  - _40_coefs.csv
  - top_11_mets.csv 
  - coeff_validation.csv

### CL_P_Shannon.ipynb

Code used to predict Shannon diversity from proteomics and Clinical labs
across the discovery and validation cohorts. The code also calculates an
R2 for different combinations of omics data.

Run order - independent

#### Needed input files
  - chemistries_git.csv
  - chemistries_val_git.csv
  - cleaned_proteomics.csv
  - proteomics_validation_impute.csv
  - data_discovery.csv
#### Generated output files
  - None

### Classification_Analysis.ipynb

Code used to classify individuals in the bottom quartile of Shannon
diversity using 11 plasma metabolites or a panel of clinical labs.

Run order: 2 (run **Metabolomics_Shannon.ipynb** first)

#### Needed input files
  - chemistries_git.csv
  - second_genome_2.csv
  - data_discovery.csv
  - top_11_mets.csv
  
#### Generated output files
  - None

### OLS_Regression.ipynb

Code used to generate Ordinary Least Square Regression models between
each metabolite and Shannon. The code depends on a list of the 40 mets
identified, which is generated using the "Metabolomics_Shannon" code.
Same code can be used to generate OLS models for chemistries and
proteomics.

Run order: 2  (run **Metabolomics_Shannon.ipynb** first)

#### Needed input files: 
  - \_40_coefs.csv
  - data_discovery.csv

#### Generated output files
  - supplementary_table_1.csv

## Requesting data

Qualified researchers can access the above deidentified input files
for research purposes. Requests should be sent to nathan.price@systemsbiology.org.

## Software Prerequisites

After receiving the files from above, unzip the files and copy the csvs
directly into the ReplicationCode folder. You should then start a
Jupyter notebook server on your machine with a Python 3 kernel.

In order to run the code you will need to have installed recent versions
(Python 3)
of:
- seaborn
- scipy
- numpy
- sklearn
- matplotlib
- statsmodels

## Docker Example

If you want to use **Docker**, the [Jupyter scipy docker
stack](https://jupyter-docker-stacks.readthedocs.io/en/latest/index.html)
has all prerequisites installed.

Note: This assumes you have requested and received the data files (see **Accessing data files** ) and unzipped them to current folder.

**Example workflow:**

```bash
git clone https://github.com/PriceLab/ShannonMets.git .
cp NBT_data_files/*.csv ShannonMets/ReplicationCode/
docker run -p 8888:8888  --user $(id -u):$(id -g) --group-add users -v "$PWD":/home/jovyan/work
jupyter/scipy-notebook:1386e2046833
```

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