FROGSSTAT DESeq2 Visualisation

Context

Are there ASV with differential abundance between 2 conditions ? And which are they ?
To answer these questions, we perform a differential abundance analysis using DESeq2 on the phyloseq object.

The package DESeq2 provides methods to test for differential expression by use of negative binomial generalized linear models

• 1^st step: FROGSSTAT DESeq2 Preprocess
• 2^nd step : FROGSSTAT DESeq2 Visualisation

Available to analyse ASV or Function (from FROGSFUNC tools)

2^nd step: visualise the DESeq2 results with FROGSSTAT DESeq2 Visualisation

FROGSSTAT DESeq2 Visualisation Launch one Rmarkdown script to visualise the DESeq2 results.

use cases:

• for ASVs
• for functions

Command line

usage: deseq2_visualisation.py [-h] [--debug] [--version] -v VAR [-m1 MOD1]
                               [-m2 MOD2] [-pa PADJ] -a {ASV,FUNCTION} -p
                               ABUNDANCEDATA -d DDS [--ipath-over IPATH_OVER]
                               [--ipath-under IPATH_UNDER] [-o HTML]
                               [-l LOG_FILE]

Launch Rmarkdown to visualise differential abundance analysis.

optional arguments:
  -h, --help            show this help message and exit
  --debug               Keep temporary files to debug program.
  --version             show programs version number and exit
  -v VAR, --var VAR     variable that you want to test.
  -m1 MOD1, --mod1 MOD1
                        one value of the tested variable you want to compare
                        (if more than 2 value in your experiement variable
                        analyzed.)
  -m2 MOD2, --mod2 MOD2
                        second value of the tested variable you want to
                        compare.(if more than 2 value in your experiement
                        variable analyzed.)
  -pa PADJ, --padj PADJ
                        the adjusted p-value threshold to defined ASV as
                        differentially abundant. [Default: 0.05]
  -a {ASV,FUNCTION}, --analysis {ASV,FUNCTION}
                        Type of data to perform the differential analysis.
                        ASV: DESeq2 is run on the ASVs abundances table. FUNC:
                        DESeq2 is run on FROGSFUNC function abundances table
                        (frogsfunc_functions_unstrat.tsv from FROGSFUNC
                        function step).

Inputs:
  -p ABUNDANCEDATA, --abundanceData ABUNDANCEDATA
                        The path to the RData file containing the ASV/FUNCTION
                        abundances table. (result of FROGS Phyloseq Import
                        Data)
  -d DDS, --dds DDS     The path to the Rdata file containing the DESeq dds
                        object (result of FROGS DESeq2 Preprocess)

Outputs:
  --ipath-over IPATH_OVER
                        The tsv file of over abundants functions (FUNCTION
                        analysis only)
  --ipath-under IPATH_UNDER
                        The tsv file of under abundants functions (FUNCTION
                        analysis only)
  -o HTML, --html HTML  The HTML file containing the graphs. [Default:
                        DESeq2_visualisation.html]
  -l LOG_FILE, --log-file LOG_FILE
                        This output file will contain several informations on
                        executed commands.

Example of command line:

• for ASVs

./deseq2_visualisation.py \
--abundanceData  data/phyloseq_ASV.Rdata \
--analysis ASV \
--dds EnvType_DESeq_ASV.Rdata \
--var EnvType \
--mod1 SaumonFume \
--mod2 DesLardons \
--log-file deseq2_preprocess_EnvType_ASV.log  \
--html EnvType_DesLardons_SaumonFume_ASV.nb.html 

• for functions

./deseq2_visualisation.py \
--abundanceData  phyloseq_FUNC.Rdata \
--analysis FUNCTION \
--dds EnvType_DESeq_FUNC.Rdata \
--var EnvType \
--mod1 SaumonFume \
--mod2 DesLardons \
--log-file deseq2_preprocess_EnvType_FUNC.log  \
--html EnvType_DesLardons_SaumonFume_FUNC.nb.html

Galaxy

for ASV

for functions

Inputs

• Data object (data.Rdata): One phyloseq object stored in a Rdata file. This file is the result of :

  • for ASV: asv.dds.Rdata from FROGSSTAT_Phyloseq_Import_Data tool
  • for FUNCTION: DESeq2_preprocess_tool output for FUNCTION parameter.

• DESeq2 object (dds.Rdata): A DESeq2 dataset (dds) stored in Rdata file. This file is the result of FROGSSTAT_DESeq2_preprocess tool.

Outputs

HTML file: visualisation of Differential Abundance of ASV or FUNCTIONS depending on the case.

The html file contains Table, Pie Chart, MA plot, Volcano plot and Heatmap plot. If the experimental variable is qualitative, only samples corresponding to the 2 compared conditions are shown in the Heatmap. Otherwise, samples are sorted in increasing order of the experimental variable.

Table containing the differentially abundant ASVs.

Only significantly differentially abundant ASV are displayed with an adjusted p-value (previously defined threshold). p-value are adjusted using the Benjamini-Hochberg method

• log2 is used for interpret and scale reasons
• Positive values denote an increase, and negative a decrease of abundance
  • log2FC = 1 means a doubling
  • log2FC = 2 means a quadrupling
  • log2FC = -1 means a halving
  • log2FC = -2 means a quartering

Pie Chart, MA plot, Volcano plot and Heatmap plot corresponding to the differentially abundant ASVs.

Differentially abundant functions visualized with iPath 3

FUNCTION analysis only

Two TSV files for Ipath3 visualisation

For function analyses only

1. ipath_under.tsv
2. ipath_over.tsv

To visualise and explore metabolic pathways with IPATH3 website , use the two files ipath_under.tsv and ipath_over.tsv as inputs.