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ggPicrust2 Functional Analysis visualization

TASK
Typing name :  TASK.gws_ubiome.Ggpicrust2FunctionalAnalysisVisualization Brick :  gws_ubiome v Parent : 

This task permit to analyze and interpret the results of PICRUSt2 functional prediction of 16s rRNA data

  • ggPicrust2 (paper can be found here) is an R package developed explicitly for PICRUSt2 predicted functional profile.
  • ggpicrust2() integrates ko abundance which is the abundance of different gene orthologs in your microbial community generated by Picrust2 to kegg pathway abundance conversion that represents the predicted abundance of entire metabolic pathways, which are composed of multiple KO groups. It also involves annotation of pathway and differential abundance (DA) analysis in order to understand the functional potential of your microbial community at the pathway level
  • It takes PICRUSt2 original output pred_metagenome_unstrat.tsv generated using Picrust2 Functional Analysis task without reformat and a metadata file.
  • The mainstream visualization of PICRUSt2 is error_bar_plot, pca_plot and heatmap_plot. pathway_errorbar can show the relative abundance difference between groups and log2 fold change and P-values (adjusted) derived from DA results. All the p-adjusted values that you see are significantly < 0.05, but they are truncated on the graph. If you want to see these values, you can go through the daa_annotated_results table. pathway_pca() can show the difference after dimensional reduction via principal component analysis. pathway_heatmap() can visualize the patterns in PICRUSt2 output data which can be useful for identifying trends or highlighting areas of interests.

Input

ko_abundance_file
File containing the kegg orthology (KO) abundance
Metadata file
This file contain informations about the experince

Output

Metadata associated to each cell
This table stores metadata associated with each cell such as mitochondrial content , number of counts etc
pathway_pca
Show the difference after dimensional reduction via principal component analysis.

Configuration

DA_method

Differential abundance (DA) method

Type : stringAllowed values : LinDA   

Samples_column_name

Column name in metadata file containing the sample name

Type : string

Reference_column

Column name in metadata file containing the reference group

Type : string

Reference_group

Reference group level for DA

Type : string

Round_digit

Optional

Remember to click on this button whenever you observe p-adjust values higher than 0.05 in order to ensure accurate and appropriately formatted results.

Type : bool

PCA_component

Optional

Perform 3D PCA if True, 2D PCA if False.

Type : bool

Slice_start

OptionalAdvanced parameter

You can modify the slice window of the errorbar and the heatmap by modifying the slice start in order to focus on a subset of the results

Type : intDefault value : 1