Publication dateMar 9, 2022
Confidentiality public Public
ITS Functional Analysis Prediction Visualization
TASK
Typing name : TASK.gws_ubiome.ItsGgpicrust2FunctionalAnalysisVisualization Brick : gws_ubiome This task permit to analyze and interpret the results of PICRUSt2 functional prediction of ITS data
- ggPicrust2 (paper can be found here) is an R package developed explicitly for PICRUSt2 predicted functional profile.
- ggpicrust2() integrates MetaCyc pathway abundance which is the abundance of different gene orthologs in your microbial community generated by Picrust2
- It takes PICRUSt2 original output path_abun_unstrat.tsv.gz 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.
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Input
metacyc_abundance
File containing MetaCyc pathway abundance
Metadata file
This file contain informations about the experince
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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.
settings
Configuration
Differential abundance (DA) method
Type : stringAllowed values : LinDA Column name in metadata file containing the sample name
Type : stringColumn name in metadata file containing the reference group
Type : stringReference group level for DA
Type : stringRemember 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 : boolPerform 3D PCA if True, 2D PCA if False.
Type : bool Slice_start
Optional Advanced 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 : 1Technical bricks to reuse or customize
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