Tweedieverse

Tweedieverse is an R package to test associations between omics data and phenotypes (metadata). We keep the source code under Tweedieverse source code. Here we provide examples and tutorials on configuring the runs, interpreting results, and combining figures for scientific reports and manuscripts.

Citation

To cite Tweedieverse in publications, please use:

Mallick, H, Chatterjee, S, Chowdhury, S, Chatterjee, S, Rahnavard, A, Hicks, SC. Differential expression of single-cell RNA-seq data using Tweedie models. Statistics in Medicine. 2022; 41( 18): 3492- 3510. doi:10.1002/sim.9430

To cite the Tweedieverse software, please use:

Mallick, H; Rahnavard, A (2021). Tweedieverse - A Unified Statistical Framework for Differential Analysis of Multi-omics Data. R package, https://github.com/himelmallick/Tweedieverse.

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Contents

• Applications
  • Biomarkers and Pathways for COVID-19

Demo run code

library(Tweedieverse)
# use your path
setwd("~/path-to-your-working-directory/")

metadata <- read.table(
  'data//metadata.txt',
  sep = '\t',
  header = TRUE,
  fill = FALSE,
  comment.char = "" ,
  check.names = FALSE,
  row.names = 1
)

metabolites <- read.delim(
  'data/metabolites.txt',
  sep = '\t',
  header = TRUE,
  fill = T,
  comment.char = "" ,
  check.names = F,
  row.names = 1
)

### Run Tweedieverse 

#  imputation strategy 
metabolites[is.na(metabolites)] <- 0 #min(metabolites, na.rm = T)/2.0

Tweedieverse::Tweedieverse(metabolites, 
                           metadata, 
                           'analysis/my_meatbolites_Tweedieverse',
                           max_significance = 0.1,
                           plot_heatmap = T,
                           plot_scatter = T,
                           standardize = F)