In Jasondd66/covid_19bioMarkers: Performs common bioinformatics analyses

knitr::opts_chunk$set(echo = TRUE, cache = FALSE, warning = FALSE, message = FALSE)

library(tidyverse); library(knitr);

Steps to reproduce the heart failure case study data

• download demographics and omics data from https://amritsingh.shinyapps.io/multiomics_HFhospitalizations/
• reduce the transcripts dataset to 5000 features (10MB to <5MB)
• delete the large trascripts file to reduce package size

# transcripts <- read.csv(here::here("inst", "extdata", "heartFailure", "transcripts.tsv"), header = TRUE, sep = "\t")
# geneSd <- apply(transcripts, 2, sd)
# write.table(transcripts[, order(geneSd, decreasing = TRUE)[1:5000]], file = here::here("inst", "extdata", "caseStudy", "mrna.tsv"), sep = "\t")

omicsData <- lapply(c("cells.tsv", "holter.tsv", "mrna.tsv", "proteins.tsv"), function(i){
  read.csv(here::here("inst", "extdata", "heartFailure", i), header = TRUE, sep = "\t")
})
names(omicsData) <- c("cells", "holter", "mrna", "proteins")

## save heart failure datasets
heartFailure <- list(
  demo = read.csv(here::here("inst", "extdata", "heartFailure", "clinical.tsv"), header = TRUE, sep = "\t"),
  omicsData = omicsData
)

Save package data

usethis::use_data(heartFailure, overwrite = TRUE)

Session Info

sessionInfo()

Jasondd66/covid_19bioMarkers documentation built on Dec. 18, 2021, 12:33 a.m.