GitHub - davemcg/geyser (original) (raw)

geyser

Gene Expression displaY of SummarizedExperiment in R

Shiny-based app that uses the SummarizedExperiment data structure to flexibly plot gene (or transcript) expression by user-led sample grouping(s).

Solves the problem of "uh, here is a bunch of RNAseq data and I just want to quickly look at some gene expression patterns across arbitrary sample metadata labels without writing code"

Install

# if needed: install.packages('remotes') 
remotes::install_github("davemcg/geyser")

Quick Start

Note: Use tiny included SE object just to confirm the app functions for you

library(geyser)
load(system.file('extdata/tiny_rse.Rdata', package = 'geyser'))
geyser(tiny_rse,'Test RSE')

Use recount3 to make a SE object

library(recount3)
library(geyser)
library(dplyr)
human_projects <- available_projects()
proj_info <- subset(
  human_projects,
  project == "SRP107937" & project_type == "data_sources"
)
rse_SRP107937 <- create_rse(proj_info)
assay(rse_SRP107937, "counts") <- transform_counts(rse_SRP107937)
# first tweak that glues the gene name onto the gene id in the row names
rownames(rse_SRP107937) <- paste0(rowData(rse_SRP107937)$gene_name, ' (', row.names(rse_SRP107937), ')')
# creates two new metadataa fields 
colData(rse_SRP107937)$tissue <- colData(rse_SRP107937)$sra.sample_title %>% stringr::str_extract(.,'PRC|PR')
colData(rse_SRP107937)$disease <- colData(rse_SRP107937)$sra.sample_title %>% stringr::str_extract(.,'AMD|Normal')
geyser::geyser(rse_SRP107937, " geyser: SRP107937")

R Pseudocode to turn your count matrix and metadata into a SE object

# YOUR METADATA ROWS NEED TO MATCH YOUR COUNT MATRIX COLUMNS
# your_count_matrix is an actual matrix (not a tibble!) with the row.names set to the gene
library(SummarizedExperiment)
rse <- SummarizedExperiment(assays = list(counts = your_count_matrix), colData = your_metadata)
colnames(rse) <- your_metadata$sample_id

Related tools

The theme between these tools is that they do A LOT OF STUFF. geyser just does one thing - shows gene expression across your samples. Which, effectively, means less energy spent trying to figure out how to get started.

• iSEE
• DEVis
• omicsViewer