The goal of rinfino is to provide helper-functions and (eventually) an interface to infino. Infino is a computational biology tool that estimates the composition of immune infiltrate in a bulk-biopsy sample given RNAseq data using a Bayesian hierarchical mixture model.
You can install rinfino from github with:
# install.packages("devtools")
devtools::install_github("hammerlab/rinfino")Since this package makes use of Bioconductor packages, you may prefer to use biocLite to install from github:
# source("https://bioconductor.org/biocLite.R")
biocLite('hammerlab/rinfino')Load an example dataset, filter to genes that are expressed in at least one sample & run PCA:
library(dplyr)
library(rinfino)
data("rcctils_expression")
pca_results <-
rcctils_expression %>%
filter_expdata(fun = function(x) {max(x)>0}) %>%
run_pca(use_ggplot=T)
Alternatively, you might want to load data from multiple sources (say, TCGA & a sample of isolated tils), filter to marker genes & run combat:
library(dplyr)
library(rinfino)
path_to_rcctils <- system.file("testdata", "rcctils_expression_matrix.tsv.gz", package = "rinfino")
path_to_tcgaexp <- system.file("testdata", "tcga_expression_matrix.tsv.gz", package = "rinfino")
combat_results <-
load_all_expdata(c(path_to_rcctils, path_to_tcgaexp), batch = c('rcctils', 'tcgaexp')) %>%
filter_expdata() %>% ## filter to expressed genes
run_pca(use_ggplot=F) %>% ## run_pca
filter_genes() %>% ## filter to marker genes
run_combat()
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