Description
Utility Functions for Single-Cell RNA Sequencing Data.
Description
Analysis of single-cell RNA sequencing data can be simple and clear with the right utility functions. This package collects such functions, aiming to fulfill the following criteria: code clarity over performance (i.e. plain R code instead of C code), most important analysis steps over completeness (analysis 'by hand', not automated integration etc.), emphasis on quantitative visualization (intensity-coded color scale, etc.).
README.md
scUtils
The goal of scUtils is to collect utility functions that make single-cell RNAseq data analysis simple and understandable for anyone. At the same time, I will use it when writing my PhD thesis.
Installation
You can install the released version of scUtils from CRAN with:
install.packages("scUtils")
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("FelixTheStudent/scUtils")
Example: Single-Cell Feature Plot
Feature Plots highlight gene expression in a 2-dimensional embedding (computed e.g. with UMAP or tSNE).
library(scUtils)
# simulate some data
set.seed(100)
my_umap <- matrix(rnorm(2000, c(.1, 3)), ncol=2, dimnames = list(NULL, c("umap_1", "umap_2")))
my_expr <- rpois(1000, c(.1, 11))
feat(my_umap, my_expr)
