knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
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.
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")
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)
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