s2_feature_reduction: s2 feature reduction

In galelab/GaleGEAnalysis: What the Package Does (One Line, Title Case)

Description

This function does feature reduction and visualization on normalized voom counts

Usage

s2_feature_reduction(
  countfile = "./s1_norm_raw_counts_results/1.norm_matrix.txt",
  targetfile = "./p1_modified_count_matrix_results/targets_mod.csv",
  target_columns = c(2, 5),
  figres = 100,
  pca = TRUE,
  UMAP = TRUE,
  tsne = TRUE,
  base_file_name = "vnorm.png"
)

Arguments

countfile	normalized counts table (generally should have been generated by first step s1_normalize_raw_counts, but also this function can be run on raw cout file).
targetfile	target file.
target_columns	columns from the target file to label samples on the pca/umap plots (has to be 2)
figres	resolution at which to output figures (default is 300).
pca	run principal component analysis (and singular vector decomposition) (default set to TRUE)
UMAP	run Uniform Manifold Aproximation Projection (default set to TRUE)
base_file_name	file name for all figures

Examples

s2_feature_reduction(countfile="./s1_norm_raw_counts_results/1.norm_matrix.txt",  targetfile="./p1_modified_count_matrix_results/target_file.csv", target_columns=c(2,5), base_file_name="vnorm.png")

galelab/GaleGEAnalysis documentation built on May 18, 2020, 7:32 a.m.