vis_identifier_dim_dist: Visualize the distribution difference of samples after...

In UCSCXenaShiny: Interactive Analysis of UCSC Xena Data

Visualize the distribution difference of samples after Molecule Identifier dimension reduction analysis

Description

NOTE: the dataset must be dense matrix in UCSC Xena data hubs.

Usage

vis_identifier_dim_dist(
  dataset = NULL,
  ids = NULL,
  grp_df,
  samples = NULL,
  return.data = FALSE,
  DR_method = c("PCA", "UMAP", "tSNE"),
  add_margin = NULL,
  palette = "Set1"
)

Arguments

dataset	the dataset to obtain identifiers.
ids	the molecule identifiers.
grp_df	When dataset and id are all not NULL, it should be a data.frame with 2 columns. The first column refers to sample ID. The second column refers to groups indicated in axis X.
samples	default is NULL, can be common sample names for two datasets.
return.data	whether to reture the raw meta/matrix data (list) instead of plot
DR_method	the dimension reduction method
add_margin	the marginal plot (NULL, "density", "boxplot")
palette	the color setting of RColorBrewer

Value

a ggplot object.

Examples

library(UCSCXenaTools)
expr_dataset <- "TCGA.LUAD.sampleMap/HiSeqV2_percentile"
ids = c("TP53", "KRAS", "PTEN", "MDM2", "CDKN1A")

cli_dataset <- "TCGA.LUAD.sampleMap/LUAD_clinicalMatrix"
cli_df <- XenaGenerate(
  subset = XenaDatasets == cli_dataset
) %>%
  XenaQuery() %>%
  XenaDownload() %>%
  XenaPrepare()
grp_df = cli_df[, c("sampleID", "gender")]
vis_identifier_dim_dist(expr_dataset, ids, grp_df, DR_method="PCA")

UCSCXenaShiny documentation built on May 29, 2024, 1:10 a.m.