vis_identifier_dim_dist {UCSCXenaShiny} | R Documentation |
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
|
samples |
default is |
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")
[Package UCSCXenaShiny version 2.1.0 Index]