rnaSeqData | R Documentation |
Get normalized RNA-seq expression data
rnaSeqData(
cancerStudy,
geneNames,
zscore = c("all samples", "diploid samples")
)
cancerStudy |
|
geneNames |
|
zscore |
|
Examples of cancer studies with different mRNA data types:
RNA-seq v2: gbm_tcga_pub2013_rna_seq_v2_mrna
.
RNA-seq v1: nbl_target_2018_pub_rna_seq_mrna
.
Microarray: gbm_tcga_pub_mrna
.
SummarizedExperiment
.
Samples (e.g. patient tumors) in the columns and genes in the rows.
cBioPortal currently generates two z-score profiles using two different base populations:
Distribution based on diploid samples only: The expression distribution
for unaltered copies of the gene is estimated by calculating the mean and
variance of the expression values for samples in which the gene is diploid
(i.e. value is "0" as reported by discrete CNA data). We call this the
unaltered distribution. If the gene has no diploid samples, then its
normalized expression is reported as NA
.
Distribution based on all samples: The expression distribution of the
gene is estimated by calculating the mean and variance of all samples with
expression values (excludes zero's and non-numeric values like NA
, NULL
or NaN
). If the gene has samples whose expression values are all zeros or
non-numeric, then its normalized expression is reported as NA
.
Otherwise for every sample, the gene's normalized expression for both the profiles is reported as:
(r - mu)/sigma
where r
is the raw expression value, and mu
and sigma
are the mean and
standard deviation of the base population, respectively.
See also:
https://github.com/cBioPortal/cbioportal/blob/master/docs/ Z-Score-normalization-script.md
Updated 2021-09-03.
geneNames <- c("MYC", "TP53")
## ACC TCGA 2018 ====
cancerStudy <- "acc_tcga_pan_can_atlas_2018"
x <- rnaSeqData(cancerStudy = cancerStudy, geneNames = geneNames)
print(x)
## CCLE Broad 2019 ====
cancerStudy <- "ccle_broad_2019"
x <- rnaSeqData(cancerStudy = cancerStudy, geneNames = geneNames)
print(x)
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