In BioinformaticsFMRP/TCGAbiolinks: TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data

knitr::opts_chunk$set(dpi = 300)
knitr::opts_chunk$set(cache = FALSE)

library(TCGAbiolinks)

library(SummarizedExperiment)
library(dplyr)
library(DT)

Calculate stemness score with TCGAanalyze_Stemness

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If you use this function please also cite:

Malta TM, Sokolov A, Gentles AJ, et al. Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell. 2018;173(2):338-354.e15. (doi:10.1016/j.cell.2018.03.034)

Data

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The input data are: - a matrix (samples as columns, Gene names as rows) - the signature to calculate the correlation score.

Possible scores are:

• SC_PCBC_stemSig - Stemness Score
• DE_PCBC_stemSig - endoderm score
• EB_PCBC_stemSig - embryoid bodies score
• ECTO_PCBC_stemSig - ectoderm score
• MESO_PCBC_stemSig - mesoderm score

Function

# Selecting TCGA breast cancer (10 samples) for example stored in dataBRCA
dataNorm <- TCGAanalyze_Normalization(
    tabDF = dataBRCA, 
    geneInfo =  geneInfo
)

# quantile filter of genes
dataFilt <- TCGAanalyze_Filtering(
  tabDF = dataNorm,
  method = "quantile",
  qnt.cut =  0.25
)

data(SC_PCBC_stemSig)
Stemness_score <- TCGAanalyze_Stemness(
  stemSig = SC_PCBC_stemSig,
  dataGE = dataFilt
)
data(ECTO_PCBC_stemSig)
ECTO_score <- TCGAanalyze_Stemness(
  stemSig = ECTO_PCBC_stemSig,
  dataGE = dataFilt,
  colname.score = "ECTO_PCBC_stem_score"
)

data(MESO_PCBC_stemSig)
MESO_score <- TCGAanalyze_Stemness(
  stemSig = MESO_PCBC_stemSig,
  dataGE = dataFilt,
  colname.score = "MESO_PCBC_stem_score"
)

Output

head(Stemness_score)
head(ECTO_score)
head(MESO_score)

BioinformaticsFMRP/TCGAbiolinks documentation built on April 12, 2024, 2:08 a.m.