TCGAanalyze_Stemness
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)
The input data are: - a matrix (samples as columns, Gene names as rows) - the signature to calculate the correlation score.
Possible scores are:
# Selecting TCGA breast cancer (10 samples) for example stored in dataBRCA
<- TCGAanalyze_Normalization(
dataNorm tabDF = dataBRCA,
geneInfo = geneInfo
)
# quantile filter of genes
<- TCGAanalyze_Filtering(
dataFilt tabDF = dataNorm,
method = "quantile",
qnt.cut = 0.25
)
data(SC_PCBC_stemSig)
<- TCGAanalyze_Stemness(
Stemness_score stemSig = SC_PCBC_stemSig,
dataGE = dataFilt
)data(ECTO_PCBC_stemSig)
<- TCGAanalyze_Stemness(
ECTO_score stemSig = ECTO_PCBC_stemSig,
dataGE = dataFilt,
colname.score = "ECTO_PCBC_stem_score"
)
data(MESO_PCBC_stemSig)
<- TCGAanalyze_Stemness(
MESO_score stemSig = MESO_PCBC_stemSig,
dataGE = dataFilt,
colname.score = "MESO_PCBC_stem_score"
)
head(Stemness_score)
head(ECTO_score)
head(MESO_score)