CELLector.createAllSignatures | R Documentation |
This function takes in input the CELLector search space encoded as a binary tree in a navigable table. Then, for each individual path (from the root to a node) of this tree it derives a rule (signature), represented as a logic AND of multiple terms (or their negation), one per each node in the path. Negations are added when right branches are encountered.
CELLector.createAllSignatures(NavTab)
NavTab |
A CELLector searching space encoded as binary tree in a navigable table, as returned by the |
A list with two vectors of strings and a numerical vector. Each element of the first two vectors represent a signature of cancer functional events (CFEs, defined in [1]) corresponding to a node in the CELLector searching space. This is expressed as a logic formula (rule), which a cancer patient's genome must satisfy in order to be included in the sub-population represented by the node under consideration. The first vector (S
) contains decoded signatures, i.e. where the CFEs involving copy number alterations are represented by a genomic loci and contained cancer driver genes. The second vector (ES
) contains signatures of CFEs as they are represented in the binary event matrix containing the patients genomic data used to build the CELLector searching space. Further deatils are provided in [2]. The third vector (STS
) contains the percentage of cancer patients belonging to the subtype represented by the signatures.
Hanna Najgebauer and Francesco Iorio
[1] Iorio, F. et al. A Landscape of Pharmacogenomic Interactions in Cancer. Cell 166, 740–754 (2016).
[2] Najgebauer, H. et al. Genomics Guided Selection of Cancer in vitro Models.
https://doi.org/10.1101/275032
CELLector.Build_Search_Space
data(CELLector.PrimTum.BEMs)
data(CELLector.Pathway_CFEs)
data(CELLector.CFEs.CNAid_mapping)
data(CELLector.CFEs.CNAid_decode)
data(CELLector.HCCancerDrivers)
data(CELLector.CellLine.BEMs)
### Change the following two lines to work with a different cancer type
tumours_BEM<-CELLector.PrimTum.BEMs$COREAD
CELLlineData<-CELLector.CellLine.BEMs$COREAD
### unicize the sample identifiers for the tumour data
tumours_BEM<-CELLector.unicizeSamples(tumours_BEM)
### building a CELLector searching space focusing on three pathways
### and TP53 wild-type patients only
CSS<-CELLector.Build_Search_Space(ctumours = t(tumours_BEM),
verbose = FALSE,
minGlobSupp = 0.05,
cancerType = 'COREAD',
pathwayFocused = c("RAS-RAF-MEK-ERK / JNK signaling",
"PI3K-AKT-MTOR signaling",
"WNT signaling"),
pathway_CFEs = CELLector.Pathway_CFEs,
cnaIdMap = CELLector.CFEs.CNAid_mapping,
cnaIdDecode = CELLector.CFEs.CNAid_decode,
cdg = CELLector.HCCancerDrivers,
subCohortDefinition='TP53',
NegativeDefinition=TRUE)
### derive signatures from searching space
Signatures <- CELLector.createAllSignatures(CSS$navTable)
data.frame(Signatures = Signatures$S,'SubType Size'=Signatures$STS)
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