CELLector.createAllSignatures: Derive signatures underlying the cancer patients' subtypes in...

In najha/CELLector: Genomics guided selection of cancer cell lines

Derive signatures underlying the cancer patients' subtypes in the CELLector search space

Description

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.

Usage

CELLector.createAllSignatures(NavTab)

Arguments

NavTab

A CELLector searching space encoded as binary tree in a navigable table, as returned by the CELLector.Build_Search_Space function.

Value

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.

Author(s)

Hanna Najgebauer and Francesco Iorio

References

[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

See Also

CELLector.Build_Search_Space

Examples

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)

najha/CELLector documentation built on Feb. 8, 2023, 5:35 a.m.