CELLector.createAllSignatures_Partitioned: Extract signatures underlying the cancer patients' subtypes...

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

Extract signatures underlying the cancer patients' subtypes in the partitioned CELLector search space

Description

This function takes in input the partitioned CELLector search space encoded as a navigable table in a data frame. The signature rule is already derived to defined the paritioned space and extracted here in the same format of CELLector.createAllSignatures

Usage

CELLector.createAllSignatures_Partitioned(NavTab)

Arguments

NavTab

A CELLector searching space partitioned encoded as a navigable table in data frame format, as returned by the CELLector.Build_Search_Space_Partitioned function.

Value

A list with two vectors of strings and two numerical vector. Each element of the first two vectors represent a signature of cancer functional events (CFEs, defined in [1]) corresponding to a group in the CELLector searching space partitioned. This is expressed as a logic formula (rule), which a cancer patient's genome must satisfy in order to be included in the group 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 group represented by the signatures. The fourth vector (SC) contains the number of cancer patients from the cohort belonging to the group.

Author(s)

Lucia Trastulla 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/10.1016/j.cels.2020.04.007

See Also

CELLector.Build_Search_Space_Partitioned CELLector.createAllSignatures

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_p <- CELLector.Build_Search_Space_Partitioned(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_Partitioned(CSS_p$partitioned)
data.frame(Signatures = Signatures$S,'SubType Size'=Signatures$STS, 'SubType Count'=Signatures$SC)

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