CELLector.buildModelMatrix_Partitioned: Mapping cell lines on the partitioned CELLector searching...

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

Mapping cell lines on the partitioned CELLector searching space

Description

This function maps cell line on the groups identified by the partitioned CELLector searching space, based on the collective presence/absence of the signatures of cancer functional events underlying these groups. Groups lacking representative cell lines are not considered and the rownames in the output matches the numerical id (Idx) in partitioned CELLector table.

Usage

CELLector.buildModelMatrix_Partitioned(Sigs, dataset, searchSpace)

Arguments

Sigs	A vector of string, in which each element represents a signature of cancer functional events (CFEs, defined in [1]) corresponding to a group in the partitioned 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 group under consideration. This vector is outputted by the CELLector.createAllSignatures_Partitioned function starting from a partitioned CELLector searching space (created by the CELLector.Build_Search_Space_Partitioned) function
dataset	A data frame in which the first two columns contain the COSMIC [2] identiefiers and names of cell lines (one per row), respectively, and then binary entries indicating the status of each CFEs (one per column) across cell lines. The format is the same of the entries of the list in the built-in CELLector.CellLine.BEMs object.
searchSpace	Partitioned output of CELLector searching space encoded as table and returned by the CELLector.Build_Search_Space_Partitioned function

Value

A named binary matrix with groups numerical identifiers on the rows, cell line names on the column and entries specifiyng whether the cell line in the column is representative of the subtype on the row (based on the collective presence/absence of the corresponding signature of CFEs)

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] Forbes, S. A. et al. COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res. 43, D805–11 (2015).

See Also

CELLector.createAllSignatures_Partitioned,

CELLector.Build_Search_Space_Partitioned,

CELLector.CellLine.BEMs

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)

### take all the signatures from the searching space
Signatures <- CELLector.createAllSignatures_Partitioned(CSS_p$partitioned)

### mapping colorectal cancer cell lines onto the CELLector searching space
ModelMat <- CELLector.buildModelMatrix_Partitioned(Signatures$ES,CELLlineData,CSS_p$partitioned)
head(ModelMat)

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