Computes a set of summary features for the cohort, in the form of a matrix. The fits must be available inside the cohort to retrieve some of these features, which are:
the matrix of driver events with mean CCF or binary value;
the matrix of the drivers occurrence acrross all the cohort;
the matrix of the clonal drivers occurrence acrross all the cohort;
the matrix of the subclonal drivers occurrence acrross all the cohort;
the matrix of the occurrence of all evolutionary trajectories across patients
The function returns a named list, so that names can be used to access the matrices.
get_features(x, patients = x$patients)
| x | A |
|---|---|
| patients | A vector of patient ids for which the features are extracted. |
A list of matrices.
Other Getters:
CCF_clusters(),
CCF(),
Clonal_cluster(),
Data(),
Drivers(),
ITransfer(),
Phylo(),
Samples(),
Subclonal(),
Truncal()
# Data released in the 'evoverse.datasets' data('TRACERx_NEJM_2017_REVOLVER', package = 'evoverse.datasets') features = get_features(TRACERx_NEJM_2017_REVOLVER) print(names(features))#> [1] "Matrix_mean_CCF" "Matrix_drivers" #> [3] "Matrix_clonal_drivers" "Matrix_subclonal_drivers" #> [5] "Matrix_trajectories"#> $Matrix_mean_CCF #> # A tibble: 99 x 80 #> patientID APC ARHGAP35 ARID1B ARID2 ASXL1 ATM BAP1 BRAF CBLB CCND1 #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 CRUK0001 0 0.38 0 0 0 0 0 0 0 0 #> 2 CRUK0002 0 0 0 0 0 0 0 0 0 0 #> 3 CRUK0003 0 0 0 0 0 0 0 0 0 0 #> 4 CRUK0004 0 0 0 0 0 0 0 0 0 0 #> 5 CRUK0005 0 0 0 0 0 0 0 0.99 0 0 #> 6 CRUK0006 0 0 0 0 0 0 0 0 0 0 #> 7 CRUK0007 0 0 0 0 0 0 0 0 0 0 #> 8 CRUK0008 0 0 0 0.43 0 0 0 0 0 0 #> 9 CRUK0009 0 0.988 0 0 0 0 0 0.988 0 0 #> 10 CRUK0010 0 0 0 0 0 0 0 0 0 0 #> # … with 89 more rows, and 69 more variables: CDKN2A <dbl>, CHEK2 <dbl>, #> # CIC <dbl>, CMTR2 <dbl>, COL2A1 <dbl>, COL5A2 <dbl>, CREBBP <dbl>, #> # CTNNB1 <dbl>, CUX1 <dbl>, CYLD <dbl>, DICER1 <dbl>, DNM2 <dbl>, EGFR <dbl>, #> # EP300 <dbl>, FANCC <dbl>, FANCM <dbl>, FAS <dbl>, FAT1 <dbl>, FBXW7 <dbl>, #> # FGFR1 <dbl>, FLT4 <dbl>, GATA3 <dbl>, IKZF1 <dbl>, KEAP1 <dbl>, #> # KMT2C <dbl>, KMT2D <dbl>, KRAS <dbl>, LATS1 <dbl>, MAP3K1 <dbl>, MET <dbl>, #> # MGA <dbl>, MLH1 <dbl>, MYC <dbl>, NCOA6 <dbl>, NCOR1 <dbl>, NF1 <dbl>, #> # NFE2L2 <dbl>, NOTCH1 <dbl>, NOTCH2 <dbl>, NRAS <dbl>, PASK <dbl>, #> # PDGFRA <dbl>, PHOX2B <dbl>, PIK3CA <dbl>, PLXNB2 <dbl>, POLE <dbl>, #> # PRDM1 <dbl>, PRF1 <dbl>, PTEN <dbl>, PTPRC <dbl>, RAD21 <dbl>, RASA1 <dbl>, #> # RB1 <dbl>, RNF43 <dbl>, SERPINB13 <dbl>, SETD2 <dbl>, SGK223 <dbl>, #> # SMAD4 <dbl>, SMARCA4 <dbl>, SOX2 <dbl>, SPEN <dbl>, STK11 <dbl>, #> # TERT <dbl>, TP53 <dbl>, TSC2 <dbl>, U2AF1 <dbl>, UBR5 <dbl>, WRN <dbl>, #> # WT1 <dbl> #> #> $Matrix_drivers #> # A tibble: 99 x 80 #> patientID APC ARHGAP35 ARID1B ARID2 ASXL1 ATM BAP1 BRAF CBLB CCND1 #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 CRUK0001 0 1 0 0 0 0 0 0 0 0 #> 2 CRUK0002 0 0 0 0 0 0 0 0 0 0 #> 3 CRUK0003 0 0 0 0 0 0 0 0 0 0 #> 4 CRUK0004 0 0 0 0 0 0 0 0 0 0 #> 5 CRUK0005 0 0 0 0 0 0 0 1 0 0 #> 6 CRUK0006 0 0 0 0 0 0 0 0 0 0 #> 7 CRUK0007 0 0 0 0 0 0 0 0 0 0 #> 8 CRUK0008 0 0 0 1 0 0 0 0 0 0 #> 9 CRUK0009 0 1 0 0 0 0 0 1 0 0 #> 10 CRUK0010 0 0 0 0 0 0 0 0 0 0 #> # … with 89 more rows, and 69 more variables: CDKN2A <dbl>, CHEK2 <dbl>, #> # CIC <dbl>, CMTR2 <dbl>, COL2A1 <dbl>, COL5A2 <dbl>, CREBBP <dbl>, #> # CTNNB1 <dbl>, CUX1 <dbl>, CYLD <dbl>, DICER1 <dbl>, DNM2 <dbl>, EGFR <dbl>, #> # EP300 <dbl>, FANCC <dbl>, FANCM <dbl>, FAS <dbl>, FAT1 <dbl>, FBXW7 <dbl>, #> # FGFR1 <dbl>, FLT4 <dbl>, GATA3 <dbl>, IKZF1 <dbl>, KEAP1 <dbl>, #> # KMT2C <dbl>, KMT2D <dbl>, KRAS <dbl>, LATS1 <dbl>, MAP3K1 <dbl>, MET <dbl>, #> # MGA <dbl>, MLH1 <dbl>, MYC <dbl>, NCOA6 <dbl>, NCOR1 <dbl>, NF1 <dbl>, #> # NFE2L2 <dbl>, NOTCH1 <dbl>, NOTCH2 <dbl>, NRAS <dbl>, PASK <dbl>, #> # PDGFRA <dbl>, PHOX2B <dbl>, PIK3CA <dbl>, PLXNB2 <dbl>, POLE <dbl>, #> # PRDM1 <dbl>, PRF1 <dbl>, PTEN <dbl>, PTPRC <dbl>, RAD21 <dbl>, RASA1 <dbl>, #> # RB1 <dbl>, RNF43 <dbl>, SERPINB13 <dbl>, SETD2 <dbl>, SGK223 <dbl>, #> # SMAD4 <dbl>, SMARCA4 <dbl>, SOX2 <dbl>, SPEN <dbl>, STK11 <dbl>, #> # TERT <dbl>, TP53 <dbl>, TSC2 <dbl>, U2AF1 <dbl>, UBR5 <dbl>, WRN <dbl>, #> # WT1 <dbl> #> #> $Matrix_clonal_drivers #> # A tibble: 99 x 80 #> patientID APC ARHGAP35 ARID1B ARID2 ASXL1 ATM BAP1 BRAF CBLB CCND1 #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 CRUK0001 0 0 0 0 0 0 0 0 0 0 #> 2 CRUK0002 0 0 0 0 0 0 0 0 0 0 #> 3 CRUK0003 0 0 0 0 0 0 0 0 0 0 #> 4 CRUK0004 0 0 0 0 0 0 0 0 0 0 #> 5 CRUK0005 0 0 0 0 0 0 0 1 0 0 #> 6 CRUK0006 0 0 0 0 0 0 0 0 0 0 #> 7 CRUK0007 0 0 0 0 0 0 0 0 0 0 #> 8 CRUK0008 0 0 0 0 0 0 0 0 0 0 #> 9 CRUK0009 0 1 0 0 0 0 0 1 0 0 #> 10 CRUK0010 0 0 0 0 0 0 0 0 0 0 #> # … with 89 more rows, and 69 more variables: CDKN2A <dbl>, CHEK2 <dbl>, #> # CIC <dbl>, CMTR2 <dbl>, COL2A1 <dbl>, COL5A2 <dbl>, CREBBP <dbl>, #> # CTNNB1 <dbl>, CUX1 <dbl>, CYLD <dbl>, DICER1 <dbl>, DNM2 <dbl>, EGFR <dbl>, #> # EP300 <dbl>, FANCC <dbl>, FANCM <dbl>, FAS <dbl>, FAT1 <dbl>, FBXW7 <dbl>, #> # FGFR1 <dbl>, FLT4 <dbl>, GATA3 <dbl>, IKZF1 <dbl>, KEAP1 <dbl>, #> # KMT2C <dbl>, KMT2D <dbl>, KRAS <dbl>, LATS1 <dbl>, MAP3K1 <dbl>, MET <dbl>, #> # MGA <dbl>, MLH1 <dbl>, MYC <dbl>, NCOA6 <dbl>, NCOR1 <dbl>, NF1 <dbl>, #> # NFE2L2 <dbl>, NOTCH1 <dbl>, NOTCH2 <dbl>, NRAS <dbl>, PASK <dbl>, #> # PDGFRA <dbl>, PHOX2B <dbl>, PIK3CA <dbl>, PLXNB2 <dbl>, POLE <dbl>, #> # PRDM1 <dbl>, PRF1 <dbl>, PTEN <dbl>, PTPRC <dbl>, RAD21 <dbl>, RASA1 <dbl>, #> # RB1 <dbl>, RNF43 <dbl>, SERPINB13 <dbl>, SETD2 <dbl>, SGK223 <dbl>, #> # SMAD4 <dbl>, SMARCA4 <dbl>, SOX2 <dbl>, SPEN <dbl>, STK11 <dbl>, #> # TERT <dbl>, TP53 <dbl>, TSC2 <dbl>, U2AF1 <dbl>, UBR5 <dbl>, WRN <dbl>, #> # WT1 <dbl> #> #> $Matrix_subclonal_drivers #> # A tibble: 99 x 80 #> patientID APC ARHGAP35 ARID1B ARID2 ASXL1 ATM BAP1 BRAF CBLB CCND1 #> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> #> 1 CRUK0001 0 1 0 0 0 0 0 0 0 0 #> 2 CRUK0002 0 0 0 0 0 0 0 0 0 0 #> 3 CRUK0003 0 0 0 0 0 0 0 0 0 0 #> 4 CRUK0004 0 0 0 0 0 0 0 0 0 0 #> 5 CRUK0005 0 0 0 0 0 0 0 0 0 0 #> 6 CRUK0006 0 0 0 0 0 0 0 0 0 0 #> 7 CRUK0008 0 0 0 1 0 0 0 0 0 0 #> 8 CRUK0009 0 0 0 0 0 0 0 0 0 0 #> 9 CRUK0011 0 0 0 0 0 0 0 0 0 0 #> 10 CRUK0013 0 0 0 0 0 0 0 0 0 0 #> # … with 89 more rows, and 69 more variables: CDKN2A <dbl>, CHEK2 <dbl>, #> # CIC <chr>, CMTR2 <dbl>, COL2A1 <dbl>, COL5A2 <chr>, CREBBP <dbl>, #> # CTNNB1 <chr>, CUX1 <dbl>, CYLD <chr>, DICER1 <dbl>, DNM2 <chr>, EGFR <chr>, #> # EP300 <chr>, FANCC <chr>, FANCM <chr>, FAS <chr>, FAT1 <chr>, FBXW7 <dbl>, #> # FGFR1 <dbl>, FLT4 <chr>, GATA3 <dbl>, IKZF1 <chr>, KEAP1 <dbl>, #> # KMT2C <dbl>, KMT2D <chr>, KRAS <chr>, LATS1 <chr>, MAP3K1 <chr>, MET <dbl>, #> # MGA <chr>, MLH1 <chr>, MYC <dbl>, NCOA6 <chr>, NCOR1 <chr>, NF1 <chr>, #> # NFE2L2 <chr>, NOTCH1 <chr>, NOTCH2 <dbl>, NRAS <chr>, PASK <chr>, #> # PDGFRA <dbl>, PHOX2B <dbl>, PIK3CA <chr>, PLXNB2 <chr>, POLE <dbl>, #> # PRDM1 <dbl>, PRF1 <dbl>, PTEN <dbl>, PTPRC <chr>, RAD21 <dbl>, RASA1 <dbl>, #> # RB1 <chr>, RNF43 <chr>, SERPINB13 <dbl>, SETD2 <chr>, SGK223 <dbl>, #> # SMAD4 <chr>, SMARCA4 <dbl>, SOX2 <dbl>, SPEN <dbl>, STK11 <dbl>, #> # TERT <chr>, TP53 <chr>, TSC2 <dbl>, U2AF1 <dbl>, UBR5 <chr>, WRN <dbl>, #> # WT1 <dbl> #> #> $Matrix_trajectories #> # A tibble: 99 x 263 #> patientID `ARHGAP35 --> T… `ARID1B --> ASX… `ARID1B --> COL… `ARID1B --> KRA… #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 CRUK0001 0 0 0 0 #> 2 CRUK0002 0 0 0 0 #> 3 CRUK0003 0 0 0 0 #> 4 CRUK0004 0 0 0 0 #> 5 CRUK0005 0 0 0 0 #> 6 CRUK0006 0 0 0 0 #> 7 CRUK0007 0 0 0 0 #> 8 CRUK0008 0 0 0 0 #> 9 CRUK0009 1 0 0 0 #> 10 CRUK0010 0 0 0 0 #> # … with 89 more rows, and 258 more variables: ARID2 --> KRAS <dbl>, #> # ATM --> CCND1 <dbl>, ATM --> MGA <dbl>, ATM --> NCOR1 <dbl>, #> # BAP1 --> PIK3CA <dbl>, BAP1 --> RB1 <dbl>, BAP1 --> TP53 <dbl>, #> # BRAF --> TERT <dbl>, CBLB --> ARID1B <dbl>, CBLB --> DNM2 <dbl>, #> # CBLB --> LATS1 <dbl>, CBLB --> PTPRC <dbl>, CCND1 --> ARID1B <dbl>, #> # CCND1 --> FAS <dbl>, CCND1 --> IKZF1 <dbl>, CCND1 --> UBR5 <dbl>, #> # CDKN2A --> COL5A2 <dbl>, CDKN2A --> CTNNB1 <dbl>, CDKN2A --> KMT2D <dbl>, #> # CDKN2A --> NF1 <dbl>, CDKN2A --> NFE2L2 <dbl>, CDKN2A --> PTPRC <dbl>, #> # CDKN2A --> TP53 <dbl>, CMTR2 --> NRAS <dbl>, CMTR2 --> UBR5 <dbl>, #> # COL2A1 --> BAP1 <dbl>, COL2A1 --> NCOR1 <dbl>, COL5A2 --> CBLB <dbl>, #> # COL5A2 --> NCOR1 <dbl>, COL5A2 --> NFE2L2 <dbl>, CREBBP --> TP53 <dbl>, #> # CYLD --> FANCM <dbl>, DICER1 --> PLXNB2 <dbl>, DNM2 --> CBLB <dbl>, #> # DNM2 --> NFE2L2 <dbl>, EGFR --> ARHGAP35 <dbl>, EGFR --> CTNNB1 <dbl>, #> # EGFR --> RB1 <dbl>, EGFR --> TP53 <dbl>, EP300 --> CYLD <dbl>, #> # EP300 --> NF1 <dbl>, FANCC --> CYLD <dbl>, FANCM --> FAT1 <dbl>, #> # FAS --> FLT4 <dbl>, FAT1 --> ARID1B <dbl>, FAT1 --> CYLD <dbl>, #> # FAT1 --> DNM2 <dbl>, FAT1 --> KRAS <dbl>, FAT1 --> LATS1 <dbl>, #> # FAT1 --> NFE2L2 <dbl>, FAT1 --> PTPRC <dbl>, FBXW7 --> EP300 <dbl>, #> # FGFR1 --> COL5A2 <dbl>, FGFR1 --> KRAS <dbl>, FGFR1 --> NF1 <dbl>, #> # FGFR1 --> NFE2L2 <dbl>, FGFR1 --> PLXNB2 <dbl>, GL --> APC <dbl>, #> # GL --> ARHGAP35 <dbl>, GL --> ARID2 <dbl>, GL --> ASXL1 <dbl>, #> # GL --> ATM <dbl>, GL --> BAP1 <dbl>, GL --> BRAF <dbl>, GL --> CCND1 <dbl>, #> # GL --> CDKN2A <dbl>, GL --> CHEK2 <dbl>, GL --> CMTR2 <dbl>, #> # GL --> COL2A1 <dbl>, GL --> COL5A2 <dbl>, GL --> CREBBP <dbl>, #> # GL --> CTNNB1 <dbl>, GL --> CUX1 <dbl>, GL --> DICER1 <dbl>, #> # GL --> EGFR <dbl>, GL --> FANCM <dbl>, GL --> FAT1 <dbl>, #> # GL --> FBXW7 <dbl>, GL --> FGFR1 <dbl>, GL --> GATA3 <dbl>, #> # GL --> KEAP1 <dbl>, GL --> KMT2C <dbl>, GL --> KMT2D <dbl>, #> # GL --> KRAS <dbl>, GL --> LATS1 <dbl>, GL --> MAP3K1 <dbl>, #> # GL --> MET <dbl>, GL --> MGA <dbl>, GL --> MYC <dbl>, GL --> NCOA6 <dbl>, #> # GL --> NF1 <dbl>, GL --> NOTCH2 <dbl>, GL --> NRAS <dbl>, #> # GL --> PASK <dbl>, GL --> PDGFRA <dbl>, GL --> PHOX2B <dbl>, #> # GL --> PIK3CA <dbl>, GL --> POLE <dbl>, GL --> PRDM1 <dbl>, #> # GL --> PRF1 <dbl>, … #>