Introduction on how to use Select package to find Evolutionary Dependencies.

The goal of select pacakge is to detect Evolutionary Dependecies between alterations/genes in cancer.
select package provides function to generate the backgorund model and other utilites functions.

Installation

You can install the development version of select from GitHub with:

# install.packages("devtools")
# devtools::install_github("CSOgroup/select")

Example

We will run select algorithm on processed LUAD dataset from TCGA provided with the package.

library(select)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following object is masked from 'package:select':
#> 
#>     select
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

Data Description & Format

The loaded data is list object which consists of
- gam: a presence absence matrix of alterations
- samples: a named vector of sample annotations
- alt: a named vector of alteration annotations

# Check the data strucutre
## Load the data provided with the package
data(luad_data, package = "select")
str(luad_data)
#> List of 3
#>  $ gam    : num [1:502, 1:659] 0 0 0 0 0 0 0 0 1 0 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : chr [1:502] "TCGA-05-4244-01" "TCGA-05-4249-01" "TCGA-05-4250-01" "TCGA-05-4382-01" ...
#>   .. ..$ : chr [1:659] "MUT.ABL1" "MUT.ACVR1B" "MUT.ACVR2A" "MUT.AKT1" ...
#>  $ alt    : Named chr [1:659] "MUT" "MUT" "MUT" "MUT" ...
#>   ..- attr(*, "names")= chr [1:659] "MUT.ABL1" "MUT.ACVR1B" "MUT.ACVR2A" "MUT.AKT1" ...
#>  $ samples: Named chr [1:502] "LUAD" "LUAD" "LUAD" "LUAD" ...
#>   ..- attr(*, "names")= chr [1:502] "TCGA-05-4244-01" "TCGA-05-4249-01" "TCGA-05-4250-01" "TCGA-05-4382-01" ...

Running select

We use the function select() which generates the background model and results.
The parameters for the functions are:
- M: the list object of GAMs & TMB
- sample.class: a named vector of samples with covariates
- alteration.class: a named vector of alteration with covariates
- many more
The function returns a dataframe.

alpi <- select::select(
  M = luad_data$gam,
  sample.class = luad_data$samples,
  alteration.class = luad_data$alt,
  folder = './',
  r.seed = 110,
  n.cores = 1,
  vetos = NULL,
  n.permut = 100,
  min.feature.support=5,
  min.feature.freq=0.001,
  remove.0.samples=TRUE,
  remove.unknown.class.samples=TRUE,
  rho = 0.1,
  lambda = 15,
  save.intermediate.files = FALSE,
  randomization.switch.threshold = 30,
  max.memory.size=100,
  calculate_APC_threshold = TRUE,
  calculate_FDR = TRUE,
  FDR.cutoff=0.1,
  verbose = TRUE
  )
#> [1] "Running SELECT... [ .// ]"
#> [1] "-> Parsing and Filtering GAM..."
#> [1] "-> Generating null model..."
#>    user  system elapsed 
#>   3.362   0.006   3.368 
#> [1] "Memory usage: 31.4 MB"
#> [1] "-> Collecting event stats on observed GAM..."
#>    user  system elapsed 
#>   0.104   0.229   0.023 
#> [1] "Memory usage: 31.6 MB"
#> [1] "-> Calculating wMI on observed GAM..."
#>    user  system elapsed 
#>   0.025   0.000   0.024 
#> [1] "Memory usage: 32.1 MB"
#> [1] "-> Collecting event stats on null model..."
#>    user  system elapsed 
#>   9.555  31.501   2.059 
#> [1] "Memory usage: 37.4 MB"
#> [1] "-> Comparing observed GAM vs null model..."
#>    user  system elapsed 
#>   1.018   0.000   1.018 
#> [1] "Memory usage: 38 MB"
#> [1] "-> Calculating wMI on null model..."
#>    user  system elapsed 
#>   1.692   0.006   1.698 
#> [1] "Memory usage: 59.8 MB"
#> [1] "-> Calculating P-value based on wMI..."
#>    user  system elapsed 
#>   0.029   0.002   0.031 
#> [1] "Memory usage: 60 MB"
#> [1] "-> Deriving average background wMI..."
#>    user  system elapsed 
#>    0.91    0.00    0.91 
#> [1] "Memory usage: 60.3 MB"
#> [1] "-> Building results table..."
#> [1] "-> Calculating APC score..."
#> [1] "Not correcting with APC as there are not enough interactions to estimate the background distribution. Uncorrected scores will be used."
#> [1] "Not correcting with APC as there are not enough interactions to estimate the background distribution. Uncorrected scores will be used."
#> [1] "Not correcting with APC as there are not enough interactions to estimate the background distribution. Uncorrected scores will be used."
#> [1] "Not correcting with APC as there are not enough interactions to estimate the background distribution. Uncorrected scores will be used."
#> [1] "Not correcting with APC as there are not enough interactions to estimate the background distribution. Uncorrected scores will be used."
#>    user  system elapsed 
#>   0.201   0.000   0.201 
#> [1] "Memory usage: 64.1 MB"
#> [1] "-> Performing wMI FDR analysis..."
#> Warning in rbind(deparse.level, ...): number of columns of result, 7, is not a
#> multiple of vector length 6 of arg 2
#>    user  system elapsed 
#>   3.819   0.024   3.843 
#> [1] "Memory usage: 64.3 MB"
#> [1] "-> Calculating APC threshold..."

Intrepreting the results

Lets look into the results
The below table explains what each coloum means.

Colnames	Meaning
SFE_1	Selected Functional Event (SFE_1)
SFE_2	Selected Functional Event (SFE_2)
name	Interaction Motif
type_1	Type of mutation of SFE_1
type_2	Type of mutation of SFE_2
int_type	Interaction Motif type
support_1	Samples mutated with SFE_1
support_2	Samples mutated with SFE_2
freq_1	Frequency of SFE_1
freq_2	Frequency of SFE_2
overlap	Co-mutation between SFE_1 and SFE_2
max_overlap	Maximum possible Co-mutation
freq_overlap	Frequency of Co-mutation
r_overlap	Background Co-mutation
r_freq_overlap	Background frequency of Co-mutation
diff_overlap	Difference of co-mutations
abs_diff_overlap	Absoulte Difference of co-mutations
direction	Interaction Type
wMI_stat	Weighted Mutual Information
wMI_p.value	P-value on Mutual Information
ME_p.value	P-value on co-mutation
E.r.wMI_stat	Background Weighted Mutual Information
MI_diff	Difference of Mutual Information
wMI_p.value_FDR	FDR
select_score_good_cancer_cell_2017_criterion_1	Cancer Cell Paper Criteria
select_score	Effect Size (select Score)

options(width=100)
# Look into dataframe
(alpi %>% filter(wMI_p.value_FDR) %>% arrange(desc(select_score))) %>% head(2) 
#>                                                                                                                  SFE_1
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493    AMP.consensus.chr14:35870717-36159897
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876 DEL.consensus.chr4:183089197-186421724
#>                                                                                                                  SFE_2
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493    AMP.consensus.chr14:37858832-38371493
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876 DEL.consensus.chr4:187186290-187647876
#>                                                                                                                                                            name
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493     AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876 DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876
#>                                                                                 type_1 type_2
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493      AMP    AMP
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876    DEL    DEL
#>                                                                                  int_type support_1
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493   AMP - AMP        57
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876 DEL - DEL        12
#>                                                                                 support_2
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493          53
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876        12
#>                                                                                     freq_1
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493   0.11656442
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876 0.02453988
#>                                                                                     freq_2 overlap
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493   0.10838446      47
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876 0.02453988      11
#>                                                                                 max_overlap
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493            53
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876          12
#>                                                                                 freq_overlap
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493      0.8867925
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876    0.9166667
#>                                                                                 r_overlap
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493       10.04
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876      0.72
#>                                                                                 r_freq_overlap
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493         0.189434
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876       0.060000
#>                                                                                 diff_overlap
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493          36.96
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876        10.28
#>                                                                                 abs_diff_overlap
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493              36.96
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876            10.28
#>                                                                                 direction  wMI_stat
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493          CO 0.4241418
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876        CO 0.3612067
#>                                                                                 wMI_p.value
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493             0
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876           0
#>                                                                                 ME_p.value
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493            0
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876          0
#>                                                                                 E.r.wMI_stat
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493    0.003527209
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876  0.016350097
#>                                                                                   MI_diff       APC
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493   0.4206146 0.4005788
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876 0.3448566 0.3413985
#>                                                                                 wMI_p.value_FDR
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493              TRUE
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876            TRUE
#>                                                                                 select_score_good_cancer_cell_2017_criterion_1
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493                                             TRUE
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876                                           TRUE
#>                                                                                 select_score
#> AMP.consensus.chr14:35870717-36159897 - AMP.consensus.chr14:37858832-38371493      0.4005788
#> DEL.consensus.chr4:183089197-186421724 - DEL.consensus.chr4:187186290-187647876    0.3413985
# Total significant Hits
alpi  %>% filter(wMI_p.value_FDR) %>% count(wMI_p.value_FDR,direction)
#>   wMI_p.value_FDR direction   n
#> 1            TRUE        CO 108
#> 2            TRUE        ME  18

Suggestion (Important)

Please go through the methods on best way to filter and use the method as mentioned in the paper.
- Mina, M., Iyer, A., Tavernari, D., Raynaud, F., & Ciriello, G. (2020). Discovering functional evolutionary dependencies in human cancers Nature Genetics, 52(11), 1198-1207.
If have queries feel free to raise an Issuse on the github.

SessionInfo

# Print the sessionInfo
sessionInfo()
#> R version 4.3.2 (2023-10-31)
#> Platform: x86_64-conda-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#> 
#> Matrix products: default
#> BLAS/LAPACK: /mnt/ndata/arvind/envs/R_4/lib/libopenblasp-r0.3.25.so;  LAPACK version 3.11.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
#>  [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
#> [10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Europe/Zurich
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] dplyr_1.1.4  select_1.6.4
#> 
#> loaded via a namespace (and not attached):
#>  [1] sass_0.4.8        utf8_1.2.4        generics_0.1.3    slam_0.1-50       stringi_1.8.3    
#>  [6] lattice_0.22-5    digest_0.6.34     magrittr_2.0.3    evaluate_0.23     grid_4.3.2       
#> [11] iterators_1.0.14  fastmap_1.1.1     foreach_1.5.2     doParallel_1.0.17 jsonlite_1.8.8   
#> [16] Matrix_1.6-5      purrr_1.0.2       fansi_1.0.6       BiRewire_3.34.0   codetools_0.2-19 
#> [21] textshaping_0.3.7 jquerylib_0.1.4   cli_3.6.2         rlang_1.1.3       cachem_1.0.8     
#> [26] yaml_2.3.8        Rtsne_0.17        tools_4.3.2       parallel_4.3.2    memoise_2.0.1    
#> [31] vctrs_0.6.5       R6_2.5.1          matrixStats_1.2.0 lifecycle_1.0.4   stringr_1.5.1    
#> [36] fs_1.6.3          ragg_1.2.7        pkgconfig_2.0.3   desc_1.4.3        pkgdown_2.0.7    
#> [41] bslib_0.6.1       pillar_1.9.0      glue_1.7.0        Rcpp_1.0.12       systemfonts_1.0.5
#> [46] xfun_0.41         tibble_3.2.1      tidyselect_1.2.0  knitr_1.45        htmltools_0.5.7  
#> [51] igraph_1.6.0      rmarkdown_2.25    compiler_4.3.2

Arvind Iyer

2024-11-26

Installation

Example

Data Description & Format

Running select

Intrepreting the results

Suggestion (Important)

SessionInfo