Package: RareComb 1.1

RareComb: Combinatorial and Statistical Analyses of Rare Events

A custom implementation of the apriori algorithm and binomial tests to identify combinations of features (genes, variants etc) significantly enriched for simultaneous mutations/events from sparse Boolean input, see Vijay Kumar Pounraja, Santhosh Girirajan (2021). Version 1.1 includes a minor adjustment to the number of combinations to be considered for multiple testing correction. This updated version is more conservative in its approach and hence more selective. <doi:10.1101/2021.10.01.462832>.

Authors:Vijay Kumar Pounraja [aut, cre]

RareComb_1.1.tar.gz
RareComb_1.1.zip(r-4.7)RareComb_1.1.zip(r-4.6)RareComb_1.1.zip(r-4.5)
RareComb_1.1.tgz(r-4.6-any)RareComb_1.1.tgz(r-4.5-any)
RareComb_1.1.tar.gz(r-4.7-any)RareComb_1.1.tar.gz(r-4.6-any)
RareComb_1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
RareComb/json (API)

# Install 'RareComb' in R:

install.packages('RareComb', repos = c('https://vijaymp38.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

boolean_input_df - Sparse Boolean dataframe with rare variant information and a single outcome variable
boolean_input_mult_df - Sparse Boolean dataframe with rare variant information and multiple outcome variables
input_list - A list of 50 random input variables

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.70 score 1 stars 3 scripts 246 downloads 11 exports 39 dependencies

Last updated from:307ffe4553. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	178
source / vignettes	OK	163
linux-release-x86_64	OK	165
macos-release-arm64	OK	187
macos-oldrel-arm64	OK	187
windows-devel	OK	119
windows-release	OK	110
windows-oldrel	OK	116
wasm-release	OK	126

Exports:analyze_in_out_simultaneity compare_enrichment compare_enrichment_depletion compare_enrichment_modifiers compare_expected_vs_observed custom_left_join run_apriori_freqitems run_apriori_rules run_apriori_rules_inout_simult run_apriori_rules_modifiers run_apriori_w_sample_names

Dependencies:arules bit bit64 blob cachem chron cli cpp11 DBI dplyr fastmap generics glue gsubfn lattice lifecycle magrittr Matrix memoise pillar pkgconfig plyr proto purrr pwr R6 Rcpp reshape2 rlang RSQLite sqldf stringi stringr tibble tidyr tidyselect utf8 vctrs withr

Help page	Topics
Analyze relationships between rare events among multiple input and output variables	analyze_in_out_simultaneity
Sparse Boolean dataframe with rare variant information and a single outcome variable	boolean_input_df
Sparse Boolean dataframe with rare variant information and multiple outcome variables	boolean_input_mult_df
Compare the enrichment in combinations of input variables between the binary outcomes (case/control)	compare_enrichment
Compare the enrichment in combinations of input variables between the binary outcomes (case/control)	compare_enrichment_depletion
Compare the enrichment in combinations of input variables between the binary outcomes (case/control)	compare_enrichment_modifiers
Compare the observed frequencies of combinations with their expected frequencies under the assumption of independence within a single group	compare_expected_vs_observed
Perform successive left joins to fetch information about the constituent elements of the combinations	custom_left_join
A list of 50 random input variables	input_list
Generate frequent items using the apriori algorithm	run_apriori_freqitems
Generate rules using the apriori algorithm	run_apriori_rules
Generate rules using the apriori algorithm	run_apriori_rules_inout_simult
Generate rules using the apriori algorithm	run_apriori_rules_modifiers
Generate frequent items along with the names of supporting observations using the apriori algorithm	run_apriori_w_sample_names

Package: RareComb 1.1

RareComb: Combinatorial and Statistical Analyses of Rare Events

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Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)