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Count number of genes by method

genes_per_method.Rd

Returns the number of unique genes or SNPs identified by each method (the most concise categorisation of lists).

Usage

genes_per_method(data_study, data_genes)

Arguments

data_study

Data frame in the format of ARDSMAICR::data_study

data_genes

Data frame in the format of ARDSMAICR::data_genes

Value

A tibble

Details

Input columns for data_study should be:

  • id - Integer 1 to n studies - dbl

  • First_author - First author family name - chr

  • Article_title - Article title - chr

  • Year - Year of publication - dbl

  • Journal - Journal - chr

  • DOI - Digital object identifier - dbl

  • PMID - PubMed ID - dbl

  • uID - Unique ID. Format is First_Author Year PMID - chr

  • Method - Study method e.g., "GWAS" - chr

  • Technology - Technology used e.g., "Microarray" - chr

  • Tissue - Tissue type sampled e.g., "BALF" - chr

  • Cell - Cell type sampled e.g., "Neutrophils" - chr

  • Focus Study focus e.g., "Susceptibility" - chr

  • ARDS_pts - Total number of patients with ARDS included in study - dbl

  • ARDS_definition - Definition of ARDS used in study - chr

  • List_available - Was the gene list associated with the study retrievable - lgl

Input columns for data_genes should be (this is the standard output of the MAIC algorithm):

  • gene - HGNC gene symbol - chr

  • 1...

  • uID - Study unique identifier. Column contains study specific gene score - dbl

  • n...

  • maic_score - MAIC score for gene - dbl

  • contributors - Studies contributing to MAIC score by method - chr

Examples

if (FALSE) {
if(interactive()){
 genes_per_method(ARDSMAICR::data_study, ARDSMAICR::data_genes)
 }
}