im_syng_tcga: Calculation and statistical assessment of synergistic...
In korkutlab/imogene: : Immuno-oncology gene expression associations

Description Usage Arguments Details Value Examples

Takes a list of Tumor-intrinsic pathway(TIP) genes and returns their combinatorial association with immune checkpoint(ICP) genes by evaluating their synergistic impact on immune-associated phenotypes(IAP) using RNASeq2GeneNorm expressions as provided by curatedTCGAData in a selected disease cohort.

im_syng_tcga(
  onco_gene,
  icp_gene,
  cohort,
  select_iap,
  method,
  specificity,
  N_iteration_specificity,
  sensitivity,
  N_iteration_sensitivity,
  sample_list
)

`onco_gene`	A character vector of gene Hugo symbols.
`icp_gene`	An optional character vector of immune checkpoint gene Hugo symbols.
`cohort`	A list of TCGA diseases
`select_iap`	An optional character vector or numeric matrix or data.frame.
`method`	A character string indicating which synergy score to be used. one of "max" or "independence". Default is "max".
`specificity`	An optional logical indicating if specificity analysis should be done. Default is FALSE.
`N_iteration_specificity`	Number of iterations for random sampling for specificity p.value calculation. Default is 1000.
`sensitivity`	An optional logical indicating if a sensitivity analysis should be done. Default is FALSE.
`N_iteration_sensitivity`	Number of iterations for random sampling for sensitivity analysis. Default is 1000.
`sample_list`	An optional character vector of TCGA samples barcodes indicating a subset of samples within a cohort.

If no icp_gene is specified, the default icp_gene_list will be used. If select_iap is a character vector, it must be any sub-list of IAP names as listed in TCGA_immune_features_list. If a numeric matrix or data.frame, each column represents a user-defined IAP and must have a range between 0,1. If select_iap is missing all IAPs listed in TCGA_immune_features_list will be considered for analysis.

For synergy score calculations all features are normalized to be on 0,1 range. For details of synergy score and significance pvalue calculations see find_a_synergy function.

A specificity p.value is computed using random sampling with replacement from two null models, generated from one of the two genes against a set of genes randomly selected from the genome. Two P-values are calculated for the synergistic interaction of the pair against the two null models. The highest of the two P-values is used to assess the specificity of the interaction against the whole genome. The number of randomly selected genes in each null model is determined by N_iteration_specificity.

Sensitivity (Robustness) score defined as normalized root mean square deviation of scores calculated over 70% of samples, selected via random sampling. The number of sub-sample iterations is determined by N_iteration_sensitivity.

All barcodes in sample_list must be 15 character long and belong to the same cohort. When sample_list is provided, cohort should be the disease cohort that they belong to, otherwise only the first element of the cohort list will be used.

A data.frame of synergy scores and bootstrapping p.values.