Sliding_Window_Results_Summary: Summarize the sliding window analysis results generated by...

In xihaoli/STAARpipelineSummary: Summarization and Visualization of Analysis Results Generated by STAARpipeline

Summarize the sliding window analysis results generated by STAARpipeline package

Description

The Sliding_Window_Results_Summary function takes in the results of sliding window analysis, the object from fitting the null model, and the set of known variants to be adjusted for in conditional analysis to summarize the sliding window analysis results and analyze the conditional association between a quantitative/dichotomous phenotype (including imbalanced case-control setting) and the rare variants in the unconditional significant genetic region.

Usage

Sliding_Window_Results_Summary(
  agds_dir,
  jobs_num,
  input_path,
  output_path,
  sliding_window_results_name,
  obj_nullmodel,
  known_loci = NULL,
  cMAC_cutoff = 0,
  method_cond = c("optimal", "naive"),
  rare_maf_cutoff = 0.01,
  QC_label = "annotation/filter",
  variant_type = c("SNV", "Indel", "variant"),
  geno_missing_imputation = c("mean", "minor"),
  Annotation_dir = "annotation/info/FunctionalAnnotation",
  Annotation_name_catalog,
  Use_annotation_weights = FALSE,
  Annotation_name = NULL,
  alpha = 0.05,
  manhattan_plot = FALSE,
  QQ_plot = FALSE,
  cond_null_model_name = NULL,
  cond_null_model_dir = NULL,
  SPA_p_filter = FALSE,
  p_filter_cutoff = 0.05
)

Arguments

agds_dir	file directory of annotated GDS (aGDS) files for all chromosomes (1-22).
jobs_num	a data frame containing the number of jobs for association analysis. The data frame must include a column with the name "sliding_window_num"
input_path	file directory of the sliding window analysis results.
output_path	file output directory of the summary results.
sliding_window_results_name	the file name of the input sliding window analysis results.
obj_nullmodel	an object from fitting the null model, which is either the output from fit_nullmodel function in the STAARpipeline package, or the output from fitNullModel function in the GENESIS package and transformed using the genesis2staar_nullmodel function in the STAARpipeline package.
known_loci	the data frame of variants to be adjusted for in conditional analysis and should contain 4 columns in the following order: chromosome (CHR), position (POS), reference allele (REF), and alternative allele (ALT) (default = NULL).
cMAC_cutoff	the cutoff of the minimum number of the cumulative minor allele of variants in the masks when summarizing the results (default = 0).
method_cond	a character value indicating the method for conditional analysis. optimal refers to regressing residuals from the null model on known_loci as well as all covariates used in fitting the null model (fully adjusted) and taking the residuals; naive refers to regressing residuals from the null model on known_loci and taking the residuals (default = optimal).
rare_maf_cutoff	the cutoff of maximum minor allele frequency in defining rare variants (default = 0.01).
QC_label	channel name of the QC label in the GDS/aGDS file (default = "annotation/filter").
variant_type	variants include in the conditional analysis. Choices include "variant", "SNV", or "Indel" (default = "SNV").
geno_missing_imputation	method of handling missing genotypes. Either "mean" or "minor" (default = "mean").
Annotation_dir	channel name of the annotations in the aGDS file (default = "annotation/info/FunctionalAnnotation").
Annotation_name_catalog	a data frame containing the name and the corresponding channel name in the aGDS file.
Use_annotation_weights	use annotations as weights or not (default = FALSE).
Annotation_name	a vector of annotation names used in STAAR (default = NULL).
alpha	threshod to control the genome-wise (family-wise) error rate (default = 0.05), the p-value threshold is alpha/total number of sliding windows
manhattan_plot	output manhattan plot or not (default = FALSE).
QQ_plot	output Q-Q plot or not (default = FALSE).
cond_null_model_name	the null model name for conditional analysis in the SPA setting, only used for imbalanced case-control setting (default = NULL).
cond_null_model_dir	the directory of storing the null model for conditional analysis in the SPA setting, only used for imbalanced case-control setting (default = NULL).
SPA_p_filter	logical: are only the variants with a normal approximation based p-value smaller than a pre-specified threshold use the SPA method to recalculate the p-value, only used for imbalanced case-control setting (default = FALSE).
p_filter_cutoff	threshold for the p-value recalculation using the SPA method, only used for imbalanced case-control setting (default = 0.05).

Value

The function returns the following analysis results:

results_sliding_window_genome.Rdata: a matrix contains the STAAR p-values (including STAAR-O or STAAR-B in imbalanced case-control setting) of the sliding windows across the genome.

sliding_window_sig.Rdata and sliding_window_sig.csv: a matrix contains the unconditional STAAR p-values (including STAAR-O or STAAR-B in imbalanced case-control setting) of the significant sliding windows (unconditional p-value<alpha/total number of sliding windows).

sliding_window_sig_cond.Rdata and sliding_window_sig_cond.csv: a matrix contains the conditional STAAR p-values (including STAAR-O or STAAR-B in imbalanced case-control setting) of the significant sliding windows (available if known_loci is not a NULL).

manhattan plot (optional) and Q-Q plot (optional) of the sliding window analysis results.

References

Li, Z., Li, X., et al. (2022). A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies. Nature Methods, 19(12), 1599-1611. (pub)

xihaoli/STAARpipelineSummary documentation built on July 27, 2024, 4:30 p.m.