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STAARpipelineSummary v0.9.7
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xihaoli committed Mar 24, 2024
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2 changes: 1 addition & 1 deletion DESCRIPTION
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Expand Up @@ -2,7 +2,7 @@ Package: STAARpipelineSummary
Type: Package
Title: Summarization and Visualization of Analysis Results Generated by STAARpipeline
Version: 0.9.7
Date: 2023-11-11
Date: 2024-03-23
Author: Xihao Li [aut, cre], Zilin Li [aut, cre]
Maintainer: Xihao Li <xihaoli@unc.edu>, Zilin Li <li@nenu.edu.cn>
Description: An R package for summarizing analysis results generated by STAARpipeline.
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11 changes: 3 additions & 8 deletions R/Gene_Centric_Coding_Results_Summary.R
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Expand Up @@ -24,16 +24,11 @@
#' as well as all covariates used in fitting the null model (fully adjusted) and taking the residuals;
#' \code{naive} refers to regressing residuals from the null model on \code{known_loci}
#' and taking the residuals (default = \code{optimal}).
#' @param geno_missing_imputation method of handling missing genotypes. Either "mean" or "minor" (default = "mean").
#' @param variant_type type of variant included in the analysis. Choices include "SNV", "Indel", or "variant" (default = "SNV").
#' @param method_cond a character value indicating the method for conditional analysis.
#' \code{optimal} refers to regressing residuals from the null model on \code{known_loci}
#' as well as all covariates used in fitting the null model (fully adjusted) and taking the residuals;
#' \code{naive} refers to regressing residuals from the null model on \code{known_loci}
#' and taking the residuals (default = \code{optimal}).
#' @param rare_maf_cutoff the cutoff of maximum minor allele frequency in
#' defining rare variants (default = 0.01).
#' @param QC_label channel name of the QC label in the GDS/aGDS file (default = "annotation/filter").
#' @param variant_type type of variant included in the analysis. Choices include "SNV", "Indel", or "variant" (default = "SNV").
#' @param geno_missing_imputation method of handling missing genotypes. Either "mean" or "minor" (default = "mean").
#' @param Annotation_dir channel name of the annotations in the aGDS file \cr (default = "annotation/info/FunctionalAnnotation").
#' @param Annotation_name_catalog a data frame containing the name and the corresponding channel name in the aGDS file.
#' @param Use_annotation_weights use annotations as weights or not (default = FALSE).
Expand Down Expand Up @@ -79,7 +74,7 @@
Gene_Centric_Coding_Results_Summary <- function(agds_dir,gene_centric_coding_jobs_num,input_path,output_path,gene_centric_results_name,
obj_nullmodel,known_loci=NULL,cMAC_cutoff=0,
method_cond=c("optimal","naive"),rare_maf_cutoff=0.01,
QC_label="annotation/filter",geno_missing_imputation=c("mean","minor"),variant_type=c("SNV","Indel","variant"),
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=2.5E-06,manhattan_plot=FALSE,QQ_plot=FALSE,
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25 changes: 10 additions & 15 deletions R/Gene_Centric_Coding_Results_Summary_incl_ptv.R
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Expand Up @@ -24,16 +24,11 @@
#' as well as all covariates used in fitting the null model (fully adjusted) and taking the residuals;
#' \code{naive} refers to regressing residuals from the null model on \code{known_loci}
#' and taking the residuals (default = \code{optimal}).
#' @param geno_missing_imputation method of handling missing genotypes. Either "mean" or "minor" (default = "mean").
#' @param variant_type type of variant included in the analysis. Choices include "SNV", "Indel", or "variant" (default = "SNV").
#' @param method_cond a character value indicating the method for conditional analysis.
#' \code{optimal} refers to regressing residuals from the null model on \code{known_loci}
#' as well as all covariates used in fitting the null model (fully adjusted) and taking the residuals;
#' \code{naive} refers to regressing residuals from the null model on \code{known_loci}
#' and taking the residuals (default = \code{optimal}).
#' @param rare_maf_cutoff the cutoff of maximum minor allele frequency in
#' defining rare variants (default = 0.01).
#' @param QC_label channel name of the QC label in the GDS/aGDS file (default = "annotation/filter").
#' @param variant_type type of variant included in the analysis. Choices include "SNV", "Indel", or "variant" (default = "SNV").
#' @param geno_missing_imputation method of handling missing genotypes. Either "mean" or "minor" (default = "mean").
#' @param Annotation_dir channel name of the annotations in the aGDS file \cr (default = "annotation/info/FunctionalAnnotation").
#' @param Annotation_name_catalog a data frame containing the name and the corresponding channel name in the aGDS file.
#' @param Use_annotation_weights use annotations as weights or not (default = FALSE).
Expand Down Expand Up @@ -83,14 +78,14 @@
#' @export

Gene_Centric_Coding_Results_Summary_incl_ptv <- function(agds_dir,gene_centric_coding_jobs_num,input_path,output_path,gene_centric_results_name,
obj_nullmodel,known_loci=NULL,cMAC_cutoff=0,
method_cond=c("optimal","naive"),rare_maf_cutoff=0.01,
QC_label="annotation/filter",geno_missing_imputation=c("mean","minor"),variant_type=c("SNV","Indel","variant"),
Annotation_dir="annotation/info/FunctionalAnnotation",Annotation_name_catalog,
Use_annotation_weights=FALSE,Annotation_name=NULL,
alpha=2.5E-06,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){
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=2.5E-06,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){

## evaluate choices
method_cond <- match.arg(method_cond)
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11 changes: 3 additions & 8 deletions R/Gene_Centric_Noncoding_Results_Summary.R
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Expand Up @@ -27,16 +27,11 @@
#' as well as all covariates used in fitting the null model (fully adjusted) and taking the residuals;
#' \code{naive} refers to regressing residuals from the null model on \code{known_loci}
#' and taking the residuals (default = \code{optimal}).
#' @param geno_missing_imputation method of handling missing genotypes. Either "mean" or "minor" (default = "mean").
#' @param variant_type type of variant included in the analysis. Choices include "SNV", "Indel", or "variant" (default = "SNV").
#' @param method_cond a character value indicating the method for conditional analysis.
#' \code{optimal} refers to regressing residuals from the null model on \code{known_loci}
#' as well as all covariates used in fitting the null model (fully adjusted) and taking the residuals;
#' \code{naive} refers to regressing residuals from the null model on \code{known_loci}
#' and taking the residuals (default = \code{optimal}).
#' @param rare_maf_cutoff the cutoff of maximum minor allele frequency in
#' defining rare variants (default = 0.01).
#' @param QC_label channel name of the QC label in the GDS/aGDS file (default = "annotation/filter").
#' @param variant_type type of variant included in the analysis. Choices include "SNV", "Indel", or "variant" (default = "SNV").
#' @param geno_missing_imputation method of handling missing genotypes. Either "mean" or "minor" (default = "mean").
#' @param Annotation_dir channel name of the annotations in the aGDS file \cr (default = "annotation/info/FunctionalAnnotation").
#' @param Annotation_name_catalog a data frame containing the name and the corresponding channel name in the aGDS file.
#' @param Use_annotation_weights use annotations as weights or not (default = FALSE).
Expand Down Expand Up @@ -92,7 +87,7 @@ Gene_Centric_Noncoding_Results_Summary <- function(agds_dir,gene_centric_noncodi
ncRNA_jobs_num,ncRNA_input_path,ncRNA_output_path,ncRNA_results_name,
obj_nullmodel,known_loci=NULL,cMAC_cutoff=0,
method_cond=c("optimal","naive"),rare_maf_cutoff=0.01,
QC_label="annotation/filter",geno_missing_imputation=c("mean","minor"),variant_type=c("SNV","Indel","variant"),
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=2.5E-06,alpha_ncRNA=2.5E-06,
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19 changes: 10 additions & 9 deletions R/Individual_Analysis_Results_Summary.R
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Expand Up @@ -21,8 +21,9 @@
#' as well as all covariates used in fitting the null model (fully adjusted) and taking the residuals;
#' \code{naive} refers to regressing residuals from the null model on \code{known_loci}
#' and taking the residuals (default = \code{optimal}).
#' @param geno_missing_imputation method of handling missing genotypes. Either "mean" or "minor" (default = "mean").
#' @param QC_label channel name of the QC label in the GDS/aGDS file.
#' @param variant_type type of variant included in the analysis. Choices include "variant", "SNV", or "Indel" (default = "variant").
#' @param geno_missing_imputation method of handling missing genotypes. Either "mean" or "minor" (default = "mean").
#' @param alpha p-value threshold of significant results (default = 5E-09).
#' @param manhattan_plot output manhattan plot or not (default = FALSE).
#' @param QQ_plot output Q-Q plot or not (default = FALSE).
Expand All @@ -44,7 +45,7 @@
Individual_Analysis_Results_Summary <- function(agds_dir,jobs_num,input_path,output_path,individual_results_name,
obj_nullmodel,known_loci=NULL,
method_cond=c("optimal","naive"),
QC_label="annotation/filter",geno_missing_imputation=c("mean","minor"),
QC_label="annotation/filter",variant_type=c("variant","SNV","Indel"),geno_missing_imputation=c("mean","minor"),
alpha=5E-09,manhattan_plot=FALSE,QQ_plot=FALSE,
SPA_p_filter=FALSE,p_filter_cutoff=0.05,
cond_null_model_name=NULL,cond_null_model_dir=NULL){
Expand Down Expand Up @@ -183,9 +184,9 @@ Individual_Analysis_Results_Summary <- function(agds_dir,jobs_num,input_path,out
gds.path <- agds_dir[chr]
genofile <- seqOpen(gds.path)

results_sig_cond_chr <- Individual_Analysis_cond(chr=chr,individual_results=results_sig_chr,genofile,obj_nullmodel=obj_nullmodel,
known_loci=known_loci,variant_type="variant",
QC_label=QC_label,geno_missing_imputation=geno_missing_imputation,method_cond=method_cond)
results_sig_cond_chr <- Individual_Analysis_cond(chr=chr,individual_results=results_sig_chr,genofile=genofile,obj_nullmodel=obj_nullmodel,
known_loci=known_loci,method_cond=method_cond,QC_label=QC_label,
variant_type=variant_type,geno_missing_imputation=geno_missing_imputation)

results_sig_cond <- rbind(results_sig_cond,results_sig_cond_chr)

Expand All @@ -210,8 +211,8 @@ Individual_Analysis_Results_Summary <- function(agds_dir,jobs_num,input_path,out

obj_nullmodel_cond <- get(load(paste0(cond_null_model_dir,cond_null_model_name,".chr",chr,".Rdata")))

results_sig_cond_chr <- Individual_Analysis_cond_spa(chr=chr,individual_results=results_sig_chr,genofile,obj_nullmodel=obj_nullmodel_cond,
variant_type="variant",QC_label=QC_label,geno_missing_imputation=geno_missing_imputation,
results_sig_cond_chr <- Individual_Analysis_cond_spa(chr=chr,individual_results=results_sig_chr,genofile=genofile,obj_nullmodel=obj_nullmodel_cond,
QC_label=QC_label,variant_type=variant_type,geno_missing_imputation=geno_missing_imputation,
SPA_p_filter=SPA_p_filter,p_filter_cutoff=p_filter_cutoff)

results_sig_cond <- rbind(results_sig_cond,results_sig_cond_chr)
Expand All @@ -224,8 +225,8 @@ Individual_Analysis_Results_Summary <- function(agds_dir,jobs_num,input_path,out
gds.path <- agds_dir[chr]
genofile <- seqOpen(gds.path)

results_sig_cond_chr <- Individual_Analysis_cond_spa(chr=chr,individual_results=results_sig_chr,genofile,obj_nullmodel=obj_nullmodel,
variant_type="variant",QC_label=QC_label,geno_missing_imputation=geno_missing_imputation,
results_sig_cond_chr <- Individual_Analysis_cond_spa(chr=chr,individual_results=results_sig_chr,genofile=genofile,obj_nullmodel=obj_nullmodel,
QC_label=QC_label,variant_type=variant_type,geno_missing_imputation=geno_missing_imputation,
SPA_p_filter=SPA_p_filter,p_filter_cutoff=p_filter_cutoff)

results_sig_cond <- rbind(results_sig_cond,results_sig_cond_chr)
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2 changes: 1 addition & 1 deletion R/Single_Variants_List_Analysis.R
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Expand Up @@ -8,8 +8,8 @@
#' the following names: "CHR" (chromosome number), "POS" (position), "REF" (reference allele), and "ALT" (alternative allele).
#' @param obj_nullmodel an object from fitting the null model, which is either the output from \code{fit_nullmodel} function in the \code{STAARpipeline} package,
#' or the output from \code{fitNullModel} function in the \code{GENESIS} package and transformed using the \code{genesis2staar_nullmodel} function in the \code{STAARpipeline} package.
#' @param geno_missing_imputation method of handling missing genotypes. Either "mean" or "minor" (default = "mean").
#' @param QC_label channel name of the QC label in the GDS/aGDS file (default = "annotation/filter").
#' @param geno_missing_imputation method of handling missing genotypes. Either "mean" or "minor" (default = "mean").
#' @param p_filter_cutoff threshold for the p-value recalculation using the SPA method (default = 0.05)
#' @param tol a positive number specifying tolerance, the difference threshold for parameter
#' estimates in saddlepoint approximation algorithm below which iterations should be stopped (default = ".Machine$double.eps^0.25").
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11 changes: 3 additions & 8 deletions R/Sliding_Window_Results_Summary.R
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Expand Up @@ -23,16 +23,11 @@
#' as well as all covariates used in fitting the null model (fully adjusted) and taking the residuals;
#' \code{naive} refers to regressing residuals from the null model on \code{known_loci}
#' and taking the residuals (default = \code{optimal}).
#' @param geno_missing_imputation method of handling missing genotypes. Either "mean" or "minor" (default = "mean").
#' @param variant_type variants include in the conditional analysis. Choices include "variant", "SNV", or "Indel" (default = "SNV").
#' @param method_cond a character value indicating the method for conditional analysis.
#' \code{optimal} refers to regressing residuals from the null model on \code{known_loci}
#' as well as all covariates used in fitting the null model (fully adjusted) and taking the residuals;
#' \code{naive} refers to regressing residuals from the null model on \code{known_loci}
#' and taking the residuals (default = \code{optimal}).
#' @param rare_maf_cutoff the cutoff of maximum minor allele frequency in
#' defining rare variants (default = 0.01).
#' @param QC_label channel name of the QC label in the GDS/aGDS file (default = "annotation/filter").
#' @param variant_type variants include in the conditional analysis. Choices include "variant", "SNV", or "Indel" (default = "SNV").
#' @param geno_missing_imputation method of handling missing genotypes. Either "mean" or "minor" (default = "mean").
#' @param Annotation_dir channel name of the annotations in the aGDS file \cr (default = "annotation/info/FunctionalAnnotation").
#' @param Annotation_name_catalog a data frame containing the name and the corresponding channel name in the aGDS file.
#' @param Use_annotation_weights use annotations as weights or not (default = FALSE).
Expand All @@ -58,7 +53,7 @@
Sliding_Window_Results_Summary <- function(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",geno_missing_imputation=c("mean","minor"),variant_type=c("SNV","Indel","variant"),
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,
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4 changes: 2 additions & 2 deletions R/manhattan_plot.R
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Expand Up @@ -2,8 +2,8 @@ manhattan_plot<-function(chr, pos, pvalue,
sig.level=NA, annotate=NULL, ann.default=list(),
should.thin=T, thin.pos.places=2, thin.logp.places=2,
xlab="Chromosome", ylab=expression(-log[10](p-value)),
col=c("gray","darkgray"), panel.extra=NULL, pch=20,
use_logp=FALSE,cex=0.8,...) {
col=c("gray","darkgray"), panel.extra=NULL, pch=20,
use_logp=FALSE,cex=0.8,...) {

if (length(chr)==0) stop("chromosome vector is empty")
if (length(pos)==0) stop("position vector is empty")
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -31,7 +31,7 @@ Please see the <a href="docs/STAARpipelineSummary_manual.pdf">**STAARpipelineSum
## Data Availability
The whole-genome functional annotation data assembled from a variety of sources and the precomputed annotation principal components are available at the [Functional Annotation of Variant - Online Resource (FAVOR)](https://favor.genohub.org) site and [FAVOR Essential Database](https://doi.org/10.7910/DVN/1VGTJI).
## Version
The current version is 0.9.7 (January 29, 2024).
The current version is 0.9.7 (March 23, 2024).
## Citation
If you use **STAARpipeline** and **STAARpipelineSummary** for your work, please cite:

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