STAARpipeline v0.9.7

xihaoli · Feb 13, 2024 · 2e013b4 · 2e013b4
1 parent ee8b071
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diff --git a/R/Gene_Centric_Coding.R b/R/Gene_Centric_Coding.R
@@ -30,7 +30,7 @@
 #' @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 = TRUE).
 #' @param Annotation_name a vector of annotation names used in STAAR (default = NULL).
-#' @param 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).
+#' @param 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 = TRUE).
 #' @param p_filter_cutoff threshold for the p-value recalculation using the SPA method, only used for imbalanced case-control setting (default = 0.05).
 #' @param silent logical: should the report of error messages be suppressed (default = FALSE).
 #' @return A list of data frames containing the STAAR p-values (including STAAR-O or STAAR-B in imbalanced case-control setting) corresponding to the coding functional category of the given gene.
@@ -49,7 +49,7 @@ Gene_Centric_Coding <- function(chr,gene_name,category=c("all_categories","plof"
                                 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=c(TRUE,FALSE),Annotation_name=NULL,
-                                SPA_p_filter=FALSE,p_filter_cutoff=0.05,silent=FALSE){
+                                SPA_p_filter=TRUE,p_filter_cutoff=0.05,silent=FALSE){
 
 	## evaluate choices
 	category <- match.arg(category)

diff --git a/R/Gene_Centric_Noncoding.R b/R/Gene_Centric_Noncoding.R
@@ -29,7 +29,7 @@
 #' @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 = TRUE).
 #' @param Annotation_name a vector of annotation names used in STAAR (default = NULL).
-#' @param 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).
+#' @param 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 = TRUE).
 #' @param p_filter_cutoff threshold for the p-value recalculation using the SPA method, only used for imbalanced case-control setting (default = 0.05).
 #' @param silent logical: should the report of error messages be suppressed (default = FALSE).
 #' @return A list of data frames containing the STAAR p-values (including STAAR-O or STAAR-B in imbalanced case-control setting) corresponding to each noncoding functional category of the given gene.
@@ -48,7 +48,7 @@ Gene_Centric_Noncoding <- function(chr,gene_name,category=c("all_categories","do
                                    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=c(TRUE,FALSE),Annotation_name=NULL,
-                                   SPA_p_filter=FALSE,p_filter_cutoff=0.05,silent=FALSE){
+                                   SPA_p_filter=TRUE,p_filter_cutoff=0.05,silent=FALSE){
 
 	## evaluate choices
 	category <- match.arg(category)

diff --git a/R/Individual_Analysis.R b/R/Individual_Analysis.R
@@ -21,7 +21,7 @@
 #' @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").
 #' @param max_iter a positive integer specifying the maximum number of iterations for applying the saddlepoint approximation algorithm (default = "1000").
-#' @param SPA_p_filter logical: are only the variants with a score-test-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).
+#' @param SPA_p_filter logical: are only the variants with a score-test-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 = TRUE).
 #' @param p_filter_cutoff threshold for the p-value recalculation using the SPA method, only used for imbalanced case-control setting (default = 0.05)
 #' @return A data frame containing the score test p-value and the estimated effect size of the minor allele for each individual variant in the given genetic region.
 #' The first 4 columns correspond to chromosome (CHR), position (POS), reference allele (REF), and alternative allele (ALT).
@@ -36,7 +36,7 @@
 
 Individual_Analysis <- function(chr,start_loc,end_loc,genofile,obj_nullmodel,mac_cutoff=20,subset_variants_num=5e3,
                                 QC_label="annotation/filter",variant_type=c("variant","SNV","Indel"),geno_missing_imputation=c("mean","minor"),
-                                tol=.Machine$double.eps^0.25,max_iter=1000,SPA_p_filter=FALSE,p_filter_cutoff=0.05){
+                                tol=.Machine$double.eps^0.25,max_iter=1000,SPA_p_filter=TRUE,p_filter_cutoff=0.05){
 
 	## evaluate choices
 	variant_type <- match.arg(variant_type)

diff --git a/R/Sliding_Window.R b/R/Sliding_Window.R
@@ -31,7 +31,7 @@
 #' @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 = TRUE).
 #' @param Annotation_name a vector of annotation names used in STAAR (default = NULL).
-#' @param 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).
+#' @param 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 = TRUE).
 #' @param p_filter_cutoff threshold for the p-value recalculation using the SPA method, only used for imbalanced case-control setting (default = 0.05).
 #' @param silent logical: should the report of error messages be suppressed (default = FALSE).
 #' @return A data frame containing the STAAR p-values (including STAAR-O or STAAR-B in imbalanced case-control setting) corresponding to each sliding window in the given genetic region.
@@ -50,7 +50,7 @@ Sliding_Window <- function(chr,start_loc,end_loc,sliding_window_length=2000,type
                            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=c(TRUE,FALSE),Annotation_name=NULL,
-                           SPA_p_filter=FALSE,p_filter_cutoff=0.05,silent=FALSE){
+                           SPA_p_filter=TRUE,p_filter_cutoff=0.05,silent=FALSE){
 
 	## evaluate choices
 	type <- match.arg(type)

diff --git a/R/fit_nullmodel.R b/R/fit_nullmodel.R
@@ -103,7 +103,7 @@ fit_nullmodel <- function(fixed, data = parent.frame(), kins, use_sparse = NULL,
 		                         method.optim = method.optim, maxiter = maxiter,
 		                         tol = tol, taumin = taumin, taumax = taumax,
 		                         tauregion = tauregion, verbose = verbose, ...)
-    	obj_nullmodel$sparse_kins <- TRUE
+		obj_nullmodel$sparse_kins <- TRUE
 
 		if(use_SPA)
 		{

diff --git a/R/ncRNA.R b/R/ncRNA.R
@@ -27,7 +27,7 @@
 #' @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 = TRUE).
 #' @param Annotation_name a vector of annotation names used in STAAR (default = NULL).
-#' @param 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).
+#' @param 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 = TRUE).
 #' @param p_filter_cutoff threshold for the p-value recalculation using the SPA method, only used for imbalanced case-control setting (default = 0.05).
 #' @param silent logical: should the report of error messages be suppressed (default = FALSE).
 #' @return A data frame containing the STAAR p-values (including STAAR-O) corresponding to the exonic and splicing category of the given ncRNA gene.
@@ -46,7 +46,7 @@ ncRNA <- function(chr,gene_name,genofile,obj_nullmodel,
                   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=c(TRUE,FALSE),Annotation_name=NULL,
-                  SPA_p_filter=FALSE,p_filter_cutoff=0.05,silent=FALSE){
+                  SPA_p_filter=TRUE,p_filter_cutoff=0.05,silent=FALSE){
 
 	## evaluate choices
 	variant_type <- match.arg(variant_type)

diff --git a/README.md b/README.md
@@ -32,7 +32,7 @@ Please see the <a href="docs/STAARpipeline_manual.pdf">**STAARpipeline** user ma
 ## 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 22, 2024).
+The current version is 0.9.7 (February 13, 2024).
 ## Citation
 If you use **STAARpipeline** and **STAARpipelineSummary** for your work, please cite:
 

diff --git a/man/Gene_Centric_Coding.Rd b/man/Gene_Centric_Coding.Rd
diff --git a/man/Gene_Centric_Noncoding.Rd b/man/Gene_Centric_Noncoding.Rd
diff --git a/man/Individual_Analysis.Rd b/man/Individual_Analysis.Rd
diff --git a/man/Sliding_Window.Rd b/man/Sliding_Window.Rd
diff --git a/man/ncRNA.Rd b/man/ncRNA.Rd