StatFunGen · xueweic · Apr 25, 2025 · Apr 25, 2025 · Apr 25, 2025 · Apr 25, 2025
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -14,7 +14,8 @@ Authors@R: c(
 Maintainer: Xuewei Cao <xc2270@cumc.columbia.edu>
 Description: A multi-task learning approach to variable selection regression with highly correlated predictors and sparse effects, 
   based on frequentist statistical inference. It provides statistical evidence to identify which subsets of predictors have non-zero 
-  effects on which subsets of response variables, motivated and designed for colocalization analysis of multiple genetic association studies.
+  effects on which subsets of response variables, motivated and designed for colocalization analysis across genome-wide association studies (GWAS)
+  and quantitative trait loci (QTL) studies.
   The ColocBoost model is described in Cao et. al. (2025) <doi:10.1101/2025.04.17.25326042>.
 Encoding: UTF-8
 LazyDataCompression: xz

diff --git a/LICENSE b/LICENSE
@@ -1,2 +1,3 @@
 YEAR: 2025
-COPYRIGHT HOLDER: StatFunGen Lab, Columbia University
+COPYRIGHT HOLDER: Xuewei Cao, Haochen Sun, Ru Feng, Daniel Nachun, Kushal Dey, Gao Wang
+ORGANIZATION: StatFunGen Lab, Columbia University
diff --git a/R/colocboost_init.R b/R/colocboost_init.R
@@ -191,7 +191,6 @@ colocboost_init_model <- function(cb_data,
     }
     tmp$multi_correction <- multiple_testing_correction
     tmp$multi_correction_univariate <- multiple_testing_correction
-    if (length(multiple_testing_correction) == 1) print(class(multiple_testing_correction))
     if (all(multiple_testing_correction == 1)) {
       tmp$stop_null <- 1
     } else if (min(multiple_testing_correction) > multi_test_max) {

diff --git a/R/colocboost_plot.R b/R/colocboost_plot.R
@@ -204,6 +204,9 @@ colocboost_plot <- function(cb_output, y = "log10p",
     } else {
       bottom <- 2
     }
+    # - restore users' options
+    oldpar <- par(no.readonly = TRUE) 
+    on.exit(par(oldpar))
     if (!is.null(cb_plot_init$title)) {
       par(mfrow = c(nrow, plot_cols), mar = c(bottom, 5, 2, 1), oma = c(0, 0, 3, 0))
     } else {

diff --git a/cran-comments.md b/cran-comments.md
@@ -0,0 +1,15 @@
+## R CMD check results
+
+0 errors | 0 warnings | 1 note
+
+* This is a new release.
+
+## Addtional comments
+
+* This package implements methods described in our paper "ColocBoost" (Cao et al., 2025), added in DESCRIPTION
+* Fixed issues requested by CRAN in previous submission:
+  - Reduced tarball less than 5 MB
+  - Fixed reset users' options issues
+  - Added proper COPYRIGHT HOLDER and ORGANIZATION to LICENSE
+  - Added explanation of acronyms used in this package to inst/WORDLIST
+* The examples and vignettes use small datasets to avoid long check times
diff --git a/inst/WORDLIST b/inst/WORDLIST
@@ -1,86 +1,92 @@
-Biobank
-Bioinformatics
-CFB
-COLOC
-CoS
-Codecov
-ColocBoost
-Colocalization
-Colocalized
-Conda
-FineBoost
-GTEx
-GWAS
-HyPrColoc
-INDELs
-Jager
-KK
-LD
-MAF
-Mazumder
-Micromamba
-NPC
-Najar
-Nealelab
-PIPs
-PLINK
-Pre
-Recalibrate
-SEL
-SuSiE
-Sumstat
-UKBB
-VCP
-VPA
-Xcorr
-YI
-al
-bioinformatics
-chrom
-cis
-colocalization
-colocalize
-colocalized
-conda
-de
-decayrate
-doi
-eQTL
-et
-grey
-iteratively
-jk
-ld
-lfsr
-lth
-maf
-medRxiv
-modularity
-nd
-npc
-omics
-phenotypes
-pixi
-pos
-pre
-probabilistically
-pvalue
-qc
-rcond
-recalibrate
-recalibrated
-reconciliate
-repo
-rss
-sQTL
-subsampled
-sumstat
-sumstats
-tabix
-uCoS
-uS
-ucos
-uncolocalized
-vcp
-xQTL
-xQTLs
+# Analysis Tools and Methods
+ColocBoost      # Multiple trait colocalization algorithm we propsed
+Colocalization  # Method to identify shared genetic signals
+COLOC           # A pairwise colocalization method name
+FineBoost       # Single trait fine-mapping algorithm we proposed
+HyPrColoc       # Hypothesis Prioritization in multi-trait Colocalization method name
+SuSiE           # Sum of Single Effects regression model
+
+# Statistical and Genetic Terms
+eQTL            # Expression Quantitative Trait Loci
+GWAS            # Genome-Wide Association Study
+INDELs          # Insertions and Deletions
+LD              # Linkage Disequilibrium
+ld              # Linkage Disequilibrium
+MAF             # Minor Allele Frequency
+maf             # Minor Allele Frequency
+pvalue          # Statistical p-value
+sQTL            # Splicing Quantitative Trait Loci
+Sumstat         # Summary Statistics
+sumstat         # Summary Statistics
+sumstats        # Summary Statistics
+xQTL            # Any molecular Quantitative Trait Loci
+
+# Software, Platforms and Tools
+Biobank         # Refer to UK Biobank, a large-scale biomedical database and research resource
+Bioinformatics  # Computational biology field
+bioinformatics  # Computational biology field
+Codecov         # Code coverage testing tool
+Conda           # Package and environment management system
+GTEx            # Genotype-Tissue Expression project
+Micromamba      # Lightweight Conda implementation
+Nealelab        # Lab developing genetics GWAS summary statistics
+PLINK           # Whole genome association analysis toolset
+tabix           # Tool for indexing genomic data
+UKBB            # UK Biobank dataset
+medRxiv         # A preprint resource
+pixi            # An environment manager
+conda           # Package and environment management system
+
+# Researcher Names
+Jager           # Philip L. de Jager
+KK              # Kushal K. Dey
+Mazumder        # Rahul Mazumder
+Najar           # Cristian F. B. Najar
+CFB             # Cristian F. B. Najar
+YI              # Yang I. Li
+et              # and more
+al              # and more
+
+# Technical Terms
+cis             # Referring to nearby location of a regulatory element
+chrom           # Chromosome
+decayrate       # Decay rate, an input parameter in our package
+doi             # Digital Object Identifier
+grey            # One color name in R
+iteratively     # Performed through iterations
+lfsr            # Local False Sign Rate
+lth             # Lower threshold
+modularity      # Property of network structure
+omics           # Collective biological data fields
+phenotypes      # Observable traits
+pos             # Position in genome
+probabilistically # Based on probability theory
+qc              # Quality Control
+rcond           # Reciprocal condition number
+reconciliate    # Process of resolving discrepancies
+repo            # Repository
+rss             # Residual Sum of Squares
+subsampled      # Analyzed using data subsets
+uncolocalized   # Not showing colocalization
+uCoS            # Trait-specific (uncolocalization) confidence set
+ucos            # Trait-specific (uncolocalization) confidence set
+VCP             # Variant colocalization probability
+VPA             # Variant probability of association
+vcp             # Variant colocalization probability
+Xcorr           # Cross-correlation matrix
+Recalibrate     # Process of adjusting statistical calibration
+recalibrate     # Process of adjusting statistical calibration
+recalibrated    # Process of adjusting statistical calibration
+Colocalized     # Having undergone colocalization analysis
+colocalize      # Having undergone colocalization analysis
+colocalized     # Having undergone colocalization analysis
+CoS             # Colocalization confidence set in our proposed ColocBoost method
+NPC             # Normalization probability of colocalization in our proposed ColocBoost method
+PIPs            # Posterior Inclusion Probabilities
+SEL             # Single-effect learner in our proposed ColocBoost method
+uS              # The number of uCoS
+npc             # Normalization probability of colocalization in our proposed ColocBoost method
+Pre             # Before
+pre             # Before
+jk              # Index used in ColocBoost
+nd              # Second
diff --git a/vignettes/Interpret_ColocBoost_Output.Rmd b/vignettes/Interpret_ColocBoost_Output.Rmd
@@ -234,19 +234,25 @@ plot(res$model_info$outcome_profile_loglik[[i]], type="p", col="#CC3333", lwd=2,
 
 
 ```{r objective-proximity}
+# Save to restore default options
+oldpar <- par(no.readonly = TRUE)
 # Plotting trait-specific proximity objective
 par(mfrow=c(2,3), mar=c(4,4,2,1))
 for(i in 1:5){
 plot(res$model_info$outcome_proximity_obj[[i]], type="p", col="#3366CC", lwd=2, xlab="", ylab="Trait-specific Objective", main = paste0("Trait ", i))
 }
+par(oldpar)
 ```
 
 ```{r objective-best}
+# Save to restore default options
+oldpar <- par(no.readonly = TRUE)
 # Plotting trait-specific objective at the best update variant
 par(mfrow=c(2,3), mar=c(4,4,2,1))
 for(i in 1:5){ 
   plot(res$model_info$outcome_coupled_best_update_obj[[i]], type="p", col="#CC3333", lwd=2, xlab="", ylab=paste0("Objective at best update variant"), main = paste0("Trait ", i)) 
 }
+par(oldpar)
 ```
 
 ### 3.5. Trait-specific effects information (**`ucos_details`**)