Package: scQTLtools 1.5.0

Xiaofeng Wu

scQTLtools: scQTLtools: an R/Bioconductor package for comprehensive identification and visualization of single-cell eQTLs

scQTLtools is a comprehensive R/Bioconductor package that facilitates end-to-end single-cell eQTL analysis, from preprocessing to visualization

Authors:Xiaofeng Wu [aut, cre, cph], Xin Huang [aut, cph], Jingtong Kang [com], Siwen Xu [aut, cph]

scQTLtools_1.5.0.tar.gz
scQTLtools_1.5.0.zip(r-4.7)scQTLtools_1.5.0.zip(r-4.6)scQTLtools_1.5.0.zip(r-4.5)
scQTLtools_1.5.0.tgz(r-4.6-any)scQTLtools_1.5.0.tgz(r-4.5-any)
scQTLtools_1.5.0.tar.gz(r-4.7-any)scQTLtools_1.5.0.tar.gz(r-4.6-any)
scQTLtools_1.5.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
scQTLtools/json (API)
NEWS

# Install 'scQTLtools' in R:

install.packages('scQTLtools', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/xfwucn/scqtltools/issues

Datasets:

EQTL_obj - The package includes five examplt datasets: a gene expression matrix, two SNP genotype matrices, a Seurat object, and an eQTL object.
GeneData - The package includes five examplt datasets: a gene expression matrix, two SNP genotype matrices, a Seurat object, and an eQTL object.
Seurat_obj - The package includes five examplt datasets: a gene expression matrix, two SNP genotype matrices, a Seurat object, and an eQTL object.
SNPData - The package includes five examplt datasets: a gene expression matrix, two SNP genotype matrices, a Seurat object, and an eQTL object.
SNPData2 - The package includes five examplt datasets: a gene expression matrix, two SNP genotype matrices, a Seurat object, and an eQTL object.

On BioConductor:scQTLtools-1.5.0(bioc 3.24)scQTLtools-1.4.0(bioc 3.23)

software geneexpression geneticvariability snp differentialexpression genomicvariation variantdetection genetics functionalgenomics systemsbiology regression singlecell normalization visualization preprocessing rna-seq sc-eqtl

4.38 score 6 stars 6 scripts 242 downloads 40 exports 112 dependencies

Last updated from:19bb7507c7. Checks:1 NOTE, 3 WARNING, 5 ERROR, 1 OK. Indexed: yes.

Target	Result	Time
bioc-checks	NOTE	294
linux-devel-x86_64	WARNING	632
source / vignettes	ERROR	449
linux-release-x86_64	WARNING	659
macos-release-arm64	ERROR	318
macos-oldrel-arm64	ERROR	456
windows-devel	WARNING	560
windows-release	ERROR	465
windows-oldrel	ERROR	459
wasm-release	OK	221

Exports:adjust_pvalues buildZINB callQTL checkSNPList CPM_normalize createGeneLoc createQTLObject createSNPsLoc DESeq_normalize draw_boxplot draw_histplot draw_QTLplot draw_violinplot filter_by_abs_b filterGeneSNP get_cell_groups get_counts get_filter_data get_model_info get_raw_data get_result_info initialize_progress_bar limma_normalize linearModel load_biclassify_info load_group_info load_species_info log_normalize normalizeGene plots_theme_opts poissonModel process_matrix remove_outliers set_filter_data set_model_info set_raw_data set_result_info TPM_normalize visualizeQTL zinbModel

Dependencies:abind AnnotationDbi askpass BH Biobase BiocFileCache BiocGenerics BiocParallel biomaRt Biostrings bit bit64 blob cachem cli codetools cpp11 crayon curl DBI dbplyr DelayedArray DESeq2 digest dotCall64 dplyr farver fastmap filelock formatR fs futile.logger futile.options future future.apply gamlss gamlss.data gamlss.dist generics GenomicRanges ggplot2 globals glue gtable hms httr httr2 IRanges isoband jsonlite KEGGREST labeling lambda.r lattice lifecycle limma listenv locfit magrittr MASS Matrix MatrixGenerics matrixStats memoise mime nlme openssl parallelly patchwork pillar pkgconfig png prettyunits progress progressr purrr R6 rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen rlang RSQLite S4Arrays S4Vectors S7 scales Seqinfo SeuratObject SingleCellExperiment snow sp spam SparseArray statmod stringi stringr SummarizedExperiment survival sys tibble tidyr tidyselect utf8 vctrs VGAM viridisLite withr xml2 XVector yulab.utils

Vignettes and/or manual for this package failed to build, some documents may be unavailable. Please inspect thebuild logs for more information.

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Adjust p-values and filter SNP–gene pairs based on the adjusted p-values.	adjust_pvalues
Build a ZINB model.	buildZINB
Identify single-cell eQTLs.	callQTL
Validate SNP IDs in the input genotype matrix.	checkSNPList
Normalize gene expression using CPM.	CPM_normalize
Create a gene location data frame.	createGeneLoc
Create an eQTLObject for storing sc-eQTL analysis data.	createQTLObject
Create SNP location dataframe.	createSNPsLoc
The package includes five examplt datasets: a gene expression matrix, two SNP genotype matrices, a Seurat object, and an eQTL object.	DataSet EQTL_obj GeneData Seurat_obj SNPData SNPData2
Normalize the gene expression matrix with DESeq2.	DESeq_normalize
Generate a boxplot of gene expression by SNP genotype.	draw_boxplot
Generate a histogram of gene expression by SNP genotype.	draw_histplot
Create a combined violin-box-scatter plot.	draw_QTLplot
Generate a violin plot of gene expression by SNP genotype.	draw_violinplot
Class 'eQTLObject'	eQTLObject-class
Filter a data frame of SNP–gene pairs by absolute beta.	filter_by_abs_b
Filter gene expression and genotype matrices by cell percentage thresholds.	filterGeneSNP
Retrieve Cells by SNP Value	get_cell_groups
Extract Counts from an Expression Matrix	get_counts
Access filtered data stored in an eQTLObject.	get_filter_data get_filter_data,eQTLObject-method
Access model specification from an eQTLObject	get_model_info get_model_info,eQTLObject-method
Access raw data stored in an eQTLObject.	get_raw_data get_raw_data,eQTLObject-method
Access eQTLs results from an eQTLObject.	get_result_info get_result_info,eQTLObject-method
Progress Bar for Model Analysis.	initialize_progress_bar
Normalize the gene expression matrix with limma	limma_normalize
Fit Linear Model for eQTL Mapping	linearModel
Access biclassification information from an eQTLObject.	load_biclassify_info load_biclassify_info,eQTLObject-method
Access cell grouping information from an eQTLObject	load_group_info load_group_info,eQTLObject-method
Access species information from an eQTLObject.	load_species_info load_species_info,eQTLObject-method
Normalize the gene expression matrix with logNormalize method.	log_normalize
Normalize gene expression data.	normalizeGene
Customized ggplot2 Theme for Plots	plots_theme_opts
Poisson model fitting the gene expression matrix and genotype matrix.	poissonModel
Process a matrix to extract a row and convert it to a data frame.	process_matrix
Remove outliers from gene expression data and update cell groups.	remove_outliers
Set filtered data in an eQTLObject.	set_filter_data set_filter_data,eQTLObject-method
Set model specification in an eQTLObject	set_model_info set_model_info,eQTLObject-method
Set raw data in an eQTLObject.	set_raw_data set_raw_data,eQTLObject-method
Set eQTL results in an eQTLObject.	set_result_info set_result_info,eQTLObject-method
Normalize the gene expression matrix with TPM	TPM_normalize
Visualize eQTL results by SNP–gene pair across groups	visualizeQTL
Zinb model fitting the gene expression matrix.	zinbModel