scQTLtools:An R package for single-cell eQTL analysis.

Introduction

Expression quantitative trait loci (eQTL) analysis links variations in gene expression levels to genotypes. This package attempts to identify genetic variants that affect the expression of genes at a single-cell level, and can also do cis-eQTL analysis, visualize the results.

Citation

If you find this tool useful, please cite:

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https://github.com/XFWu/scQTLtools

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Installation

if (!require("BiocManager"))
    install.packages("BiocManager")
BiocManager::install("scQTLtools")

Overview of the package

scQTLtools functions can be categorized into mainly single-cell eQTL analysis and Visualization modules. Each of these functions and a short description is summarized as shown below.

Overview

Required input files

The input file requires genotype data, as well as either a gene expression matrix or a SeuratObject.

• gene expression matrix: describes gene expressions, the row names represent gene IDs or SYMBOL and the column names represent cell IDs.
• SeuratObject: a Seurat object, yourseurat@assays$RNA@data is the gene expression matrix after normalizing.
• genotype matrix: A genotype matrix where each row is one variant and each column is one sample, and the scoring method is 0/1/2/3, 0 represents missing values, 1 represents ref/ref, 2 represents alt/alt, and 3 represents ref/alt.

The columns of the genotype matrix should correspond to the columns of the gene expression matrix.

Example

library(scQTLtools)
# gene expression matrix
data(testGene)
# SeuratObject
data(testSeurat)
# load the genotype data
data(testSNP)
data(testSNP2)

Create eqtl object

The createQTLObject class is an R object designed to store data related to eQTL analysis, encompassing data lists, result data frames, and slots for biClassify, species, and group information.

Example

eqtl_matrix <- createQTLObject(
    snpMatrix = testSNP,
    genedata = testGene,
    biClassify = FALSE,
    species = 'human',
    group = NULL)

Users can set biClassify to TRUE to change the genotype coding method.

Example

eqtl_matrix_bi <- createQTLObject(
    snpMatrix = testSNP,
    genedata = testGene,
    biClassify = TRUE,
    species = 'human',
    group = NULL)

Users can use Seuratobjct instead of gene expression matrix.

Example

eqtl_seurat <- createQTLObject(
    snpMatrix = testSNP2,
    genedata = testSeurat,
    biClassify = FALSE,
    species = 'human',
    group = "celltype")

Normalize gene expression matrix

Use normalizeGene() to normalize the gene expression matrix.

Example

eqtl_matrix  <- normalizeGene(
    eQTLObject = eqtl_matrix, 
    method = "logNormalize")

Identify the valid gene snp pairs

Here we use filterGeneSNP() to filter snp gene pairs.

Example

eqtl_matrix <- filterGeneSNP(
    eQTLObject = eqtl_matrix,
    snpNumOfCellsPercent = 2,
    expressionMin = 0,
    expressionNumOfCellsPercent = 2)

eqtl_seurat <- filterGeneSNP(
    eQTLObject = eqtl_seurat,
    snpNumOfCellsPercent = 2,
    expressionMin = 0,
    expressionNumOfCellsPercent = 2)

Call single cell eQTL

Here we use callQTL() to do single cell eQTL analysis.

Example

eqtl1_matrix <- callQTL(
    eQTLObject = eqtl_matrix,
    gene_ids = NULL,
    downstream = NULL,
    upstream = NULL,
    pAdjustMethod = "bonferroni",
    useModel = "poisson",
    pAdjustThreshold = 0.05,
    logfcThreshold = 0.1)

eqtl1_seurat <- callQTL(
    eQTLObject = eqtl_seurat,
    gene_ids = NULL,
    downstream = NULL,
    upstream = NULL,
    pAdjustMethod = "bonferroni",
    useModel = "linear",
    pAdjustThreshold = 0.05,
    logfcThreshold = 0.025)

Users can use the parameter gene_ids to select one or several genes of interest for identifying sc-eQTLs.

Example

eqtl2_matrix <- callQTL(
    eQTLObject = eqtl_matrix,
    gene_ids = c("CNN2", 
                "RNF113A", 
                "SH3GL1", 
                "INTS13", 
                "PLAU"),
    downstream = NULL,
    upstream = NULL,
    pAdjustMethod = "bonferroni",
    useModel = "poisson",
    pAdjustThreshold = 0.05,
    logfcThreshold = 0.1)

Users can also use upstream and downstream to specify SNPs proximal to the gene in the genome.

Example

eqtl3_matrix <- callQTL(
    eQTLObject = eqtl_matrix,
    gene_ids = NULL,
    downstream = -9e7,
    upstream = 2e8,
    pAdjustMethod = "bonferroni",
    useModel = "poisson",
    pAdjustThreshold = 0.05,
    logfcThreshold = 0.05)

Visualize the result.

Here we use visualizeQTL() to visualize the result. There are four types of plots available to visualize sc-eQTL results. Users can choose "histplot", "violin", "boxplot", or "QTLplot".

Example

visualizeQTL(
    eQTLObject = eqtl1_matrix,
    SNPid = "1:632647",
    Geneid = "RPS27",
    groupName = NULL,
    plottype = "QTLplot",
    removeoutlier = TRUE)

visualizeQTL(
    eQTLObject = eqtl1_seurat,
    SNPid = "1:632647",
    Geneid = "RPS27",
    groupName = NULL,
    plottype = "QTLplot",
    removeoutlier = TRUE)

In addition, the parameter groupName is used to specify a particular single-cell group of interest.

visualizeQTL(
    eQTLObject = eqtl1_seurat,
    SNPid = "1:632647",
    Geneid = "RPS27",
    groupName = "GMP",
    plottype = "QTLplot",
    removeoutlier = TRUE)