# 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:
------------------------------------------------------------------------
***[https://github.com/XFWu/scQTLtools](https://github.com/XFWu/scQTLtools)***
------------------------------------------------------------------------
## Installation
```{r, eval = FALSE}
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](vignettes/Overview.svg)***
## 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**
```{r input, message=FALSE}
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**
```{r createObject_matrix, message=FALSE}
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**
```{r createObject_matrix_bi, message=FALSE}
eqtl_matrix_bi <- createQTLObject(
snpMatrix = testSNP,
genedata = testGene,
biClassify = TRUE,
species = 'human',
group = NULL)
```
Users can use Seuratobjct instead of gene expression matrix.
**Example**
```{r createObject_seuratobject, message=FALSE}
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**
```{r Normalize_matrix, message=FALSE}
eqtl_matrix <- normalizeGene(
eQTLObject = eqtl_matrix,
method = "logNormalize")
```
## Identify the valid gene snp pairs
Here we use `filterGeneSNP()` to filter snp gene pairs.
**Example**
```{r filter_matrix, message=FALSE}
eqtl_matrix <- filterGeneSNP(
eQTLObject = eqtl_matrix,
snpNumOfCellsPercent = 2,
expressionMin = 0,
expressionNumOfCellsPercent = 2)
```
```{r filter_seuratobject, message=FALSE}
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**
```{r callQTL1_matrix, message=FALSE}
eqtl1_matrix <- callQTL(
eQTLObject = eqtl_matrix,
gene_ids = NULL,
downstream = NULL,
upstream = NULL,
pAdjustMethod = "bonferroni",
useModel = "poisson",
pAdjustThreshold = 0.05,
logfcThreshold = 0.1)
```
```{r callQTL1_seuratobject, message=FALSE}
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**
```{r callQTL2_matrix, message=FALSE}
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**
```{r callQTL3_matrix, message=FALSE}
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**
```{r visualizeQTL_matrix, message=FALSE}
visualizeQTL(
eQTLObject = eqtl1_matrix,
SNPid = "1:632647",
Geneid = "RPS27",
groupName = NULL,
plottype = "QTLplot",
removeoutlier = TRUE)
```
```{r visualizeQTL_seuratobject, message=FALSE}
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.
```{r visualizeQTL_seuratobject_groupName, message=FALSE}
visualizeQTL(
eQTLObject = eqtl1_seurat,
SNPid = "1:632647",
Geneid = "RPS27",
groupName = "GMP",
plottype = "QTLplot",
removeoutlier = TRUE)
```
