Package 'scQTLtools'

Title: An R package for single-cell eQTL analysis and visualization
Description: This package specializes in analyzing and visualizing eQTL at the single-cell level. It can read gene expression matrices or Seurat data, or SingleCellExperiment object along with genotype data. It offers a function for cis-eQTL analysis to detect eQTL within a given range, and another function to fit models with three methods. Using this package, users can also generate single-cell level visualization result.
Authors: Xiaofeng Wu [aut, cre, cph] , Xin Huang [aut, cph], Jingtong Kang [com], Siwen Xu [aut, cph]
Maintainer: Xiaofeng Wu <[email protected]>
License: MIT + file LICENSE
Version: 0.99.12
Built: 2025-03-27 03:39:34 UTC
Source: https://github.com/bioc/scQTLtools

Help Index

Adjust p-values and perform threshold filtering based on the adjusted p-values.

Description

Adjust p-values and perform threshold filtering based on the adjusted p-values.

Usage

adjust_pvalues(result, pAdjustMethod = "bonferroni", pAdjustThreshold = 0.05)

Arguments

result

Dataframe that contains gene-SNP pairs' information.
pAdjustMethod

Methods for p-value adjusting, one of "bonferroni", "holm", "hochberg", "hommel" or "BH". The default option is "bonferroni".
pAdjustThreshold

Only SNP-Gene pairs with adjusted p-values meeting the threshold will be displayed. Default by 0.05.

Value

A dataframe that has been adjusted and filtered, containing information on gene-SNP pairs.

Examples

example_data <- data.frame(
  gene = c("Gene1", "Gene2", "Gene3", "Gene4"),
  SNP = c("SNP1", "SNP2", "SNP3", "SNP4"),
  pvalue = c(0.001, 0.04, 0.03, 0.0005))
pAdjustMethod <- "BH"
pAdjustThreshold <- 0.05
adjusted_result <- adjust_pvalues(example_data, pAdjustMethod,
pAdjustThreshold)

Build zinb model.

Description

Build zinb model.

Usage

buildZINB(counts)

Arguments

counts

a vector for gene expression.

Value

Four parameters

Examples

data(testGene)
gene <- unlist(testGene[1, ])
result <-buildZINB(gene)

callQTL: Uncover single-cell eQTLs exclusively using scRNA-seq data. A function designed to identify eQTLs from scRNA-seq data.

Description

callQTL: Uncover single-cell eQTLs exclusively using scRNA-seq data. A function designed to identify eQTLs from scRNA-seq data.

Usage

callQTL(
  eQTLObject,
  gene_ids = NULL,
  downstream = NULL,
  upstream = NULL,
  gene_mart = NULL,
  snp_mart = NULL,
  pAdjustMethod = "bonferroni",
  useModel = "zinb",
  pAdjustThreshold = 0.05,
  logfcThreshold = 0.1
)

Arguments

eQTLObject

An S4 object of class eQTLObject.
gene_ids

A gene ID or a list of gene IDS.
downstream

Being used to match SNPs within a base range defined by the start position of genes.
upstream

Being used to match SNPs within a base range defined by the end position of genes.
gene_mart

An object of class Mart representing the BioMart database to connect to. If NULL, the function will use the Ensembl Gene BioMart.
snp_mart

An object of class Mart representing the BioMart database to connect to. If NULL, the function will use the Ensembl SNP BioMart.
pAdjustMethod

Methods for p-value adjusting, one of 'bonferroni', 'holm', 'hochberg', 'hommel' or 'BH'. Default by 'bonferroni'.
useModel

Model for fitting dataframe, one of 'possion', 'zinb', or 'linear'.
pAdjustThreshold

Only SNP-Gene pairs with adjusted p-values meeting the threshold will be displayed. Default by 0.05.
logfcThreshold

Represents the minimum beta threshold for fitting SNP-Gene pairs.

Value

A dataframe, each row describes eQTL discovering result of a SNP-Gene pair.

Examples

data(testEQTL)
library(biomaRt)
gene_mart <- useEnsembl(biomart = "genes",
                        dataset = "hsapiens_gene_ensembl",
                        mirror = 'asia')
snp_mart <- useEnsembl(biomart = "snps",
                        dataset = "hsapiens_snp",
                        mirror = 'asia')
eqtl <- callQTL(
  eQTLObject = testEQTL,
  gene_ids = NULL,
  downstream = NULL,
  upstream = NULL,
  gene_mart = gene_mart,
  snp_mart = snp_mart,
  pAdjustMethod = 'bonferroni',
  useModel = 'linear',
  pAdjustThreshold = 0.05,
  logfcThreshold = 0.025
)

Check if the SNP ids in the input genotype matrix are valid.

Description

Check if the SNP ids in the input genotype matrix are valid.

Usage

checkSNPList(snpList, snp_mart = NULL, snpDataset = "hsapiens_snp")

Arguments

snpList

a list of SNPs id.
snp_mart

An object of class 'Mart' representing the BioMart database connect to for SNPs. If provided, this should be a 'Mart' object obtained by calling 'useEnsembl()', which allows specifying a mirror in case of connection issues. If 'NULL', the function will create and use a 'Mart' object pointing to the Ensembl SNP BioMart using the specified 'snpDataset' and a default mirror.
snpDataset

A character string specifying the SNP dataset to use from ENSEMBL. The default is 'hsapiens_snp' for human SNPs.

Value

SNP location dataframe

Examples

data(testSNP2)
snpList <- rownames(testSNP2)
snpDataset <- 'hsapiens_snp'
snps_loc <- checkSNPList(snpList = snpList,
                        snpDataset = snpDataset)

Normalize the gene expression matrix with CPM.

Description

'CPM_normalize()' scales an expression matrix using Counts Per Million (CPM) normalization, applying logarithm and scaling operations to adjust data.

Usage

CPM_normalize(expressionMatrix)

Arguments

expressionMatrix

Input raw gene expression matrix.

Value

A gene expression matrix after normalized.

Examples

data(testGene)
CPM_normalize(testGene)

Create gene location dataframe.

Description

Create gene location dataframe.

Usage

createGeneLoc(
  geneList,
  gene_mart = NULL,
  geneDataset = "hsapiens_gene_ensembl",
  OrgDb
)

Arguments

geneList

A gene id or a list of genes id.
gene_mart

An object of class 'Mart' representing the BioMart database connect to for gene. If provided, this should be a 'Mart' object obtained by calling 'useEnsembl()', which allows specifying a mirror in case of connection issues. If 'NULL', the function will create and use a 'Mart' object pointing to the Ensembl Gene BioMart using the specified 'geneDataset' and a default mirror.
geneDataset

A character string specifying the gene dataset to use from ENSEMBL. The default is "hsapiens_gene_ensembl" for human genes.
OrgDb

OrgDb name:"org.Hs.eg.db", "org.Mm.eg.db".

Value

data.frame

Examples

data(testGene)
geneList <- rownames(testGene)
library(GOSemSim)
library(biomaRt)
OrgDb <- load_OrgDb("org.Hs.eg.db")
gene_mart <- useEnsembl(biomart = "genes",
                        dataset = "hsapiens_gene_ensembl",
                        mirror = 'asia')
gene_loc <- createGeneLoc(geneList = geneList,
                          gene_mart = gene_mart,
                          OrgDb = OrgDb)

createObject:Create the eQTLObject. We next create a S4 object. The object serves as a container that contains both data (like the count matrix) and meta.data.

Description

createObject:Create the eQTLObject. We next create a S4 object. The object serves as a container that contains both data (like the count matrix) and meta.data.

Usage

createQTLObject(
  snpMatrix,
  genedata,
  biClassify = FALSE,
  species = NULL,
  group = NULL,
  ...
)

Arguments

snpMatrix

A genotype matrix where each row is one variant and each column is one sample, and the scoring method is 0/1/2/3.
genedata

A gene expression matrix or a Seurat object, or a SingleCellExperiment object.
biClassify

The user chooses whether to convert the counting method of the snpMatrix to 0/1/2, TRUE indicates conversion, and FALSE indicates no conversion, default is no conversion.
species

The species that the user wants to select, human or mouse.
group

Provided by Seurat's meta.data, such as celltypes, cellstatus and so on. By default, it is NULL.
...

other parameters

Value

eQTLObject

Examples

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

Create SNP location dataframe.

Description

Create SNP location dataframe.

Usage

createSNPsLoc(snpList, snp_mart = NULL, snpDataset = "hsapiens_snp")

Arguments

snpList

a list of SNPs id.
snp_mart

An object of class 'Mart' representing the BioMart database connect to for SNPs. If provided, this should be a 'Mart' object obtained by calling 'useEnsembl()', which allows specifying a mirror in case of connection issues. If 'NULL', the function will create and use a 'Mart' object pointing to the Ensembl SNP BioMart using the specified 'snpDataset' and a default mirror.
snpDataset

A character string specifying the SNP dataset to use from ENSEMBL. The default is 'hsapiens_snp' for human SNPs.

Value

data.frame

Examples

snpList <- c('rs546', 'rs549', 'rs568', 'rs665', 'rs672')
library(biomaRt)
snp_mart <- useEnsembl(biomart = "snps",
                        dataset = "hsapiens_snp",
                        mirror = 'asia')
snp_loc <- createSNPsLoc(snpList = snpList,
                         snp_mart = snp_mart)

Normalize the gene expression matrix with DESeq.

Description

'DESeq_normalize()'normalizes an expression matrix using the DESeq2 package.

Usage

DESeq_normalize(expressionMatrix)

Arguments

expressionMatrix

Input raw gene expression matrix.

Value

A gene expression matrix after normalized.

Examples

data(testGene)
DESeq_normalize(testGene)

Generate a boxplot of expression levels by SNP factor

Description

'draw_boxplot()' creates a boxplot visualizing expression levels across different SNP factors in the dataframe. It uses ggplot2 to produce a plot with customizable aesthetics for clarity and presentation.

Usage

draw_boxplot(df, unique_group)

Arguments

df

Data frames listed as gene expression data, genotype data, and groups
unique_group

name of unique group

Value

ggplot

Examples

set.seed(123)
counts_Ref <- rnorm(50, mean = 10, sd = 2)
counts_Alt <- rnorm(50, mean = 12, sd = 2)
i <- rep("GroupA", 100)
unique_group <- unique(i)
dataframe <- data.frame(
expression = c(counts_Ref, counts_Alt),
snp = c(rep("REF", length(counts_Ref)),
        rep("ALT", length(counts_Alt))), group = i)
dataframe$snp <- factor(dataframe$snp, levels = c("REF", "ALT"))
draw_boxplot(df = dataframe, unique_group = unique_group)

Generate a hist plot of expression levels by SNP factor.

Description

'draw_histplot()' generates histograms using ggplot2, displaying the distribution of expression values categorized by SNP type.

Usage

draw_histplot(df, unique_group)

Arguments

df

Data frames listed as gene expression data, genotype data, and groups
unique_group

name of unique group

Value

ggplot

Examples

set.seed(123)
counts_Ref <- rnorm(50, mean = 10, sd = 2)
counts_Alt <- rnorm(50, mean = 12, sd = 2)
i <- rep("GroupA", 100);unique_group <- unique(i)
dataframe <- data.frame(expression = c(counts_Ref, counts_Alt),
                        snp = c(rep("REF", length(counts_Ref)),
                        rep("ALT", length(counts_Alt))), group = i)
dataframe$snp <- factor(dataframe$snp, levels = c("REF", "ALT"))
draw_histplot(df = dataframe, unique_group = unique_group)

Create a combined plot with violin, boxplot, and scatter point overlay.

Description

'draw_QTLplot()' generates a combined plot using ggplot2, showing the distribution of expression values across different SNPs. It combines a violin plot, boxplot, and scatter points for each SNP category.

Usage

draw_QTLplot(df, unique_group)

Arguments

df

Data frames listed as gene expression data, genotype data, and groups
unique_group

name of unique group

Value

ggplot

Examples

set.seed(123)
counts_Ref <- rnorm(50, mean = 10, sd = 2)
counts_Alt <- rnorm(50, mean = 12, sd = 2)
i <- rep("GroupA", 100);unique_group <- unique(i)
dataframe <- data.frame(expression = c(counts_Ref, counts_Alt),
                        snp = c(rep("REF", length(counts_Ref)),
                        rep("ALT", length(counts_Alt))), group = i)
dataframe$snp <- factor(dataframe$snp, levels = c("REF", "ALT"))
draw_QTLplot(df = dataframe, unique_group = unique_group)

Generate a violin plot of expression levels by SNP factor

Description

'draw_violinplot()' creates a violin plot visualizing expression levels across different SNP factors in the dataframe. It uses ggplot2 to produce a plot with customizable aesthetics for clarity and presentation.

Usage

draw_violinplot(df, unique_group)

Arguments

df

Data frames listed as gene expression data, genotype data, and groups
unique_group

name of unique group

Value

ggplot

Examples

set.seed(123)
counts_Ref <- rnorm(50, mean = 10, sd = 2)
counts_Alt <- rnorm(50, mean = 12, sd = 2)
i <- rep("GroupA", 100);unique_group <- unique(i)
dataframe <- data.frame(expression = c(counts_Ref, counts_Alt),
                        snp = c(rep("REF", length(counts_Ref)),
                        rep("ALT", length(counts_Alt))), group = i)
dataframe$snp <- factor(dataframe$snp, levels = c("REF", "ALT"))
draw_violinplot(df = dataframe, unique_group = unique_group)

Class 'eQTLObject' The eQTLObject class is an R object designed to store data related to eQTL analysis, encompassing data lists, result data frames, and layers for biClassify, species, and grouping information.

Description

Class 'eQTLObject' The eQTLObject class is an R object designed to store data related to eQTL analysis, encompassing data lists, result data frames, and layers for biClassify, species, and grouping information.

Value

eQTLObject

Slots

rawData

A gene expression dataframe, the row names represent gene IDs and the column names represent cell IDs.

filterData

Gene expression matrix after normalizing.

eQTLResult

The result dataframe obtained the sc-eQTL results.

biClassify

The user chooses whether to convert the counting method of the snpMatrix to 0, 1, 2, TRUE indicates conversion, and FALSE indicates no conversion, default is no conversion.

species

The species that the user wants to select, human or mouse.

groupBy

Options for cell grouping, users can choose celltype, cellstatus, etc., depending on metadata.

useModel

model for fitting dataframe.

Filters data frame by absolute b-values, returning rows meeting or exceeding a threshold.

Description

Filters data frame by absolute b-values, returning rows meeting or exceeding a threshold.

Usage

filter_by_abs_b(result, logfcThreshold)

Arguments

result

Dataframe that contains gene-SNP pairs' information.
logfcThreshold

Represents the minimum beta threshold for fitting SNP-Gene pairs. Default by 0.1.

Value

A dataframe filtered by absolute b-values.

Examples

example_result <- data.frame(
  gene = c("Gene1", "Gene2", "Gene3", "Gene4"),
  SNP = c("SNP1", "SNP2", "SNP3", "SNP4"),
  b = c(-2.5, 1.0, -0.5, 3.0))
logfcThreshold <- 0.1
filtered_result <- filter_by_abs_b(example_result, logfcThreshold)

filterGeneSNP: Filter gene expression matrix and genotype matrix.

Description

filterGeneSNP: Filter gene expression matrix and genotype matrix.

Usage

filterGeneSNP(
  eQTLObject,
  snpNumOfCellsPercent = 10,
  expressionMin = 0,
  expressionNumOfCellsPercent = 10
)

Arguments

eQTLObject

An S4 object of class eQTLObject.
snpNumOfCellsPercent

Only SNPs where cells with each of the different genotypes (REF and ALT, or AA, Aa, and aa) individually account for at least 'snpNumOfCellsPercent' Default by 10.
expressionMin

threshold for valid gene expression levels, utilized alongside another parameter,expression.number.of.cells.Default by 0.
expressionNumOfCellsPercent

Only genes with expression levels exceeding 'expressionMin' in at least 'expressionNumOfCellsPercent' of cells are considered. The default value is 10.

Value

filtered matrices.

Examples

data(testSNP)
data(testGene)
eqtl <- createQTLObject(snpMatrix = testSNP, genedata = testGene)
eqtl <- normalizeGene(eqtl)
eqtl <- filterGeneSNP(eqtl,
  snpNumOfCellsPercent = 2,
  expressionMin = 0,
  expressionNumOfCellsPercent = 2)

Retrieve Cells by SNP Value

Description

This function extracts the names of cells from a SNP matrix that correspond to a specified value for a given SNP.

Usage

get_cell_groups(snpMatrix, SNPid, biClassify)

Arguments

snpMatrix

A matrix containing SNP data where rows represent SNPs and columns represent cells.
SNPid

A character string or numeric index representing the specific SNP of interest in the SNP matrix.
biClassify

The user chooses whether to convert the counting method of the snpMatrix to 0/1/2, TRUE indicates conversion, and FALSE indicates no conversion, default is no conversion.

Value

A list of cell names (column names of the SNP matrix) that correspond to the specified genotype value for the given SNP.

Examples

data(testSNP)
biClassify <- FALSE
get_cell_groups(testSNP, "1:632445", biClassify)

Extract Counts from an Expression Matrix

Description

This function retrieves expression counts for a specified gene from an expression matrix, based on the provided list of cells.

Usage

get_counts(expressionMatrix, Geneid, cells)

Arguments

expressionMatrix

A matrix containing gene expression data where rows represent genes and columns represent cells.
Geneid

A character string or numeric index representing the specific gene of interest in the expression matrix.
cells

A character vector of cell names (column names of the expression matrix) from which to extract counts for the specified gene.

Value

A numeric vector of expression counts for the specified gene in the selected cells.

Examples

data(testGene)
get_counts(testGene, "CNN2",
          c("CGGCAGTGTAGCCCTG", "GGAGGATTCCCGTTCA"))

Generic to access eQTLObject filter data

Description

Generic to access eQTLObject filter data

Usage

get_filter_data(x)

Arguments

x

A eQTLObject object.

Value

filtered matrices.

Examples

data(testEQTL)
  get_filter_data(testEQTL)

Method to access eQTLObject filter data

Description

Method to access eQTLObject filter data

Usage

## S4 method for signature 'eQTLObject'
get_filter_data(x)

Arguments

x

A eQTLObject object.

Value

filtered matrices.

Generic to access eQTLObject used model information

Description

Generic to access eQTLObject used model information

Usage

get_model_info(x)

Arguments

x

A eQTLObject object.

Value

used model information of eQTLObject.

Examples

data(testEQTL)
  get_model_info(testEQTL)

Method to access eQTLObject used model information

Description

Method to access eQTLObject used model information

Usage

## S4 method for signature 'eQTLObject'
get_model_info(x)

Arguments

x

A eQTLObject object.

Value

used model information of eQTLObject.

Generic to access eQTLObject raw data

Description

Generic to access eQTLObject raw data

Usage

get_raw_data(x)

Arguments

x

A eQTLObject object.

Value

raw data matrix.

Examples

data(testEQTL)
  get_raw_data(testEQTL)

Method to access eQTLObject raw data

Description

Method to access eQTLObject raw data

Usage

## S4 method for signature 'eQTLObject'
get_raw_data(x)

Arguments

x

A eQTLObject object.

Value

raw data matrix.

Generic to access the result of identifying eQTLs from scRNA-seq data

Description

Generic to access the result of identifying eQTLs from scRNA-seq data

Usage

get_result_info(x)

Arguments

x

A eQTLObject object.

Value

A dataframe.

Examples

data(testEQTL)
  get_result_info(testEQTL)

Method to access the result of identifying eQTLs from scRNA-seq data

Description

Method to access the result of identifying eQTLs from scRNA-seq data

Usage

## S4 method for signature 'eQTLObject'
get_result_info(x)

Arguments

x

A eQTLObject object.

Value

A dataframe.

Progress Bar for Model Analysis.

Description

This function initializes a progress bar for use in the 'linearModel' , 'poissonModel' and 'zinbModel' function. It is designed to provide feedback on the progress of the analysis by displaying the current step and a percentage completion.

Usage

initialize_progress_bar(total, k)

Arguments

total

The total number of steps or iterations for which the progress bar will be updated.
k

A label or identifier for the specific group or iteration for which the progress bar is being initialized.

Value

A 'progress_bar' object from the 'progress' package, which is used to track and display the progress.

Examples

unique_group <- c("CMP", "GMP")
total_snp_count <- 10  # assume each group have 100 SNP.
pb_model <- lapply(unique_group, function(k) {
    pb <- initialize_progress_bar(total = total_snp_count, k)
    for (i in seq_len(total_snp_count)) {
        Sys.sleep(0.1)  # assume progress time
        pb$tick()  # update pb
    }
})

Normalize the gene expression matrix with limma

Description

'limma_normalize()'normalizes an expression matrix using the quantile normalization method provided by the limma package.

Usage

limma_normalize(expressionMatrix)

Arguments

expressionMatrix

Input raw gene expression matrix.

Value

A gene expression matrix after normalized.

Examples

data(testGene)
limma_normalize(testGene)

Linear model fitting the gene expression matrix and genotype matrix.

Description

Linear model fitting the gene expression matrix and genotype matrix.

Usage

linearModel(
  eQTLObject,
  geneIDs,
  snpIDs,
  biClassify = FALSE,
  pAdjustMethod = "bonferroni",
  pAdjustThreshold = 0.05,
  logfcThreshold = 0.1
)

Arguments

eQTLObject

An S4 object of class eQTLObject.
geneIDs

Matching genes can be used to fit data.
snpIDs

Matching SNPs can be used to fit data.
biClassify

The user chooses whether to convert the counting method of the snpMatrix to 0/1/2, TRUE indicates conversion, and FALSE indicates no conversion, default is no conversion.
pAdjustMethod

Methods for p-value adjusting, one of "bonferroni", "holm", "hochberg", "hommel" or "BH". The default option is "bonferroni".
pAdjustThreshold

Only SNP-Gene pairs with adjusted p-values meeting the threshold will be displayed. Default by 0.05.
logfcThreshold

Represents the minimum beta threshold for fitting SNP-Gene pairs. Default by 0.1.

Value

Dataframe that contains gene-SNP pairs' information.

Examples

data(testEQTL)
Gene <- rownames(slot(testEQTL, "filterData")$expMat)
SNP <- rownames(slot(testEQTL, "filterData")$snpMat)
linearResult <- linearModel(
  eQTLObject = testEQTL,
  geneIDs = Gene,
  snpIDs = SNP,
  biClassify = FALSE,
  pAdjustMethod = "bonferroni",
  pAdjustThreshold = 0.05,
  logfcThreshold = 0.025)

Generic to access eQTLObject biclassify information

Description

Generic to access eQTLObject biclassify information

Usage

load_biclassify_info(x)

Arguments

x

A eQTLObject object.

Value

biclassify information of eQTLObject.

Examples

data(testEQTL)
  load_biclassify_info(testEQTL)

Method to access eQTLObject biclassify information

Description

Method to access eQTLObject biclassify information

Usage

## S4 method for signature 'eQTLObject'
load_biclassify_info(x)

Arguments

x

A eQTLObject object.

Value

biclassify information of eQTLObject.

Generic to access eQTLObject cell grouping information

Description

Generic to access eQTLObject cell grouping information

Usage

load_group_info(x)

Arguments

x

A eQTLObject object.

Value

A dataframe.

Examples

data(testEQTL)
  load_group_info(testEQTL)

Method to access eQTLObject cell grouping information

Description

Method to access eQTLObject cell grouping information

Usage

## S4 method for signature 'eQTLObject'
load_group_info(x)

Arguments

x

A eQTLObject object.

Value

A dataframe.

Generic to access eQTLObject species information

Description

Generic to access eQTLObject species information

Usage

load_species_info(x)

Arguments

x

A eQTLObject object.

Value

species information of eQTLObject.

Examples

data(testEQTL)
  load_species_info(testEQTL)

Method to access eQTLObject species information

Description

Method to access eQTLObject species information

Usage

## S4 method for signature 'eQTLObject'
load_species_info(x)

Arguments

x

A eQTLObject object.

Value

species information of eQTLObject.

Normalize the gene expression matrix with logNormalize method.

Description

'log_normalize()' transforms an expression matrix by applying logarithm and scaling operations to normalize data.

Usage

log_normalize(expressionMatrix)

Arguments

expressionMatrix

Input raw gene expression matrix.

Value

A gene expression matrix after normalized.

Examples

data(testGene)
log_normalize(testGene)

normalizeGene: Normalize the gene expression data.

Description

Gene expression matrix normalization is necessary to eliminate technical biases and variabilities, ensuring accurate and comparable analysis of gene expression data. Here we provide 'normalizeGene()'to normalize the data.

Usage

normalizeGene(eQTLObject, method = "logNormalize")

Arguments

eQTLObject

An S4 object of class eQTLObject.
method

Method for normalizing for gene expression dataframe, one of "logNormalize", "CPM", "TPM", "DESeq" or "limma"

Value

A normalized gene expression matrix.

Examples

data(testEQTL)
eqtl <- normalizeGene(testEQTL, method = "logNormalize")

Theme options for customized plots

Description

Theme options for customized plots

Usage

plots_theme_opts()

Value

A ggplot2 theme object with customized settings.

Examples

library(ggplot2)
data <- data.frame(
  x = c("A", "B", "C", "D", "E"),
  y = c(10, 20, 30, 40, 50))
ggplot(data, aes(x, y)) +
  geom_point() +
  plots_theme_opts()

Poisson model fitting the gene expression matrix and genotype matrix.

Description

Poisson model fitting the gene expression matrix and genotype matrix.

Usage

poissonModel(
  eQTLObject,
  geneIDs,
  snpIDs,
  biClassify = FALSE,
  pAdjustMethod = "bonferroni",
  pAdjustThreshold = 0.05,
  logfcThreshold = 0.1
)

Arguments

eQTLObject

An S4 object of class eQTLObject.
geneIDs

Matching genes can be used to fit data.
snpIDs

Matching SNPs can be used to fit data.
biClassify

The user chooses whether to convert the counting method of the snpMatrix to 0/1/2, TRUE indicates conversion, and FALSE indicates no conversion, default is FALSE.
pAdjustMethod

Methods for p-value adjusting, one of "bonferroni", "holm", "hochberg", "hommel" or "BH". The default option is "bonferroni".
pAdjustThreshold

Only SNP-Gene pairs with adjusted p-values meeting the threshold will be displayed. The default value is 0.05.
logfcThreshold

Represents the minimum beta threshold for fitting SNP-Gene pairs.

Value

Dataframe that contains gene-SNP pairs' information.

Examples

data(testEQTL)
Gene <- rownames(slot(testEQTL, "filterData")$expMat)
SNP <- rownames(slot(testEQTL, "filterData")$snpMat)
poissonResult <- poissonModel(
  eQTLObject = testEQTL,
  geneIDs = Gene,
  snpIDs = SNP,
  biClassify = FALSE,
  pAdjustMethod = "bonferroni",
  pAdjustThreshold = 0.05,
  logfcThreshold = 0.025
)

Process a matrix to extract a row and convert it to a data frame

Description

Process a matrix to extract a row and convert it to a data frame

Usage

process_matrix(id, matrix, name)

Arguments

id

The identifier for the row to be extracted from the matrix.
matrix

The input matrix from which the row will be extracted.
name

The column names for the resulting data frame.

Value

A data frame containing the extracted row and a column with the row names.

Examples

rownames <- c("CNN2", "TIGD2", "DTD2")
colnames <- c("Col1", "Col2", "Col3", "Col4")
matrix_data <- matrix(1:12, nrow = 3, ncol = 4,
  dimnames = list(rownames, colnames))
geneid <- "CNN2"
gene_mat <- process_matrix(geneid, matrix_data, "gene_mat")

Remove outliers from gene expression data and update cell lists

Description

remove_outliers() is a function designed to process gene expression data stored in an expression matrix. It identifies outliers within the data based on the MAD method and filters them out. The function updates specified cell lists by retaining only those cells that have non-outlier expression values for a specified gene.

Usage

remove_outliers(exprsMat, Geneid, A_cells, B_cells, C_cells = NULL)

Arguments

exprsMat

Input gene expression matrix
Geneid

Chosen gene id.
A_cells

A genotype cells
B_cells

B genotype cells
C_cells

C genotype cells

Value

a list of cells ids

Examples

## Mock expression matrix
set.seed(123)
exprsMat <- matrix(rnorm(200), nrow = 5)
rownames(exprsMat) <- paste0("Gene", 1:nrow(exprsMat))
colnames(exprsMat)  <- paste0("cell", 1:ncol(exprsMat))
A_cells <- colnames(exprsMat)[1:13] # Example A cell list
B_cells <- colnames(exprsMat)[14:26]  # Example B cell list
C_cells <- colnames(exprsMat)[27:40]  # Example C cell list
remove_outliers(exprsMat, "Gene1", A_cells, B_cells, C_cells)

Generic to set eQTLObject filter data

Description

Generic to set eQTLObject filter data

Usage

set_filter_data(x, value, name)

Arguments

x

A eQTLObject object.
value

The filtered data.
name

The matrix named 'name' is stored under the 'filterData' slot as an element within its list.

Value

eQTLObject.

Examples

data(testEQTL)
  data123 <- matrix(0, nrow = 3, ncol = 3)
  set_filter_data(testEQTL, data123, "expMat")

Method to set eQTLObject filter data

Description

Method to set eQTLObject filter data

Usage

## S4 method for signature 'eQTLObject'
set_filter_data(x, value, name)

Arguments

x

A eQTLObject object.
value

The filtered data.
name

The matrix named 'name' is stored under the 'filterData' slot as an element within its list.

Value

eQTLObject.

Examples

data(testEQTL)
  data123 <- matrix(0, nrow = 3, ncol = 3)
  set_filter_data(testEQTL, data123, "expMat")

Generic to set eQTLObject used model information

Description

Generic to set eQTLObject used model information

Usage

set_model_info(x, value)

Arguments

x

A eQTLObject object.
value

The used model information to set to eQTLObject.

Value

eQTLObject.

Examples

data(testEQTL)
  useModel <- "zinb"
  set_model_info(testEQTL, useModel)

Method to set eQTLObject used model information

Description

Method to set eQTLObject used model information

Usage

## S4 method for signature 'eQTLObject'
set_model_info(x, value)

Arguments

x

A eQTLObject object.
value

The used model information to set to eQTLObject.

Value

eQTLObject.

Examples

data(testEQTL)
  useModel <- "zinb"
  set_model_info(testEQTL, useModel)

Generic to set eQTLObject raw data

Description

Generic to set eQTLObject raw data

Usage

set_raw_data(x, value, name)

Arguments

x

A eQTLObject object.
value

The raw data.
name

The matrix named 'name' is stored under the 'rawData' slot as an element within its list.

Value

eQTLObject.

Examples

data(testEQTL)
  data123 <- matrix(0, nrow = 3, ncol = 3)
  set_raw_data(testEQTL, data123, "rawExpMat")

Method to set eQTLObject raw data

Description

Method to set eQTLObject raw data

Usage

## S4 method for signature 'eQTLObject'
set_raw_data(x, value, name)

Arguments

x

A eQTLObject object.
value

The raw data.
name

The matrix named 'name' is stored under the 'rawData' slot as an element within its list.

Value

eQTLObject.

Examples

data(testEQTL)
  data123 <- matrix(0, nrow = 3, ncol = 3)
  set_raw_data(testEQTL, data123, "rawExpMat")

Generic to set the result of identifying eQTLs from scRNA-seq data

Description

Generic to set the result of identifying eQTLs from scRNA-seq data

Usage

set_result_info(x, value)

Arguments

x

A eQTLObject object.
value

A dataframe, each row describes eQTL discovering result of a SNP-Gene pair.

Value

eQTLObject.

Examples

data(testEQTL)
  result <- matrix(0, nrow = 3, ncol = 3)
  set_result_info(testEQTL, result)

Method to set the result of identifying eQTLs from scRNA-seq data

Description

Method to set the result of identifying eQTLs from scRNA-seq data

Usage

## S4 method for signature 'eQTLObject'
set_result_info(x, value)

Arguments

x

A eQTLObject object.
value

A dataframe, each row describes eQTL discovering result of a SNP-Gene pair.

Value

eQTLObject.

Examples

data(testEQTL)
  result <- matrix(0, nrow = 3, ncol = 3)
  set_result_info(testEQTL, result)

Show Method for eQTLObject Class

Description

This method is to display information about an object of class eQTLObject. When called on an eQTLObject, it prints a descriptive message to the console

Usage

## S4 method for signature 'eQTLObject'
show(object)

Arguments

object

An S4 object of class eQTLObject.

Value

information of eQTLObject

Examples

data(testEQTL)
testEQTL

Test eqtl object

Description

An 'eqtlObject' created by the 'createQTLObject' function, where the raw expression matrix is normalized using 'normalizeGene()', and both the genotype matrix and the normalized gene expression matrix are filtered by 'filterGeneSNP()'.

testEQTL.rds is the RDS format versions of the original testEQTL.rda, providing the same normalized eQTL object for easier loading and use in R.

Usage

data(testEQTL)

data(testEQTL)

Format

A simple object.

A eqtlObject read by the 'readRDS' function.

Examples

data(testEQTL)
data(testEQTL)

Test Gene Expression Dataset

Description

A dataset containing example gene expression data for testing purposes. 100 rows and 2705 colnums. The row names represent gene IDs or SYMBOL and the column names represent cell IDs.

Usage

data(testGene)

Format

A simple matrix.

Examples

data(testGene)

Test SeuratObject

Description

A Seurat object for single-cell RNA-seq data.

testSeurat.rds datasets are the RDS format versions of the original testSeurat.rda files, providing the preprocessed Seurat object for easier loading and use in R.

Usage

data(testSeurat)

data(testSeurat)

Format

A object

A Seurat read by the 'readRDS' function.

Examples

data(testSeurat)
data(testSeurat)

Test Genotype Dataset

Description

A dataset containing single nucleotide variant data. 1000 rows and 2705 colnums. Each row is one variant and each column is one cell.

Usage

data(testSNP)

Format

A simple matrix.

Examples

data(testSNP)

Test Genotype Dataset

Description

A dataset containing single nucleotide variant data.500 rows and 500 colnums Each row is one variant and each column is one cell.

Usage

data(testSNP2)

Format

A simple matrix.

Examples

data(testSNP2)

Normalize the gene expression matrix with TPM

Description

'TPM_normalize()' scales an expression matrix using Transcripts Per Million (TPM) normalization, applying logarithm and scaling operations to adjust data based on library size.

Usage

TPM_normalize(expressionMatrix)

Arguments

expressionMatrix

Input raw gene expression matrix.

Value

A gene expression matrix after normalized.

Examples

data(testGene)
TPM_normalize(testGene)

visualizeQTL: Visualize the gene-snp pairs by group.

Description

visualizeQTL: Visualize the gene-snp pairs by group.

Usage

visualizeQTL(
  eQTLObject,
  SNPid,
  Geneid,
  groupName = NULL,
  plottype = "QTLplot",
  removeoutlier = FALSE
)

Arguments

eQTLObject

An S4 object of class eQTLObject.
SNPid

ID of SNP.
Geneid

ID of Gene.
groupName

Users can choose one or more than one single cell groups.
plottype

Types of plot,one of "QTLplot","violin","boxplot" or "histplot".
removeoutlier

Whether identify and remove the outliers. Default by FALSE.

Value

list

Examples

data(testEQTL)
## We have to call the eQTLs firstly using `callQTL()`.
eqtl <- callQTL(eQTLObject = testEQTL, useModel = "linear")
visualizeQTL(eQTLObject = eqtl,
SNPid = "1:632647",
Geneid = "RPS27",
groupName = NULL,
plottype = "QTLplot",
removeoutlier = FALSE)

Zinb model fitting the gene expression matrix.

Description

Zinb model fitting the gene expression matrix.

Usage

zinbModel(
  eQTLObject,
  geneIDs,
  snpIDs,
  biClassify = FALSE,
  pAdjustMethod = "bonferroni",
  pAdjustThreshold = 0.05
)

Arguments

eQTLObject

An S4 object of class eQTLObject.
geneIDs

Matching genes can be used to fit data.
snpIDs

Matching SNPs can be used to fit data.
biClassify

The user chooses whether to convert the counting method of the snpMatrix to 0/1/2, TRUE indicates conversion, and FALSE indicates no conversion, default is no conversion.
pAdjustMethod

Methods for p-value adjusting, one of 'bonferroni', 'holm', 'hochberg', 'hommel' or 'BH'. The default option is 'bonferroni'.
pAdjustThreshold

Only SNP-Gene pairs with adjusted p-values meeting the threshold will be displayed. Default by 0.05.

Value

Dataframe that contains gene-SNP pairs' information.

Examples

data(testEQTL)
Gene <- rownames(slot(testEQTL, 'filterData')$expMat)
SNP <- rownames(slot(testEQTL, 'filterData')$snpMat)
zinbResult <- zinbModel(
  eQTLObject = testEQTL,
  geneIDs = Gene,
  snpIDs = SNP,
  biClassify = FALSE,
  pAdjustMethod = 'bonferroni',
  pAdjustThreshold = 0.05)