| Type: | Package |
| Title: | CRISPR Screen and Gene Expression Differential Analysis |
| Version: | 1.1.1 |
| Description: | Provides analytical methods for analyzing CRISPR screen data at different levels of gene expression. Multi-component normal mixture models and EM algorithms are used for modeling. |
| Depends: | R(≥ 3.5.0), limma |
| Imports: | stats, mixtools, ggplot2, dplyr, ggsci, ggridges, ggprism |
| Suggests: | knitr, rmarkdown |
| License: | Apache License (== 2.0) |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.1 |
| VignetteBuilder: | knitr, rmarkdown |
| NeedsCompilation: | no |
| Packaged: | 2024-02-27 02:41:26 UTC; yu18 |
| Author: | Lianbo Yu [aut, cre], Yue Zhao [aut], Kevin R. Coombes [aut], Lang Li [aut] |
| Maintainer: | Lianbo Yu <Lianbo.Yu@osumc.edu> |
| Repository: | CRAN |
| Date/Publication: | 2024-02-27 06:10:02 UTC |
Fitting multi-component normal mixture models by R package mixtools
Description
The function normalmixEM in R package mixtools is employed for fitting multi-component normal mixture models.
Usage
EMFit(x, k0, mean_constr, sd_constr, npara, d0)
Arguments
x |
A numeric vector |
k0 |
Number of components in the normal mixture model |
mean_constr |
A constrain on means of components |
sd_constr |
A constrain on standard deviations of components |
npara |
Number of parameters |
d0 |
Number of times for fitting mixture model using different starting values |
Value
Normal mixture model fit and BIC value of the log-likelihood
Calculating a significance score of a gene based on the corresponding sgRNAs' p-values of the gene.
Description
Code was adapted from R package gscreend.
Usage
alphaBeta(pvec)
Arguments
pvec |
A numeric vector of p-values. |
Value
A min value of the kth smallest value based on the beta distribution B(k, n-k+1), where the n is the number of probabiliteis in the vector. This min value is the significance score of the gene.
Calculating gene-level log fold ratios
Description
Log fold ratios of all sgRNAs of a gene are averaged to obtain the gene level log fold ratio.
Usage
calculateGeneLFC(lfcs, genes)
Arguments
lfcs |
A numeric vector containing log fold change of sgRNAs. |
genes |
A character string containing gene names corresponding to sgRNAs. |
Value
A numeric vector containing log fold ratio of genes.
Calculating gene level p-values using modified robust rank aggregation (alpha-RRA method) on sgRNAs' p-values
Description
Code was adapted from R package gscreend. The alpha-RRA method is adapted from MAGeCK.
Usage
calculateGenePval(pvec, genes, alpha, nperm = 20)
Arguments
pvec |
A numeric vector containing p-values of sgRNAs. |
genes |
A character string containing gene names corresponding to sgRNAs. |
alpha |
A numeric number denoting the alpha cutoff (i.e. 0.05). |
nperm |
Number of permutations, default is 20 |
Value
A list with four elements: 1) a list of genes with their p-values; 2) a numeric matrix of rho null, each column corresponding to a different number of sgRNAs per gene; 3)a numeric vector of rho; 4) a numeric vector of number of sgRNAs per gene.
2D density contour plot of gene log2 fold ratios against gene expression levels
Description
This function generates a scatter plot with 2D density contour of log2 fold ratios of sgRNAs against the corresponding gene expression levels.
Usage
densityPlot(data, ...)
Arguments
data |
A data frame from the output of preparePlotData function |
... |
Other graphical parameters |
Value
No return value
Generating the null distribution of the significance score of a gene.
Description
Code was adapted from R package gscreend.
Usage
makeRhoNull(n, p, nperm)
Arguments
n |
An integer representing sgRNA number of a gene. |
p |
A numeric vector which contains the percentiles of the p-values that meet the cut-off (alpha). |
nperm |
Number of permutation runs. |
Value
A numric vector which contains all the significance scores (rho) of genes generated by a permutation test where the sgRNAs are randomly assigned to genes.
CRISPR screen data of cell line MDA-MB-231.
Description
A dataset containing the expression data of sgRNAs in a CRISPR screen experiment of cell line MDA-MB-231.
Usage
mda231
Format
A data frame with a list of two elements:
- sgRNA
Raw Read counts of sgRNAs
- negene
A list of non-essential genes
Median normalization of sgRNA counts
Description
This function adjusts sgRNA counts by the median ratio method. The normalized sgRNA read counts are calculated as the raw read counts devided by a size factor. The size factor is calcuated as the median of all size factors caculated from negative control sgRNAs (eg., sgRNAs corresponding to non-targeting or non-essential genes).
Usage
medianNormalization(data, control)
Arguments
data |
A numeric matrix containing raw read counts of sgRNAs with rows corresponding to sgRNAs and columns correspondings to samples. |
control |
A numeric matrix containing raw read counts of negative control sgRNAs with rows corresponding to sgRNAs and columns corresponding to samples. Sample ordering is the same as in data. |
Value
A list with two elements: 1) size factors of all samples; 2) normalized counts of sgRNAs.
Examples
count <- matrix(rnbinom(5000 * 6, mu=500, size=3), ncol = 6)
colnames(count) = paste0("sample", 1:6)
rownames(count) = paste0("sgRNA", 1:5000)
control <- count[1:100,]
normalizedcount <- medianNormalization(count, control)
Performing empirical Bayes modeling on limma results
Description
This function perform an empirical Bayes modeling on log fold ratios and return the posterior log fold ratios.
Usage
normalMM(data, theta0, n.b = 5, d = 10)
Arguments
data |
A numeric matrix containing limma results and log2 gene expression levels that has a column nameed 'lfc' and a column named 'exp.level.log2' |
theta0 |
Standard deviation of log2 fold changes under permutations |
n.b |
Number of bins, default is 5 bins |
d |
Number of times for fitting mixture model using different starting values, default is 10 |
Value
A numeric matrix containing limma results, RNA expression levels, posterior log2 fold ratio, log p-values, and estimates of mixture model
Modeling CRISPR data with a permutation test between conditions by R package limma
Description
The lmFit function in R package limma is employed for group comparisons under permutations.
Usage
permuteLimma(data, design, contrast.matrix, nperm)
Arguments
data |
A numeric matrix containing log2 expression level of sgRNAs with rows corresponding to sgRNAs and columns to samples. |
design |
A design matrix with rows corresponding to samples and columns to coefficients to be estimated. |
contrast.matrix |
A matrix with columns corresponding to contrasts. |
nperm |
Number of permutations |
Value
A numeric matrix containing log2 fold changes with permutations
Examples
y <- matrix(rnorm(1000*6),1000,6)
condition <- gl(2,3,labels=c("Control","Baseline"))
design <- model.matrix(~ 0 + condition)
contrast.matrix <- makeContrasts("conditionControl-conditionBaseline",levels=design)
fit <- permuteLimma(y,design,contrast.matrix,20)
Prepare data for density plot and ridge plot
Description
Input a data frame with each gene one row, and geneID, geneLFC, geneFDR as columns. This function will stratify genes into five groups based on their FDR levels: <=0.001, (0.001,0.01], (0.01,0.05], (0.05,0.5], (0.5,1]
Usage
preparePlotData(data, gene.fdr)
Arguments
data |
A data frame containing each gene in one row, and at least three columns with geneID, geneLFC, and geneFDR. |
gene.fdr |
A numeric variable (column) in the data frame, corresponding to the gene level FDR |
Value
A data frame based on the original data frame, with an additional column "group" indicating which FDR group this gene belongs to.
Density ridgeline plot of gene expression levels for different FDR groups.
Description
This function generates a density ridgeline plot of gene expression levels for different FDR groups.
Usage
ridgePlot(data, ...)
Arguments
data |
A data frame from the output of preparePlotData function |
... |
Other graphical parameters |
Value
No return value
Modeling CRISPR screen data by R package limma
Description
The lmFit function in R package limma is employed for group comparisons.
Usage
runLimma(data, design, contrast.matrix)
Arguments
data |
A numeric matrix containing log2 expression levels of sgRNAs with rows corresponding to sgRNAs and columns corresponding to samples. |
design |
A design matrix with rows corresponding to samples and columns corresponding to coefficients to be estimated. |
contrast.matrix |
A matrix with columns corresponding to contrasts. |
Value
A data frame with rows corresponding to sgRNAs and columns corresponding to limma results
Examples
y <- matrix(rnorm(1000*6),1000,6)
condition <- gl(2,3,labels=c("Treatment","Baseline"))
design <- model.matrix(~ 0 + condition)
contrast.matrix <- makeContrasts("conditionTreatment-conditionBaseline",levels=design)
limma.fit <- runLimma(y,design,contrast.matrix)
Scatter plot of log2 fold ratios against gene expression levels
Description
This function generates a scatter plot of log2 fold ratios of sgRNAs against the corresponding gene expression levels.
Usage
scatterPlot(data, fdr, ...)
Arguments
data |
A numeric matrix from the output of normalMM function |
fdr |
A level of false discovery rate |
... |
Other graphical parameters |
Value
No return value