| Title: | Quality Control for RNA-Seq Data |
| Version: | 0.2.1 |
| Description: | Functions for semi-automated quality control of bulk RNA-seq data. |
| License: | Apache License (≥ 2) |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.3.1 |
| Depends: | R (≥ 4.0) |
| Imports: | AnnotationHub, AnnotationFilter, BiocGenerics, ensembldb, circlize, DESeq2, SummarizedExperiment, ComplexHeatmap, matrixStats, grid, ggpointdensity, ggrepel, ggplot2, cowplot, patchwork, purrr, dplyr, stringr, tidyr, tibble, tidyselect, magrittr |
| Suggests: | rmarkdown, knitr, recount3, apeglm, ggrastr, gghighlight |
| VignetteBuilder: | knitr |
| URL: | https://github.com/frederikziebell/RNAseqQC |
| BugReports: | https://github.com/frederikziebell/RNAseqQC/issues |
| NeedsCompilation: | no |
| Packaged: | 2024-07-15 14:16:36 UTC; fxz40010 |
| Author: | Frederik Ziebell [cre],
Frederik Ziebell 
[aut],
GlaxoSmithKline Research & Development Limited [cph] (GlaxoSmithKline
Research & Development Limited; registered address: 980 Great West
Road, Brentford, Middlesex TW8 9GS, United Kingdom) |
| Maintainer: | Frederik Ziebell <f_ziebell@web.de> |
| Repository: | CRAN |
| Date/Publication: | 2024-07-15 14:40:02 UTC |
RNAseqQC: Quality Control for RNA-Seq Data
Description
Functions for semi-automated quality control of bulk RNA-seq data.
Author(s)
Maintainer: Frederik Ziebell f_ziebell@web.de
Authors:
Frederik Ziebell frederik.x.ziebell@gsk.com (ORCID)
Other contributors:
GlaxoSmithKline Research & Development Limited (GlaxoSmithKline Research & Development Limited; registered address: 980 Great West Road, Brentford, Middlesex TW8 9GS, United Kingdom) [copyright holder]
See Also
Useful links:
Report bugs at https://github.com/frederikziebell/RNAseqQC/issues
The T47D cell line data of RNA-seq experiment GSE89888
Description
The dataset contains the read counts of experiment GSE89888 in which T47D cells with different mutation statuses were treated with E2 (estradiol) or vehicle.
Usage
T47D
Format
A DESeqDataSet with 43576 rows (of genes) and 24 columns (of samples).
Source
Differential expression results corresponding to the T47D data set.
Description
Differential expression results corresponding to the T47D data set.
Usage
T47D_diff_testing
Format
A DESeqResults object with 36562 rows and 3 columns.
Source
See the 'data' vignette on how to reproduce this object.
for a vector x, check if all non-NA elements of x can be converted to numeric
Description
for a vector x, check if all non-NA elements of x can be converted to numeric
Usage
all_numeric(x)
Arguments
x |
A non-numeric vector |
Filter genes with low counts
Description
Filter genes with low counts
Usage
filter_genes(dds, min_count = 5, min_rep = 3)
Arguments
dds |
A DESeqDataSet |
min_count, min_rep |
keep genes with at least min_count counts in at least min_rep replicates |
Value
A DESeq2::DESeqDataSet object with only those genes that meet the filter criteria.
Examples
library("DESeq2")
dds <- makeExampleDESeqDataSet()
filter_genes(dds)
Get all gene IDs in a DESeqDataSet for a given gene name.
Description
Get all gene IDs in a DESeqDataSet for a given gene name.
Usage
get_gene_id(gene_name, dds)
Arguments
gene_name |
A gene name |
dds |
A DESeqDataSet |
Value
A character vector
Examples
get_gene_id("HBA1", T47D)
Make DESeqDataSet from counts matrix and metadata
Description
Make DESeqDataSet from counts matrix and metadata
Usage
make_dds(counts, metadata, ah_record, design = ~1)
Arguments
counts |
The genes x samples counts matrix with row names. At least one row name must be an ENSEMBL gene ID, since gene annotation is done via the ENSEMBL database. |
metadata |
data.frame of sample information. Order of rows corresponds to the order of columns in the counts matrix. |
ah_record |
ID of AnnotationHub record used to retrieve an |
design |
The design formula specified in library(AnnotationHub) mcols(AnnotationHub()) %>% as_tibble(rownames="ah_record") %>% filter(rdataclass=="EnsDb") |
Value
A DESeq2::DESeqDataSet object containing the counts matrix and metadata.
Examples
library("DESeq2")
count_mat <- counts(T47D)
meta <- data.frame(colData(T47D))
dds <- make_dds(counts = count_mat, metadata = meta, ah_record = "AH89426")
Create a mean-sd plot Make a scatterplot that shows for each gene its standard deviation versus mean.
Description
Create a mean-sd plot Make a scatterplot that shows for each gene its standard deviation versus mean.
Usage
mean_sd_plot(vsd)
Arguments
vsd |
A DESeqTransform object |
Value
A ggplot object of the ggplot2 package that contains the mean-sd plot.
Examples
library("DESeq2")
dds <- makeExampleDESeqDataSet(interceptMean=10, n=5000)
vsd <- vst(dds)
mean_sd_plot(vsd)
Plot number of counts per sample and biotype
Description
Plot the total number of counts for each sample and the major classes of ENSEMBL gene biotypes (protein coding, lncRNA, etc.)
Usage
plot_biotypes(dds)
Arguments
dds |
A DESeqDataSet |
Value
A ggplot object of the ggplot2 package.
Examples
plot_biotypes(T47D)
Plot gene expression along a chromosome
Description
Plot gene expression along a chromosome
Usage
plot_chromosome(vsd, chr, scale = FALSE, trunc_val = NULL)
Arguments
vsd |
An object generated by |
chr |
A string denoting a chromosome as annotated by ENSEMBL, e.g. '1', '2', 'X', 'Y', 'MT' |
scale |
Whether to scale the columns of the heatmap |
trunc_val |
Truncate the expression matrix to this value prior to plotting. This is useful if some very high expression values dominate the heatmap. By default, the heatmap is truncated to expression values at most 3 standard deviations from the mean. |
Value
A Heatmap-class object of the ComplexHeatmap package that contains the heatmap of expression values.
Examples
library("DESeq2")
chr1 <- T47D[which(mcols(T47D)$chromosome=="1"),]
vsd <- vst(chr1)
plot_chromosome(vsd, chr="1")
Plot a gene
Description
Plot a gene
Usage
plot_gene(
gene,
dds,
x_var = NULL,
color_by = NULL,
point_alpha = 0.7,
point_rel_size = 2,
show_plot = TRUE
)
Arguments
gene |
A gene ID or gene name, i.e. an element of |
dds |
a DESeqDataSet |
x_var |
Variable to plot on the x-axis. If NULL, then each sample is plotted separately. |
color_by |
Variable (column in |
point_alpha |
alpha value of |
point_rel_size |
relative size of |
show_plot |
Whether to show the plot or not |
Value
The function displays the plot and returns invisible the data frame of expression values and colData annotation for the gene.
Examples
library("DESeq2")
set.seed(1)
dds <- makeExampleDESeqDataSet()
colData(dds)$type <- c("A","A","A","B","B","B")
colData(dds)$patient <- c("1","1","2","2","3","3")
dds <- estimateSizeFactors(dds)
plot_gene("gene1", dds)
plot_gene("gene1", dds, x_var="patient", color_by="type")
Plot number of detected genes for each sample
Description
For specified thresholds, the number of detected genes is shown for each sample.
Usage
plot_gene_detection(dds, thresholds = c(3, 10, 20, 50))
Arguments
dds |
A DESeqDataSet |
thresholds |
Vector of thresholds for which the number of genes with counts greater or equal than the thresholds is plotted |
Value
A ggplot object of the ggplot2 package that contains the gene detection plot.
Examples
library("DESeq2")
set.seed(1)
dds <- makeExampleDESeqDataSet()
plot_gene_detection(dds)
Plot the library complexity
Description
Plot per sample the fraction of genes, versus the fraction of total counts.
Usage
plot_library_complexity(dds, show_progress = TRUE)
Arguments
dds |
A DESeqDataSet |
show_progress |
Whether to show a progress bar of the computation. |
Value
A ggplot object of the ggplot2 package that contains the library complexity plot.
Examples
library("DESeq2")
set.seed(1)
dds <- makeExampleDESeqDataSet()
plot_library_complexity(dds)
Plot loadings of a principal component
Description
Plot loadings of a principal component
Usage
plot_loadings(
pca_res,
PC = 1,
square = FALSE,
color_by = NULL,
annotate_top_n = 0,
highlight_genes = NULL,
show_plot = TRUE
)
Arguments
pca_res |
A result returned from |
PC |
Number of the principal component to plot |
square |
Whether to plot squared loadings. The squared loading is equal to the fraction of variance explained by the respective feature in the given principal component. |
color_by |
Variable (column in |
annotate_top_n |
Annotate the top n features with positive or negative loading |
highlight_genes |
Vector of gene names or gene IDs to highlight on the plot (overwrites top_n annotation) |
show_plot |
Whether to show the plot |
Value
The function displays the loadings plot and returns invisible a list of the plot, the data.frame of the PCA loadings.
Examples
set.seed(1)
data <- matrix(rnorm(100*6), ncol=6)
data <- t(t(data)+c(-1, -1.1, -1.2, 1, 1.1, 1.2))
pca_res <- plot_pca(data)
plot_loadings(pca_res)
MA-plot of a differential testing result
Description
MA-plot of a differential testing result
Usage
plot_ma(de_res, dds, annotate_top_n = 5, highlight_genes = NULL)
Arguments
de_res |
An object returned by |
dds |
The DESeqDataSet that was used to build the ´de_res´ object. This is needed for gene name annotation. |
annotate_top_n |
Annotate the top n significant genes by fold change (up- and down-regulated) |
highlight_genes |
Vector of gene names or gene IDs to highlight on the plot (overwrites top_n annotation) |
Value
A ggplot object of the ggplot2 package that contains the MA-plot. The plot shows three classes of points: Light gray points are genes with low counts that are removed from the analysis by independent filtering. Darker gray points are not significant genes that show a density map to visualize where the majority of non-significant points are located. Finally, red point show significant genes.
Examples
library("DESeq2")
set.seed(1)
dds <- makeExampleDESeqDataSet(n=1500, m=6, betaSD=.3, interceptMean=6)
rowData(dds)$gene_name <- rownames(dds)
dds <- DESeq(dds)
de_res <- results(dds)
plot_ma(de_res, dds)
Plot results of a principal component analysis
Description
Plot results of a principal component analysis
Usage
plot_pca(
obj,
PC_x = 1,
PC_y = 2,
n_feats = 500,
scale_feats = FALSE,
na_frac = 0.3,
metadata = NULL,
color_by = NULL,
shape_by = NULL,
point_alpha = 0.7,
point_rel_size = 2,
show_plot = TRUE,
rasterise = FALSE,
...
)
Arguments
obj |
A (features x samples) matrix or SummarizedExperiment object |
PC_x |
The PC to show on the x-axis. |
PC_y |
The PC to show on the y-axis. |
n_feats |
Number of top-variable features to include. |
scale_feats |
Whether to scale the features. |
na_frac |
Only consider features with the stated maximum fraction of NAs or NaNs. NA/NaNs will be mean-imputed for PCA. |
metadata |
A data.frame used for annotating samples. |
color_by |
Variable by which to color points. Must be a column in metadata or in |
shape_by |
Variable by which to color points. Must be a column in metadata or in |
point_alpha |
alpha value of |
point_rel_size |
relative size of |
show_plot |
Whether to show the plot or not |
rasterise |
Whether to rasterise the point, using ggrastr. |
... |
Other parameters passed on to ggrastr::rasterise |
Details
If the metadata or colData of obj contain a column colname, this colum will be removed in the $pca_data slot,
because this column contains the colnames of the data matrix. Similarly, for the $loadings slot, the column rowname is
reserved for the rownames of the data matrix.
Value
The function displays the plot and returns invisible a list of the plot, the data.frame to make the plot, the vector of percentages of variance explained and the loadings matrix.
Examples
set.seed(1)
data <- matrix(rnorm(100*6), ncol=6)
data <- t(t(data)+c(-1, -1.1, -1.2, 1, 1.1, 1.2))
plot_pca(data)
Plot matrix of PCA scatter plots
Description
Plot matrix of PCA scatter plots
Usage
plot_pca_scatters(
obj,
n_PCs = min(10, nrow(obj), ncol(obj)),
show_var_exp = T,
n_feats = 500,
scale_feats = FALSE,
na_frac = 0.3,
metadata = NULL,
color_by = NULL,
shape_by = NULL,
point_alpha = 0.7,
point_rel_size = 2,
transpose = FALSE,
rasterise = FALSE,
...
)
Arguments
obj |
A (features x samples) matrix or SummarizedExperiment object |
n_PCs |
Number of principal components to plot |
show_var_exp |
Whether to show a plot of the percentage of variance explained by each PC in the bottom left corner. |
n_feats |
Number of top-variable features to include. |
scale_feats |
Whether to scale the features. |
na_frac |
Only consider features with the stated maximum fraction of NAs or NaNs. NA/NaNs will be mean-imputed for PCA. |
metadata |
A data.frame used for annotating samples. |
color_by |
Variable by which to color points. Must be a column in metadata or in |
shape_by |
Variable by which to color points. Must be a column in metadata or in |
point_alpha |
alpha value of |
point_rel_size |
relative size of |
transpose |
Wheter to transpose the whole matrix of scatter plots |
rasterise |
Whether to rasterise the points using ggrastr. |
... |
Other parameters passed on to ggrastr::rasterise |
Value
The function displays the scatter plots of the PCs
Examples
set.seed(1)
data <- matrix(rnorm(100*6), ncol=6)
data <- t(t(data)+c(-1, -1.1, -1.2, 1, 1.1, 1.2))
plot_pca_scatters(data)
MA plots of samples
Description
For each level of the grouping variable, the gene-wise median over all samples is computed to obtain a reference sample. Then, each sample is plotted against the reference.
Usage
plot_sample_MAs(vsd, group, y_lim = 3, rasterise = FALSE, ...)
Arguments
vsd |
An object generated by |
group |
A grouping variable, must be a column of |
y_lim |
Y-axis limits, the axis will run from |
rasterise |
Whether to rasterise the points using ggrastr. |
... |
Other parameters passed on to ggrastr::rasterise |
Value
A list of ggplot objects of the ggplot2 package, with each element corresponding to one MA-plot.
Examples
library("DESeq2")
set.seed(1)
dds <- makeExampleDESeqDataSet(n=1000, m=4, interceptMean=10)
colData(dds)$type <- c("A","A","B","B")
vsd <- vst(dds)
plot_sample_MAs(vsd, group="type")
Plot clustering of samples in a distance heatmap
Description
Plot clustering of samples in a distance heatmap
Usage
plot_sample_clustering(
se,
n_feats = 500,
anno_vars = NULL,
anno_title = "group",
distance = "euclidean",
...
)
Arguments
se |
A SummarizedExperiment object. |
n_feats |
Number of top-variable features (genes) to consider |
anno_vars |
Character vector of columns in |
anno_title |
The title of the color legend for |
distance |
The type of distance metric to consider. Either 'euclidean', 'pearson' or 'spearman' |
... |
Other arguments passed on to ComplexHeatmap::Heatmap() |
Value
A Heatmap-class object of the ComplexHeatmap package that contains the heatmap of pairwise sample distances.
Examples
library("DESeq2")
dds <- makeExampleDESeqDataSet(m=8, interceptMean=10)
vsd <- vst(dds)
plot_sample_clustering(vsd)
Plot total counts per sample
Description
Plot the distribution of the total number of counts per sample as histogram.
Usage
plot_total_counts(dds, n_bins = 50)
Arguments
dds |
A DESeqDataSet |
n_bins |
Number of histogram bins |
Value
A ggplot object of the ggplot2 package that contains the histogram of total counts per sample.
Examples
library("DESeq2")
set.seed(1)
dds <- makeExampleDESeqDataSet(m=30)
plot_total_counts(dds)
Plot correlations of samples within a level of a group
Description
For the given level, the gene-wise median over all samples is computed to obtain a reference sample. Then, each sample is plotted against the reference as MA-plot.
Usage
plot_within_level_sample_MAs(
vsd,
group,
level,
y_lim = 4,
rasterise = FALSE,
...
)
Arguments
vsd |
An object generated by |
group |
A grouping variable, must be a column of |
level |
A level of the grouping variable |
y_lim |
Y-axis limits, the axis will run from |
rasterise |
Whether to rasterise the points using ggrastr. |
... |
Other parameters passed on to ggrastr::rasterise |
Value
A list of ggplot objects of the ggplot2 package that contains for each sample of the specified level the the sample vs reference MA-plot.
Examples
library("DESeq2")
set.seed(1)
dds <- makeExampleDESeqDataSet(n=1000, m=4, interceptMean=10)
colData(dds)$type <- c("A","A","B","B")
vsd <- vst(dds)
plot_within_level_sample_MAs(vsd, group="type", level="A")
Save list of plots to PDF
Description
This function takes a list of plots as input and makes a pdf with ncol x nrow plots per page.
Usage
save_plots_to_pdf(
plots,
file = "plots.pdf",
ncol,
nrow,
subfig_width = subfig_height * 16/9,
subfig_height = 2.5,
legend_position = "original"
)
Arguments
plots |
List of plots that is passed to the |
file |
file where the plots are saved |
ncol |
number of columns per page for the grid of plots |
nrow |
number of rows per page for the grid of plots |
subfig_width |
width of a plot of the grid in inches |
subfig_height |
height of a plot of the grid in inches |
legend_position |
either 'original' if the original legend of each sub-plot is shown, 'none', if no legend should be shown in any of the sub-plots, 'bottom', if no legend should be shown in the sub plots and one shared legend at the bottom or 'right', which is same as 'bottom', but shown on the right |
Value
The function returns nothing but is called for it's side effect, which is to save a pdf of plots to the filesystem.
Examples
library("ggplot2")
manuf <- unique(mpg$manufacturer)
plots <- lapply(manuf, function(x){
df <- mpg[mpg$manufacturer==x,]
ggplot(df, aes(cty, hwy)) +
geom_point() +
labs(title=x)
})
save_plots_to_pdf(plots, ncol=3, nrow=2)