Package 'RNAseqQC' reference manual

Title:	Quality Control for RNA-Seq Data
Description:	Functions for semi-automated quality control of bulk RNA-seq data.
Authors:	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 <[email protected]>
License:	Apache License (>= 2)
Version:	0.2.1
Built:	2025-02-10 06:02:44 UTC
Source:	https://github.com/frederikziebell/rnaseqqc

for a character vector x, check if all non-NA elements of x can be converted to numeric

Description

for a character vector x, check if all non-NA elements of x can be converted to numeric

Usage

all_numeric(x)
all_numeric(x)

Arguments

`x`	A character vector

Filter genes with low counts

Description

Filter genes with low counts

Usage

filter_genes(dds, min_count = 5, min_rep = 3)
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)

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)
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)
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)
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 `EnsDb` object.
`design`	The design formula specified in `DESeqDataSet()` To view all valid record IDs, run 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")


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)
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)

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)
plot_biotypes(dds)

Arguments

dds

A DESeqDataSet

Value

A ggplot object of the ggplot2 package.

Examples

plot_biotypes(T47D)

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)
plot_chromosome(vsd, chr, scale = FALSE, trunc_val = NULL)

Arguments

`vsd`	An object generated by `DESeq2::vst()`
`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")

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
)
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 `rownames(dds)` or of `rowData(dds)$gene_name`
`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 `colData(dds)`) to color points by.
`point_alpha`	alpha value of `geom_point()`
`point_rel_size`	relative size of `geom_point()`
`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")

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))
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)

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)
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)

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
)
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 `plot_pca()`
`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 `pca_res$loadings`) to color points by. Can also be 'pc_sign' to color by the sign of the loading (useful in combination with the square = TRUE parameter).
`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)
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)
plot_ma(de_res, dds, annotate_top_n = 5, highlight_genes = NULL)

Arguments

`de_res`	An object returned by `DESeq2::results()` or `DESeq2::lfcShrink()`
`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)

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,
  ...
)
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. `rownames(metadata)` must match `colnames(obj)`.
`color_by`	Variable by which to color points. Must be a column in metadata or in `colData(obj)`. Alternatively, it can be the name of a feature (a rowname of obj) or a gene name (an element of rowData(obj)$gene_name).
`shape_by`	Variable by which to color points. Must be a column in metadata or in `colData(obj)`.
`point_alpha`	alpha value of `geom_point()`
`point_rel_size`	relative size of `geom_point()`
`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)

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,
  ...
)
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. `rownames(metadata)` must match `colnames(obj)`.
`color_by`	Variable by which to color points. Must be a column in metadata or in `colData(obj)`. Alternatively, it can be the name of a feature (a rowname of obj) or a gene name (an element of rowData(obj)$gene_name).
`shape_by`	Variable by which to color points. Must be a column in metadata or in `colData(obj)`.
`point_alpha`	alpha value of `geom_point()`
`point_rel_size`	relative size of `geom_point()`
`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)

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)

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",
  ...
)
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 `colData(se)` to annotate samples
`anno_title`	The title of the color legend for `anno_vars`
`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)

library("DESeq2")
dds <- makeExampleDESeqDataSet(m=8, interceptMean=10)
vsd <- vst(dds)
plot_sample_clustering(vsd)

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, ...)
plot_sample_MAs(vsd, group, y_lim = 3, rasterise = FALSE, ...)

Arguments

`vsd`	An object generated by `DESeq2::vst()`
`group`	A grouping variable, must be a column of `colData(vsd)`
`y_lim`	Y-axis limits, the axis will run from `-y_lim` to `y_lim`
`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")

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 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)
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)

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,
  ...
)
plot_within_level_sample_MAs(
  vsd,
  group,
  level,
  y_lim = 4,
  rasterise = FALSE,
  ...
)

Arguments

`vsd`	An object generated by `DESeq2::vst()`
`group`	A grouping variable, must be a column of `colData(vsd)`
`level`	A level of the grouping variable
`y_lim`	Y-axis limits, the axis will run from `-y_lim` to `y_lim`
`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")

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"
)
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 `plotlist` argument of `cowplot::plot_grid`
`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)


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)

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
T47D

Format

A DESeqDataSet with 43576 rows (of genes) and 24 columns (of samples).

Source

doi:10.1101/2021.05.21.445138

Differential expression results corresponding to the T47D data set.

Description

Differential expression results corresponding to the T47D data set.

Usage

T47D_diff_testing
T47D_diff_testing

Format

A DESeqResults object with 36562 rows and 3 columns.

Source

See the 'data' vignette on how to reproduce this object.

Package 'RNAseqQC'

Help Index

for a character vector x, check if all non-NA elements of x can be converted to numeric

Description

Usage

Arguments

Filter genes with low counts

Description

Usage

Arguments

Value

Examples

Get all gene IDs in a DESeqDataSet for a given gene name.

Description

Usage

Arguments

Value

Examples

Make DESeqDataSet from counts matrix and metadata

Description

Usage

Arguments

Value

Examples

Create a mean-sd plot Make a scatterplot that shows for each gene its standard deviation versus mean.

Description

Usage

Arguments

Value

Examples

Plot number of counts per sample and biotype

Description

Usage

Arguments

Value

Examples

Plot gene expression along a chromosome

Description

Usage

Arguments

Value

Examples

Plot a gene

Description

Usage

Arguments

Value

Examples

Plot number of detected genes for each sample

Description

Usage

Arguments

Value

Examples

Plot the library complexity

Description

Usage

Arguments

Value

Examples

Plot loadings of a principal component

Description

Usage

Arguments

Value

Examples

MA-plot of a differential testing result

Description

Usage

Arguments

Value

Examples

Plot results of a principal component analysis

Description

Usage

Arguments

Details

Value

Examples

Plot matrix of PCA scatter plots