Package 'epistack'

Title: Heatmaps of Stack Profiles from Epigenetic Signals
Description: The epistack package main objective is the visualizations of stacks of genomic tracks (such as, but not restricted to, ChIP-seq, ATAC-seq, DNA methyation or genomic conservation data) centered at genomic regions of interest. epistack needs three different inputs: 1) a genomic score objects, such as ChIP-seq coverage or DNA methylation values, provided as a `GRanges` (easily obtained from `bigwig` or `bam` files). 2) a list of feature of interest, such as peaks or transcription start sites, provided as a `GRanges` (easily obtained from `gtf` or `bed` files). 3) a score to sort the features, such as peak height or gene expression value.
Authors: SACI Safia [aut], DEVAILLY Guillaume [cre, aut]
Maintainer: DEVAILLY Guillaume <[email protected]>
License: MIT + file LICENSE
Version: 1.13.0
Built: 2024-12-18 03:41:21 UTC
Source: https://github.com/bioc/epistack

Help Index

addBins()

Description

Add an optional bin metadata column to gr, to serve as annotations for the epistack plots.

Usage

addBins(rse, nbins = 5L, bin = NULL)

Arguments

rse

a SummarizedExperiment or a GRanges object.
nbins

an integer number, the number of bins.
bin

a vector containing pre-determined bins, in the same order as gr.

Details

nbins is taken into account only if bin is NULL. rse should be sorted first, usually with the addMetricAndArrangeGRanges() function. addBin(rse, bin = vec) is equivalent to rse$bin <- vec, while addBin(rse, nbins = 5) will create 5 bins of equal size based on rse order.

Value

the RangedSummarizedExperiment or GRanges object with a new bin metadata column

See Also

addMetricAndArrangeGRanges plotBinning

Examples

data("stackepi")
addBins(stackepi)

# 3 bins instead of 5
addBins(stackepi, nbins = 3)

# assign bins using a vector
addBins(stackepi, bin = rep(c("a", "b", "c"),
 length.out = length(stackepi)))

addMetricAndArrangeGRanges()

Description

Perform an inner join between a GRanges object and a data.frame. Sort the resulting GRanges based on a metric column.

Usage

addMetricAndArrangeGRanges(
  gr,
  order,
  gr_key = "name",
  order_key = "name",
  order_value = "exp",
  shuffle_tie = TRUE
)

Arguments

gr

a GRanges object.
order

a data.frame with at least two columns: keys and values.
gr_key

name of the gr metadata column containing unique names for each genomic region in gr. Usually gene names/id or peak id.
order_key

name of the order column that will be used as key for the inner join.
order_value

name of the order column that contain value used for sorting.
shuffle_tie

a boolean Value (TRUE / FALSE). When TRUE, shuffle the GRanges before sorting, mixing the ties.

Details

This utility function allow the addition of a metric column to genomic regions of interest. One of its common use case is to add gene expression values on a set of transcription start sites. The resulting GRanges object will only contain regions presents in both gr and order.

Value

a GRanges sorted in descending order.

Examples

data("stackepi_gr")
ramdomOrder <- data.frame(gene_id = stackepi_gr$gene_id,
   value = rnorm(length(stackepi_gr)))
addMetricAndArrangeGRanges(stackepi_gr,
   ramdomOrder, gr_key = "gene_id",
   order_key = "gene_id", order_value = "value")

addMetricAndArrangeRSE()

Description

Perform an inner join between a rangedSummarizedExperiment object and a data.frame. Sort the resulting rangedSummarizedExperiment based on a metric column.

Usage

addMetricAndArrangeRSE(
  rse,
  order,
  rse_key = "name",
  order_key = "name",
  order_value = "exp",
  shuffle_tie = TRUE
)

Arguments

rse

a rangedSummarizedExperiment object.
order

a data.frame with at least two columns: keys and values.
rse_key

name of the gr metadata column containing unique names for each genomic region in rowRanges(rse). Usually gene names/id or peak id.
order_key

name of the order column that will be used as key for the inner join.
order_value

name of the order column that contain value used for sorting.
shuffle_tie

a boolean Value (TRUE / FALSE). When TRUE, shuffle the GRanges before sorting, mixing the ties.

Details

This utility function allow the addition of a metric column to genomic regions of interest. One of its common use case is to add gene expression values on a set of transcription start sites. The resulting GRanges object will only contain regions presents in both rse and order.

Value

a rangedSummarizedExperiment sorted in descending order.

Examples

data("stackepi")
ramdomOrder <- data.frame(
   gene_id = SummarizedExperiment::rowRanges(stackepi)$gene_id,
   value = rnorm(length(stackepi))
)
addMetricAndArrangeRSE(stackepi,
   ramdomOrder, rse_key = "gene_id",
   order_key = "gene_id", order_value = "value")

GRanges2RSE()

Description

Convert objects from the old input format (GRanges object) to the new recommanded input format RangedSummarizedExperiment.

Usage

GRanges2RSE(gr, patterns, names = patterns)

Arguments

gr

a GRanges object with matrix embeded as metadata columns.
patterns

A character vector of column prefixes (can be regular expressions) that should match columns of gr.
names

specify the desired names of the assays (if different from patterns).

Details

Mostly used for backward compatibilities and unit testing.

Value

a RangedSummarizedExperiment.

Examples

data("stackepi_gr")
GRanges2RSE(stackepi_gr, patterns = c("window"))
GRanges2RSE(stackepi_gr, patterns = c("^window_"), names = c("DNAme"))

meanColor

Description

Return the average color of a vector of colors, computed in the RGB space.

Usage

meanColor(colors)

Arguments

colors

a vector of colors

Details

Input colors can be either in html or color name formats. The alpha channel is supported but optional.

Value

a single color value

See Also

redimMatrix

Examples

meanColor(c("#000000FF", "#FFFFFF00", "#FFFF00FF", "#FF0000FF"))

# works with color names
meanColor(c("blue", "red"))

# Mix color names and HTML codes
meanColor(c("blue", "red", "#FFFF00FF"))

# works without alpha channel in inputs (but outputs an alpha channel):
meanColor(c("#000000", "#FFFFFF", "#FFFF00", "#FF0000"))

plotAverageProfile()

Description

Plot the average stack profiles +/- error (sd or sem). If a bin column is present in rowRanges(rse), one average profile is drawn for each bin.

Usage

plotAverageProfile(
  rse,
  assay = NULL,
  x_labels = c("Before", "Anchor", "After"),
  palette = colorRampPalette(c("#DF536B", "black", "#61D04F")),
  alpha_for_se = 0.25,
  error_type = c("sd", "sem", "ci95"),
  reversed_z_order = FALSE,
  ylim = NULL,
  y_title = NULL,
  pattern = NULL
)

Arguments

rse

a RangedSummarizedExperiment input. Aletrnatively: can be a GRanges object (for backward compatibility, pattern will be required).
assay

specify the name of the assay to plot, that should match one of assayNames(rse).
x_labels

x-axis labels.
palette

A vector of colors, or a function that returns a palette of n colors.
alpha_for_se

the transparency (alpha) value for the error band.
error_type

can be either "sd" (standard deviation), "sem" (standard error of the mean), or "ci95" (95% confidence interval). Default: "sd".
reversed_z_order

should the z-order of the curves be reversed (i.e. first or last bin on top?)
ylim

a vector of two numbers corresponding to the y-limits of the plot.
y_title

the y-axis title.
pattern

only if rse is of class GRanges. A single character that should match metadata of rse (can be a regular expression).

Value

Display a plot.

Examples

data("stackepi")
plotAverageProfile(stackepi)

plotBinning()

Description

Plot a vertical color bar of the bin column.

Usage

plotBinning(
  rse,
  target_height = 650,
  palette = colorRampPalette(c("#DF536B", "black", "#61D04F"))
)

Arguments

rse

a RangedSummarizedExperiment input with a column bin in rowRanges(rse). Alternatively (for backward compatibility), a GRanges object or any object such as rse$bin exists.
target_height

an integer, the approximate height (in pixels) of the final plot. Used to avoid overplotting artefacts.
palette

A vector of colors, or a function that returns a palette of n colors.

Value

Display a plot.

Examples

data("stackepi")
rse <- stackepi
rse <- addBins(rse, nbins = 3)
plotBinning(rse)

gr2 <- data.frame(bin = rep(c(1,2,3,4), each = 5))
plotBinning(gr2, palette = colorRampPalette(c("blue4", "forestgreen", "coral3", "goldenrod")))

plotBoxMetric()

Description

Plot distribution of a metric values as boxplots depending of bins. If the bin is absent from gr, a single boxplot is drawn.

Usage

plotBoxMetric(
  rse,
  metric = "expr",
  title = "Metric",
  trans_func = function(x) x,
  ylim = NULL,
  ylab = "metric",
  palette = colorRampPalette(c("#DF536B", "black", "#61D04F"))
)

Arguments

rse

a RangedSummarizedExperiment input. Aletrnatively: can be a GRanges object (for backward compatibility).
metric

name of the column in rse metadata containing scores.
title

title of the plot.
trans_func

A function to transform value of x before ploting. Useful to apply log10 transformation (i.e. with trans_func = function(x) log10(x+1)).
ylim

limit of the y axis; format: ylim = c(min, max)
ylab

y-axis title
palette

A vector of colors, or a function that returns a palette of n colors.

Value

Display a plot.

Examples

data("stackepi")
plotBoxMetric(
       stackepi,
       trans_func = function(x) x,
       metric = "exp",
       title = "Metric"
  )

plotEpistack()

Description

Given a list of genomic regions, epigenetic signals surrounding these regions, and a score for each regions, plot epigenetic stacks depending on the score. An optional bin column allow the grouping of several genomic regions to produce average profiles per bins.

Usage

plotEpistack(
  rse,
  assays = NULL,
  tints = "gray",
  titles = NULL,
  legends = "",
  main = NULL,
  x_labels = c("Before", "Anchor", "After"),
  zlim = c(0, 1),
  ylim = NULL,
  metric_col = "exp",
  metric_title = "Metric",
  metric_label = "metric",
  metric_ylab = NULL,
  metric_transfunc = function(x) x,
  bin_palette = colorRampPalette(c("#DF536B", "black", "#61D04F")),
  npix_height = 650,
  n_core = 1,
  high_mar = c(2.5, 0.6, 4, 0.6),
  low_mar = c(2.5, 0.6, 0.3, 0.6),
  error_type = c("ci95", "sd", "sem"),
  reversed_z_order = FALSE,
  rel_widths = c(score = 0.35, bin = 0.08, assays = 0.35),
  rel_heights = c(1, 0.14, 0.3),
  patterns = NULL,
  ...
)

Arguments

rse

a RangedSummarizedExperiment input. Aletrnatively: can be a GRanges object (for backward compatibility, patterns will be required).
assays

specify the name(s) and order of assay(s) to plot. A vector of names that should match assayNames(rse).
tints

a vector of colors to tint the heatmaps. Can alos be a function returning n colors, or a list of such palette functions.
titles

titles of each heatmap. Defaults to assays.
legends

legend names for the epistacks.
main

Main title for the figure.
x_labels

a character vector of length 3 used as x-axis labels.
zlim

the minimum and maximum z values the heatmap. Format: zlim = c(min, max). zlim can also be specified of as a list of pairs of limits, on for each assay.
ylim

limits of the y axis for bottom plots. ylim can also be specified of as a list of pairs of limits, on for each assay. Format: ylim = c(min, max)
metric_col

a character, name of a column in gr such as expression value, peak height, pvalue, fold change, etc.
metric_title

title to be display on the leftmost plots.
metric_label

label of the leftmost plots.
metric_ylab

y axis label of the top left plot.
metric_transfunc

a function to transform value of metric_col before plotting. Useful to apply log10 transformation (i.e. with trans_func = function(x) log10(x+1)).
bin_palette

A vector of colors, or a function that returns a palette of n colors. Used to color average profiles per bin in the bottom plots.
npix_height

The matrix height is reduced to this number of rows before plotting. Useful to limit overplotting artefacts. It should roughtly be set to the pixel height in the final heatmaps
n_core

number of core used to speedup the martrix resizing.
high_mar

a vector of numerical values corresponding to the margins of the top figures. c(bottom, left, top, right)
low_mar

a vector of numerical values corresponding to the margins of the bottom figures. c(bottom, left, top, right)
error_type

error_type, can be either "sd" (standard deviation), "sem" (standard error of the mean), or "ci95" (95% confidence interval). Default: "ci95".
reversed_z_order

For the bottom panels: should the z-order of the curves be reversed (i.e. first or last bin on top)?
rel_widths

A named vector of three elements of relative panel widths: score is the left-most panel, bin is the optionnal binning panels, and assays are the panels of the stacked-matrices. Default to c(score = .35, bin = .08, assays = .35)
rel_heights

A vector of three elements of relative panel heights. Default to c(1, .14, .3)
patterns

only if rse is of class GRanges. A character vector of column prefixes (can be regular expressions) that should match columns of rse.
...

Arguments to be passed to par such as cex

Details

This function produce a comprehensive figure including epigenetic heatmaps and average epigenetic profiles from a well formated RangedSummarizedExperiment object with expected rowData metadata columns. It scales resonably well up to hundreds of thousands of genomic regions.

The visualisation is centered on an anchor, a set of genomic coordinated that can be transcription start sites or peak center for example. Anchor coordinates are those of the GRanges used as a rowData in the input RangedSummarizedExperiment object (hereafter rse).

Anchors are plotted from top to bottom in the same order as in rse. One should sort rse before plotting if needed.

rse's rowData should have a metric column that is used in the leftmost plots. The name of the metric column must be specified to metric_col. The metric can be transformed before plotting if needed using the metric_transfunc parameter.

The matrix or matrices used to display the heatmap(s) should be passed as assay(s) in rse. Such matrix can be obtained using EnrichedHeatmap::normalizeToMatrix() for example. The assay names are then specified through assays.

If an optionnal bin column is present in rse's rowData, it will be used to group genomic regions to performed average profile per bins in the bottom plots.

Epistack are multipanel plots build using layout(). Margins for the panels can be specified using high_mar and low_mar parameters if needed, especially to avoid text overlaps. The default value should be appropriate in most situations. Individual component can be plotted using severa epistack functions such has plotStackProfile() or plotAverageProfile().

Plotting more than > 1000 regions can lead to overplotting issued as well as some plotting artefacts (such as horizontal white strips). Both issues can be resolved with fidling with the npix_height parameter. npix_height should be smaller than the number of regions, and in the same order of magnitude of the final heatmap height in pixels. Last minutes call to the redimMatrix() function will hapen before plotting using npix_height as target height. Parameter n_core is passed to redimMatrix() to speed up the down-scaling.

The input can also be a GRanges object for backward compatibility. See GRanges2RSE. patterns would then be required.

Value

Display a plot.

See Also

plotStackProfile, plotAverageProfile, redimMatrix, normalizeToMatrix, addMetricAndArrangeGRanges, addBins

Examples

data("stackepi")
plotEpistack(stackepi,
    metric_col = "exp",
    ylim = c(0, 1),
    metric_transfunc = function(x) log10(x+1))

plotMetric()

Description

Plot a vertical line chart of the metric column, in the same order as the input.

Usage

plotMetric(
  x,
  trans_func = function(x) x,
  title = "Metric",
  ylim = NULL,
  xlab = NULL,
  ylab = NULL
)

Arguments

x

a numeric vector.
trans_func

a function to transform x values before ploting. Useful to apply log10 transformation (i.e. with trans_func = function(x) log10(x+1)).
title

Title of the plot.
ylim

limit of the y axis; format: ylim = c(min, max)
xlab

x-axis title
ylab

y-axis title

Value

Display a plot.

See Also

plotEpistack, plotBoxMetric

Examples

data("stackepi")
plotMetric(SummarizedExperiment::rowRanges(stackepi)$exp)

plotStackProfile()

Description

Display a heatmap of an epigenetic track centered at genomic anchors such as Transcription Start Sites or peak center.

Usage

plotStackProfile(
  rse,
  assay = NULL,
  x_labels = c("Before", "Anchor", "After"),
  title = "",
  zlim = NULL,
  palette = function(n) grDevices::hcl.colors(n, rev = TRUE),
  target_height = 650,
  summary_func = function(x) mean(x, na.rm = TRUE),
  n_core = 1,
  pattern = NULL
)

Arguments

rse

a RangedSummarizedExperiment input. Aletrnatively: can be a GRanges object (for backward compatibility, pattern will be required).
assay

specify the name of the assay to plot, that should match one of assayNames(rse).
x_labels

a character vectors of length 3 used to label the x-axis.
title

The title of the heatmap
zlim

The minimum and maximum z values to match color to values. Format: zlim = c (min, max)
palette

a palette of color, (i.e. a function of parameter n that should retrun n colors).
target_height

The matrix height is reduced to this number of rows before plotting. Useful to limit overplotting artefacts. It should roughtly be set to the pixel height in the final heatmap.
summary_func

function passed to redimMatrix(). Usualy mean, but can be set to median or max for sparse matrices.
n_core

multicore option, passed to redimMatrix().
pattern

only if rse is of class GRanges. A character vector of length 1 of a column prefixe (can be regular expressions) that should match columns of rse.

Details

The visualisation is centered on an anchor, a set of genomic coordinated that can be transcription start sites or peak center for example. Anchor coordinates are those of the RangedSummarizedExperiment object used as an input (hereafter rse).

Anchors are plotted from top to bottom in the same order as in rse. One should sort rse before plotting if needed.

The matrix used to display the heatmap should be passed as assay of rse. Such matrix can be obtained using EnrichedHeatmap::normalizeToMatrix() for example.

This function scale reasonnably wells up to hundred thousands of regions. Overplotting issues are solved by last-minute reduction of the matrix size using redimMatrix().

Value

Display a plot.

See Also

plotAverageProfile, plotEpistack, normalizeToMatrix, plotStackProfileLegend

Examples

data("stackepi")
plotStackProfile(stackepi,
                   target_height = 650,
                   zlim = c(0, 1),
                   palette = colorRampPalette(c("white", "dodgerblue", "black")),
                   title = "DNA methylation")

plotStackProfileLegend()

Description

Utility function to plot the corresponding legend key of plotStackProfile()'s plots.

Usage

plotStackProfileLegend(
  zlim,
  palette = colorRampPalette(c("white", "grey", "black")),
  title = NA
)

Arguments

zlim

the limits of the values to be displayed. Format: c(min, max)
palette

a palette of color, (i.e. a function of parameter n that should retrun n colors).
title

an optionnal title to be display bellow the color legend.

Value

Display a plot.

See Also

plotStackProfile

Examples

plotStackProfileLegend(zlim = c(0, 2),
    palette = colorRampPalette(c("white", "grey", "black")))

redimMatrix()

Description

Reduce the input matrix size by applying a summary function on cells to be fused.

Usage

redimMatrix(
  mat,
  target_height = 100,
  target_width = 100,
  summary_func = function(x) mean(x, na.rm = TRUE),
  output_type = 0,
  n_core = 1
)

Arguments

mat

the input matrix.
target_height

height of the output matrix (should be smaller than or equal to nrow(mat)).
target_width

width of the output matrix (should be smaller than or equal to ncol(mat)).
summary_func

how to summerize cells? A function such has mean, median, max, or meanColors.
output_type

Type of the output, to be passed to vapply's FUN.VALUE.
n_core

number of core to use for parallel processing.

Details

This function is used to reduce matrix right before plotting them in order to avoid overplotting issues as well as other plotting artefacts.

Value

a resized matrix of size target_width x target_height where the summary_fun was apply to adjacent cells.

See Also

meanColor

Examples

data("stackepi")
mat <- SummarizedExperiment::assay(stackepi, "DNAme")
dim(mat)
smallMat <- redimMatrix(mat, target_height = 10, target_width = ncol(mat))
dim(smallMat)

# changing the summary function
mat <- matrix(sample(1:40,100,replace=TRUE),nrow=10,ncol=10)
dim(mat)
smallMat <- redimMatrix(mat, target_height = 5, target_width = ncol(mat),
   summary_func = function(x) max(x, na.rm = TRUE))
dim(smallMat)

# working with colors
colmat <- matrix(
    c("red", "red", "blue", "blue", "red", "blue", "blue", "green"),
    ncol = 2
)
redimMatrix(colmat, target_height = 2, target_width = 2,
            summary_func = meanColor, output_type = "color")

epistack example and test dataset

Description

DNA methylation profiles (from MBD-seq data) arround transcription start sites of the 693 chr18 genes annotated on the pig genome (Sscrofa11.1), as well as gene expression levels in Transcript Per Million (TPM) measured by RNA-seq in the same duodenum sample.

Usage

data("stackepi")

Format

A RangedSummarizedExperiment of the 693 rows, 2 rows metadata columns, and one assay containing the DNA methylation signal.

Source

This dataset was generated from ENSEMBL annotation data and data generated by our lab (publicly available soon).

epistack backward compatibility dataset

Description

DNA methylation profiles (from MBD-seq data) arround transcription start sites of the 693 chr18 genes annotated on the pig genome (Sscrofa11.1), as well as gene expression levels in Transcript Per Million (TPM) measured by RNA-seq in the same duodenum sample.

Usage

data("stackepi_gr")

Format

A GRanges of the 693 rows and 54 metadata columns, kept for unit-testing backward-compatibility.

Source

This dataset was generated from ENSEMBL annotation data and data generated by our lab (publicly available soon).

See Also

GRanges2RSE