| Title: | Interactive Shiny App for Bulk Sequencing Data |
| Version: | 1.1.0 |
| Maintainer: | Ilias Moutsopoulos <im383@cam.ac.uk> |
| Description: | Given an expression matrix from a bulk sequencing experiment, pre-processes it and creates a shiny app for interactive data analysis and visualisation. The app contains quality checks, differential expression analysis, volcano and cross plots, enrichment analysis and gene regulatory network inference, and can be customised to contain more panels by the user. |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| URL: | https://github.com/Core-Bioinformatics/bulkAnalyseR |
| BugReports: | https://github.com/Core-Bioinformatics/bulkAnalyseR/issues |
| RoxygenNote: | 7.2.3 |
| Depends: | R (≥ 4.0) |
| Imports: | ggplot2, shiny, gprofiler2, edgeR, DESeq2, stats, ggrepel, utils, RColorBrewer, ComplexHeatmap, circlize, grid, shinyWidgets, shinyjqui, dplyr, magrittr, ggforce, rlang, glue, preprocessCore, matrixStats, noisyr, tibble, ggnewscale, ggrastr, GENIE3, visNetwork, DT, scales, shinyjs, tidyr, shinyLP, UpSetR, stringr, ggVennDiagram |
| Suggests: | rmarkdown, knitr, shinythemes, BiocManager, AnnotationDbi, org.Hs.eg.db, org.Mm.eg.db, readxl, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2022-12-15 11:49:47 UTC; emouts |
| Author: | Ilias Moutsopoulos [aut, cre], Eleanor Williams [aut, ctb], Irina Mohorianu [aut, ctb] |
| Repository: | CRAN |
| Date/Publication: | 2022-12-15 12:20:02 UTC |
Pipe operator
Description
See magrittr::%>% for details.
Usage
lhs %>% rhs
Arguments
lhs |
A value or the magrittr placeholder. |
rhs |
A function call using the magrittr semantics. |
Value
The result of calling rhs(lhs).
Perform differential expression (DE) analysis on an expression matrix
Description
This function performs DE analysis on an expression using edgeR or DESeq2, given a vector of sample conditions.
Usage
DEanalysis_edger(expression.matrix, condition, var1, var2, anno)
DEanalysis_deseq2(expression.matrix, condition, var1, var2, anno)
Arguments
expression.matrix |
the expression matrix; rows correspond to genes and
columns correspond to samples; usually preprocessed by
|
condition |
a vector of the same length as the number of columns of expression.matrix, containing the sample conditions; this is usually the last column of the metadata |
var1, var2 |
conditions (contained in condition) to perform DE between; note that DESeq2 requires at least two replicates per condition |
anno |
annotation data frame containing a match between the row names
of the expression.matrix (usually ENSEMBL IDs) and the gene names that
should be rendered within the app and in output files; this object is
created by |
Value
A tibble with the differential expression results for all genes. Columns are
gene_id (usually ENSEMBL ID matching one of the rows of the expression matrix)
gene_name (name matched through the annotation)
log2exp (average log2(expression) of the gene across samples)
log2FC (log2(fold-change) of the gene between conditions)
pval (p-value of the gene being called DE)
pvalAdj (adjusted p-value using the Benjamini Hochberg correction)
Examples
expression.matrix.preproc <- as.matrix(read.csv(
system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"),
row.names = 1
))[1:100, 1:4]
anno <- AnnotationDbi::select(
getExportedValue('org.Mm.eg.db', 'org.Mm.eg.db'),
keys = rownames(expression.matrix.preproc),
keytype = 'ENSEMBL',
columns = 'SYMBOL'
) %>%
dplyr::distinct(ENSEMBL, .keep_all = TRUE) %>%
dplyr::mutate(NAME = ifelse(is.na(SYMBOL), ENSEMBL, SYMBOL))
edger <- DEanalysis_edger(
expression.matrix = expression.matrix.preproc,
condition = rep(c("0h", "12h"), each = 2),
var1 = "0h",
var2 = "12h",
anno = anno
)
deseq <- DEanalysis_edger(
expression.matrix = expression.matrix.preproc,
condition = rep(c("0h", "12h"), each = 2),
var1 = "0h",
var2 = "12h",
anno = anno
)
# DE genes with log2(fold-change) > 1 in both pipelines
intersect(
dplyr::filter(edger, abs(log2FC) > 1, pvalAdj < 0.05)$gene_name,
dplyr::filter(deseq, abs(log2FC) > 1, pvalAdj < 0.05)$gene_name
)
Generate the DE panel of the shiny app
Description
These are the UI and server components of the DE panel of the
shiny app. It is generated by including 'DE' in the panels.default argument
of generateShinyApp.
Usage
DEpanelUI(id, metadata, show = TRUE)
DEpanelServer(id, expression.matrix, metadata, anno)
Arguments
id |
the input slot that will be used to access the value |
metadata |
a data frame containing metadata for the samples contained
in the expression.matrix; must contain at minimum two columns:
the first column must contain the column names of the expression.matrix,
while the last column is assumed to contain the experimental conditions
that will be tested for differential expression; a list (of the same
length as modality) can be provided if #' |
show |
whether to show the panel or not; default is TRUE; there for compatibility with specifying panels to show |
expression.matrix |
the expression matrix; rows correspond to genes and
columns correspond to samples; usually preprocessed by
|
anno |
annotation data frame containing a match between the row names
of the expression.matrix (usually ENSEMBL IDs) and the gene names that
should be rendered within the app and in output files; this object is
created by |
Value
The UI and Server components of the shiny module, that can be used within the UI and Server definitions of a shiny app.
Generate the DE plot plot panel of the shiny app
Description
These are the UI and server components of the DE plot panel of the
shiny app. It is generated by including 'DEplot' in the panels.default argument
of generateShinyApp.
Usage
DEplotPanelUI(id, show = TRUE)
DEplotPanelServer(id, DEresults, anno)
Arguments
id |
the input slot that will be used to access the value |
show |
whether to show the panel or not; default is TRUE; there for compatibility with specifying panels to show |
DEresults |
differential expression results output from DEpanelServer; a reactive list with slots 'DEtable' (all genes), 'DEtableSubset' (only DE genes), 'lfcThreshold' and 'pvalThreshold' |
anno |
annotation data frame containing a match between the row names
of the expression.matrix (usually ENSEMBL IDs) and the gene names that
should be rendered within the app and in output files; this object is
created by |
Value
The UI and Server components of the shiny module, that can be used within the UI and Server definitions of a shiny app.
Generate the DE summary panel of the shiny app
Description
These are the UI and server components of the Heatmap panel of the
shiny app. It is generated by including 'DEsummary' in the panels.default argument
of generateShinyApp.
Usage
DEsummaryPanelUI(id, metadata, show = TRUE)
DEsummaryPanelServer(id, expression.matrix, metadata, DEresults, anno)
Arguments
id |
the input slot that will be used to access the value |
metadata |
a data frame containing metadata for the samples contained
in the expression.matrix; must contain at minimum two columns:
the first column must contain the column names of the expression.matrix,
while the last column is assumed to contain the experimental conditions
that will be tested for differential expression; a list (of the same
length as modality) can be provided if #' |
show |
whether to show the panel or not; default is TRUE; there for compatibility with specifying panels to show |
expression.matrix |
the expression matrix; rows correspond to genes and
columns correspond to samples; usually preprocessed by
|
DEresults |
differential expression results output from DEpanelServer; a reactive list with slots 'DEtable' (all genes), 'DEtableSubset' (only DE genes), 'lfcThreshold' and 'pvalThreshold' |
anno |
annotation data frame containing a match between the row names
of the expression.matrix (usually ENSEMBL IDs) and the gene names that
should be rendered within the app and in output files; this object is
created by |
Value
The UI and Server components of the shiny module, that can be used within the UI and Server definitions of a shiny app.
Generate the GRN cis integration panel of the shiny app
Description
These are the UI and server components of the GRN cis integration
panel of the shiny app. It is generated by including at least 1 row in the
cis.integration parameter of generateShinyApp.
Usage
GRNCisPanelUI(id, reference.table.name, comparison.table.name)
GRNCisPanelServer(
id,
expression.matrix,
anno,
coord.table.reference,
coord.table.comparison,
seed = 13
)
Arguments
id |
the input slot that will be used to access the value |
reference.table.name |
Name for reference expression matrix and coordinate table |
comparison.table.name |
Name for comparison coordinate table |
expression.matrix |
the expression matrix; rows correspond to genes and
columns correspond to samples; usually preprocessed by
|
anno |
annotation data frame containing a match between the row names
of the expression.matrix (usually ENSEMBL IDs) and the gene names that
should be rendered within the app and in output files; this object is
created by |
coord.table.reference |
Table of coordinates corresponding to rows of expression.matrix |
coord.table.comparison |
Table of coordinates to compare against coord.table.reference |
seed |
Random seed to create reproducible GRNs |
Value
The UI and Server components of the shiny module, that can be used within the UI and Server definitions of a shiny app.
Generate the GRN custom integration panel of the shiny app
Description
These are the UI and server components of the GRN custom integration
panel of the shiny app. It is generated by including at least 1 row in the
custom.integration parameter of generateShinyApp.
Usage
GRNCustomPanelUI(id, title = "GRN with custom integration", show = TRUE)
GRNCustomPanelServer(
id,
expression.matrix,
anno,
comparison.table,
DEresults = NULL,
seed = 13
)
Arguments
id |
the input slot that will be used to access the value |
title |
Name for custom panel instance |
show |
whether to show the panel or not; default is TRUE; there for compatibility with specifying panels to show |
expression.matrix |
the expression matrix; rows correspond to genes and
columns correspond to samples; usually preprocessed by
|
anno |
annotation data frame containing a match between the row names
of the expression.matrix (usually ENSEMBL IDs) and the gene names that
should be rendered within the app and in output files; this object is
created by |
comparison.table |
Table linking rows of expression.matrix to custom information, for example miRNAs or transcription factors. |
DEresults |
differential expression results output from DEpanelServer; a reactive list with slots 'DEtable' (all genes), 'DEtableSubset' (only DE genes), 'lfcThreshold' and 'pvalThreshold' |
seed |
Random seed to create reproducible GRNs |
Value
The UI and Server components of the shiny module, that can be used within the UI and Server definitions of a shiny app.
Generate the GRN trans integration panel of the shiny app
Description
These are the UI and server components of the GRN trans integration
panel of the shiny app. It is generated by including at least 1 row in the
trans.integration parameter of generateShinyApp.
Usage
GRNTransPanelUI(id, reference.table.name, comparison.table.name)
GRNTransPanelServer(
id,
expression.matrix,
anno,
anno.comparison,
expression.matrix.comparison,
tablenames,
seed = 13
)
Arguments
id |
the input slot that will be used to access the value |
expression.matrix |
the expression matrix; rows correspond to genes and
columns correspond to samples; usually preprocessed by
|
anno |
annotation data frame containing a match between the row names
of the expression.matrix (usually ENSEMBL IDs) and the gene names that
should be rendered within the app and in output files; this object is
created by |
anno.comparison |
annotation data frame containing a match between the row names
of the comparison expression matrix and the names that
should be rendered within the app and in output files.
The structure matches the anno table created in
|
expression.matrix.comparison |
Additional expression matrix to integrate. Column names must match column names from expression.matrix. |
tablenames, reference.table.name, comparison.table.name |
Names for reference and comparison expression tables. |
seed |
Random seed to create reproducible GRNs |
Value
The UI and Server components of the shiny module, that can be used within the UI and Server definitions of a shiny app.
Generate the GRN panel of the shiny app
Description
These are the UI and server components of the GRN panel of the
shiny app. It is generated by including 'GRN' in the panels.default argument
of generateShinyApp.
Usage
GRNpanelUI(id, metadata, show = TRUE)
GRNpanelServer(id, expression.matrix, metadata, anno)
Arguments
id |
the input slot that will be used to access the value |
metadata |
a data frame containing metadata for the samples contained
in the expression.matrix; must contain at minimum two columns:
the first column must contain the column names of the expression.matrix,
while the last column is assumed to contain the experimental conditions
that will be tested for differential expression; a list (of the same
length as modality) can be provided if #' |
show |
whether to show the panel or not; default is TRUE; there for compatibility with specifying panels to show |
expression.matrix |
the expression matrix; rows correspond to genes and
columns correspond to samples; usually preprocessed by
|
anno |
annotation data frame containing a match between the row names
of the expression.matrix (usually ENSEMBL IDs) and the gene names that
should be rendered within the app and in output files; this object is
created by |
Value
The UI and Server components of the shiny module, that can be used within the UI and Server definitions of a shiny app.
Generate the QC panel of the shiny app
Description
These are the UI and server components of the QC panel of the
shiny app. It is generated by including 'QC' in the panels.default argument
of generateShinyApp.
Usage
QCpanelUI(id, metadata, show = TRUE)
QCpanelServer(id, expression.matrix, metadata, anno)
Arguments
id |
the input slot that will be used to access the value |
metadata |
a data frame containing metadata for the samples contained
in the expression.matrix; must contain at minimum two columns:
the first column must contain the column names of the expression.matrix,
while the last column is assumed to contain the experimental conditions
that will be tested for differential expression; a list (of the same
length as modality) can be provided if #' |
show |
whether to show the panel or not; default is TRUE; there for compatibility with specifying panels to show |
expression.matrix |
the expression matrix; rows correspond to genes and
columns correspond to samples; usually preprocessed by
|
anno |
annotation data frame containing a match between the row names
of the expression.matrix (usually ENSEMBL IDs) and the gene names that
should be rendered within the app and in output files; this object is
created by |
Value
The UI and Server components of the shiny module, that can be used within the UI and Server definitions of a shiny app.
Calculate statistics for each gene of an expression matrix given a grouping
Description
This function calculates the mean and standard deviation of the expression of each gene in an expression matrix, grouped by the conditions supplied.
Usage
calculate_condition_mean_sd_per_gene(expression.matrix, condition)
Arguments
expression.matrix |
the expression matrix; rows correspond to genes and
columns correspond to samples; usually preprocessed by
|
condition |
the condition to group the columns of the expression matrix by; must be a factor of the same length as ncol(expression.matrix) |
Value
A tibble in long format, with the mean and standard deviation of each gene in each condition. The standard deviation is increased to the minimum value in the expression matrix (the noise threshold) if it is lower, in order to avoid sensitivity to small changes.
Examples
expression.matrix.preproc <- as.matrix(read.csv(
system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"),
row.names = 1
))[1:500,]
condition <- factor(rep(c("0h", "12h", "36h"), each = 2))
tbl <- calculate_condition_mean_sd_per_gene(expression.matrix.preproc[1:10, ], condition)
tbl
Generate the cross plot panel of the shiny app
Description
These are the UI and server components of the cross plot panel of the
shiny app. It is generated by including 'Cross' in the panels.default argument
of generateShinyApp.
Usage
crossPanelUI(id, metadata, show = TRUE)
crossPanelServer(id, expression.matrix, metadata, anno)
Arguments
id |
the input slot that will be used to access the value |
metadata |
a data frame containing metadata for the samples contained
in the expression.matrix; must contain at minimum two columns:
the first column must contain the column names of the expression.matrix,
while the last column is assumed to contain the experimental conditions
that will be tested for differential expression; a list (of the same
length as modality) can be provided if #' |
show |
whether to show the panel or not; default is TRUE; there for compatibility with specifying panels to show |
expression.matrix |
the expression matrix; rows correspond to genes and
columns correspond to samples; usually preprocessed by
|
anno |
annotation data frame containing a match between the row names
of the expression.matrix (usually ENSEMBL IDs) and the gene names that
should be rendered within the app and in output files; this object is
created by |
Value
The UI and Server components of the shiny module, that can be used within the UI and Server definitions of a shiny app.
Create a cross plot comparing differential expression (DE) results
Description
This function creates a cross plot visualising the differences in log2(fold-change) between two DE analyses.
Usage
cross_plot(
DEtable1,
DEtable2,
DEtable1Subset,
DEtable2Subset,
df = NULL,
lfc.threshold = NULL,
raster = FALSE,
mask = FALSE,
labnames = c("not DE", "DE both", "DE comparison 1", "DE comparison 2"),
cols.chosen = c("grey", "purple", "dodgerblue", "lightcoral"),
labels.per.region = 5,
fix.axis.ratio = TRUE,
add.guide.lines = TRUE,
add.labels.custom = FALSE,
genes.to.label = NULL,
seed = 0,
label.force = 1
)
Arguments
DEtable1, DEtable2, DEtable1Subset, DEtable2Subset |
tables of DE results,
usually generated by |
df |
Optionally, pre-computed cross plot table, from cross_plot_prep |
lfc.threshold |
the log2(fold-change) threshold to determine whether a gene is DE |
raster |
whether to rasterize non-DE genes with ggraster to reduce memory usage; particularly useful when saving plots to files |
mask |
whether to hide genes that were not called DE in either comparison; default is FALSE |
labnames, cols.chosen |
the legend labels and colours for the 4 categories of genes ("not DE", "DE both", "DE comparison 1", "DE comparison 2") |
labels.per.region |
how many labels to show in each region of the plot; the plot is split in 8 regions using the axes and major diagonals, and the points closest to the origin in each region are labelled; default is 5, set to 0 for no labels |
fix.axis.ratio |
whether to ensure the x and y axes have the same units, resulting in a square plot; default is TRUE |
add.guide.lines |
whether to add vertical and horizontal guide lines to the plot to highlight the thresholds; default is TRUE |
add.labels.custom |
whether to add labels to user-specified genes; the parameter genes.to.label must also be specified; default is FALSE |
genes.to.label |
a vector of gene names to be labelled in the plot; if names are present those are shown as the labels (but the values are the ones matched - this is to allow custom gene names to be presented) |
seed |
the random seed to be used for reproducibility; only used for ggrepel::geom_label_repel if labels are present |
label.force |
passed to the force argument of ggrepel::geom_label_repel; higher values make labels overlap less (at the cost of them being further away from the points they are labelling) |
Value
The cross plot as a ggplot object.
Examples
expression.matrix.preproc <- as.matrix(read.csv(
system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"),
row.names = 1
))[1:500, 1:4]
anno <- AnnotationDbi::select(
getExportedValue('org.Mm.eg.db', 'org.Mm.eg.db'),
keys = rownames(expression.matrix.preproc),
keytype = 'ENSEMBL',
columns = 'SYMBOL'
) %>%
dplyr::distinct(ENSEMBL, .keep_all = TRUE) %>%
dplyr::mutate(NAME = ifelse(is.na(SYMBOL), ENSEMBL, SYMBOL))
edger <- DEanalysis_edger(
expression.matrix = expression.matrix.preproc,
condition = rep(c("0h", "12h"), each = 2),
var1 = "0h",
var2 = "12h",
anno = anno
)
deseq <- DEanalysis_edger(
expression.matrix = expression.matrix.preproc,
condition = rep(c("0h", "12h"), each = 2),
var1 = "0h",
var2 = "12h",
anno = anno
)
cross_plot(
DEtable1 = edger,
DEtable2 = deseq,
DEtable1Subset = dplyr::filter(edger, abs(log2FC) > 1, pvalAdj < 0.05),
DEtable2Subset = dplyr::filter(deseq, abs(log2FC) > 1, pvalAdj < 0.05),
labels.per.region = 0
)
Determine the pattern between two intervals
Description
This function checks if the two input intervals oferlap and outputs the corresponding pattern (up, down, or straight) based on that.
Usage
determine_uds(min1, max1, min2, max2)
Arguments
min1, max1, min2, max2 |
the endpoints of the two intervals |
Value
A single character (one of "U", "D", "S") representing the pattern
Examples
determine_uds(10, 20, 15, 25) # overlap
determine_uds(10, 20, 25, 35) # no overlap
Generate the enrichment panel of the shiny app
Description
These are the UI and server components of the enrichment panel of the
shiny app. It is generated by including 'Enrichment' in the panels.default argument
of generateShinyApp.
Usage
enrichmentPanelUI(id, show = TRUE)
enrichmentPanelServer(id, DEresults, organism, seed = 13)
Arguments
id |
the input slot that will be used to access the value |
show |
whether to show the panel or not; default is TRUE; there for compatibility with specifying panels to show |
DEresults |
differential expression results output from DEpanelServer; a reactive list with slots 'DEtable' (all genes), 'DEtableSubset' (only DE genes), 'lfcThreshold' and 'pvalThreshold' |
organism |
organism name to be passed on to |
seed |
the random seed to be set for the jitter plot, to avoid seemingly different plots for the same inputs |
Value
The UI and Server components of the shiny module, that can be used within the UI and Server definitions of a shiny app.
Create heatmap of an expression matrix
Description
This function creates a heatmap to visualise an expression matrix
Usage
expression_heatmap(
expression.matrix.subset,
top.annotation.ids = NULL,
metadata,
type = c("Z-score", "Log2 Expression", "Expression"),
show.column.names = TRUE
)
Arguments
expression.matrix.subset |
a subset of rows from the expression matrix; rows correspond to genes and columns correspond to samples |
top.annotation.ids |
a vector of column indices denoting which columns of the metadata should become heatmap annotations |
metadata |
a data frame containing metadata for the samples contained
in the expression.matrix; must contain at minimum two columns:
the first column must contain the column names of the expression.matrix,
while the last column is assumed to contain the experimental conditions
that will be tested for differential expression; a list (of the same
length as modality) can be provided if #' |
type |
type of rescaling; one of "Expression" (defautl, does nothing), "Log2 Expression" (returns log2(x + 1) for every value), "Mean Scaled" (each row is scaled by its average), "Z-score" (each row is centered and scaled to mean = 0 and sd = 1) |
show.column.names |
whether to show the column names below the heatmap; default is TRUE |
Value
The heatmap as detailed in the ComplexHeatmap package.
Examples
expression.matrix.preproc <- as.matrix(read.csv(
system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"),
row.names = 1
))[1:500,]
metadata <- data.frame(
srr = colnames(expression.matrix.preproc),
timepoint = rep(c("0h", "12h", "36h"), each = 2)
)
print(expression_heatmap(head(expression.matrix.preproc), NULL, metadata))
Find recurring regulators
Description
This function finds regulators that appear as the same network edge in more than one of the input networks.
Usage
find_regulators_with_recurring_edges(weightMatList, plotConnections)
Arguments
weightMatList |
a list of (weighted) adjacency matrices; each list element must be an adjacency matrix with regulators in rows, targets in columns |
plotConnections |
the number of connections to subset to |
Value
A vector containing the names of the recurring regulators
Examples
weightMat1 <- matrix(
c(0.1, 0.4, 0.8, 0.3), nrow = 2, ncol = 2,
dimnames = list("regulators" = c("r1", "r2"), "targets" = c("t1", "t2"))
)
weightMat2 <- matrix(
c(0.1, 0.2, 0.8, 0.3), nrow = 2, ncol = 2,
dimnames = list("regulators" = c("r1", "r2"), "targets" = c("t1", "t2"))
)
find_regulators_with_recurring_edges(list(weightMat1, weightMat2), 2)
Generate all files required for an autonomous shiny app
Description
This function creates an app.R file and all required objects
to run the app in .rda format in the target directory. A basic argument
check is performed to avoid input data problems. The app directory
is standalone and can be used on another platform, as long as bulkAnalyseR
is installed there. It is recommended to run
preprocessExpressionMatrix before this function.
Usage
generateShinyApp(
shiny.dir = "shiny_bulkAnalyseR",
app.title = "Visualisation of RNA-Seq data",
theme = "flatly",
modality = "RNA",
expression.matrix,
metadata,
organism = NA,
org.db = NA,
panels.default = c("Landing", "SampleSelect", "QC", "GRN", "DE", "DEplot", "DEsummary",
"Enrichment", "GRNenrichment", "Cross", "Patterns"),
panels.extra = tibble::tibble(name = NULL, UIfun = NULL, UIvars = NULL, serverFun =
NULL, serverVars = NULL),
data.extra = list(),
packages.extra = c(),
cis.integration = tibble::tibble(reference.expression.matrix = NULL, reference.org.db =
NULL, reference.coord = NULL, comparison.coord = NULL, reference.table.name = NULL,
comparison.table.name = NULL),
trans.integration = tibble::tibble(reference.expression.matrix = NULL, reference.org.db
= NULL, comparison.expression.matrix = NULL, comparison.org.db = NULL,
reference.table.name = NULL, comparison.table.name = NULL),
custom.integration = tibble::tibble(reference.expression.matrix = NULL,
reference.org.db = NULL, comparison.table = NULL, reference.table.name = NULL,
comparison.table.name = NULL)
)
Arguments
shiny.dir |
directory to store the shiny app; if a non-empty directory with that name already exists an error is generated |
app.title |
title to be displayed within the app |
theme |
shiny theme to be used in the app; default is 'flatly' |
modality |
name of the modality, or a vector of modalities to be included in the app |
expression.matrix |
the expression matrix; rows correspond to genes and
columns correspond to samples; usually preprocessed by
|
metadata |
a data frame containing metadata for the samples contained
in the expression.matrix; must contain at minimum two columns:
the first column must contain the column names of the expression.matrix,
while the last column is assumed to contain the experimental conditions
that will be tested for differential expression; a list (of the same
length as modality) can be provided if #' |
organism |
organism name to be passed on to |
org.db |
database for annotations to transform ENSEMBL IDs to
gene names; a list of bioconductor packaged databases can be found with
|
panels.default |
argument to control which of the default panels
will be included in the app; default is all, but the enrichment panel
will not appear unless organism is also supplied; note that the 'DE' panel
is required for 'DEplot', 'DEsummary', 'Enrichment', and 'GRNenrichment'; a list (of the same
length as modality) can be provided if |
panels.extra, data.extra, packages.extra |
functionality to add new user-created panels to the app to extend functionality or change the default behaviour of existing panels; a data frame of the modality, panel UI and server names and default parameters should be passed to panels.extra (see example); the names of any packages required should be passed to the packages.extra argument; extra data should be a single list and passed to the data.extra argument |
cis.integration |
functionality to integrate extra cis-regulatory information into GRN panel. Tibble containing names of reference expression matrix, tables of coordinates for elements corresponding to rows of reference expression matrix (reference.coord), tables of coordinates to compare against reference.coord (comparison.coord) and names for comparison tables. See vignettes for more details about inputs. |
trans.integration |
functionality to integrate extra trans-regulatory information into GRN panel. Tibble containing names of reference expression matrix, (reference.expression.matrix), comparison expression matrix (comparison.expression.matrix). Organism database names for each expression matrix and names for each table are also required. See vignettes for more details about inputs. |
custom.integration |
functionality to integrate custom information related to rows of reference expression matrix. Tibble containing names of reference expression matrix, tables (comparison.table) with Reference_ID and Reference_Name (matching ENSEMBL and NAME columns of reference organism database) and Comparison_ID and Comparison_Name plus a Category column containing extra information. Names for the reference expression matrix and comparison table (comparison.table.name) are also required. See vignettes for more details about inputs. |
Value
The path to shiny.dir (invisibly).
Examples
expression.matrix.preproc <- as.matrix(read.csv(
system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"),
row.names = 1
))
metadata <- data.frame(
srr = colnames(expression.matrix.preproc),
timepoint = rep(c("0h", "12h", "36h"), each = 2)
)
app.dir <- generateShinyApp(
shiny.dir = paste0(tempdir(), "/shiny_Yang2019"),
app.title = "Shiny app for the Yang 2019 data",
modality = "RNA",
expression.matrix = expression.matrix.preproc,
metadata = metadata,
organism = "mmusculus",
org.db = "org.Mm.eg.db"
)
# runApp(app.dir)
# Example of an app with a second copy of the QC panel
app.dir.qc2 <- generateShinyApp(
shiny.dir = paste0(tempdir(), "/shiny_Yang2019_QC2"),
app.title = "Shiny app for the Yang 2019 data",
expression.matrix = expression.matrix.preproc,
metadata = metadata,
organism = "mmusculus",
org.db = "org.Mm.eg.db",
panels.extra = tibble::tibble(
name = "RNA2",
UIfun = "modalityPanelUI",
UIvars = "'RNA2', metadata[[1]], NA, 'QC'",
serverFun = "modalityPanelServer",
serverVars = "'RNA2', expression.matrix[[1]], metadata[[1]], anno[[1]], NA, 'QC'"
)
)
# runApp(app.dir.qc2)
# clean up tempdir
unlink(paste0(normalizePath(tempdir()), "/", dir(tempdir())), recursive = TRUE)
Create a bar plot of expression for selected genes across samples in an experiment
Description
This function creates a clustered bar plot between all samples in the expression matrix for the selection of genes.
Usage
genes_barplot(sub.expression.matrix, log.transformation = TRUE)
Arguments
sub.expression.matrix |
subset of the expression matrix containing only selected genes |
log.transformation |
whether expression should be shown on log (default) or linear scale |
Value
The bar plot as a ggplot object.
Examples
expression.matrix.preproc <- as.matrix(read.csv(
system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"),
row.names = 1
))[1:500,]
print(genes_barplot(head(expression.matrix.preproc,5)))
Convert the adjacency matrix to network links
Description
This function converts an adjacency matrix to a data frame of network links, subset to the most important ones.
Usage
get_link_list_rename(weightMat, plotConnections)
Arguments
weightMat |
the (weighted) adjacency matrix - regulators in rows, targets in columns |
plotConnections |
the number of connections to subset to |
Value
A data frame with fields from, to and value, describing the edges of the network
Examples
weightMat <- matrix(
c(0.1, 0.4, 0.8, 0.3), nrow = 2, ncol = 2,
dimnames = list("regulators" = c("r1", "r2"), "targets" = c("t1", "t2"))
)
get_link_list_rename(weightMat, 2)
Perform GRN inference
Description
This function performs Gene Regulatory Network inference on a subset of the expression matrix, for a set of potential targets
Usage
infer_GRN(
expression.matrix,
metadata,
anno,
seed = 13,
targets,
condition,
samples,
inference_method
)
Arguments
expression.matrix |
the expression matrix; rows correspond to genes and
columns correspond to samples; usually preprocessed by
|
metadata |
a data frame containing metadata for the samples contained
in the expression.matrix; must contain at minimum two columns:
the first column must contain the column names of the expression.matrix,
while the last column is assumed to contain the experimental conditions
that will be tested for differential expression; a list (of the same
length as modality) can be provided if #' |
anno |
annotation data frame containing a match between the row names
of the expression.matrix (usually ENSEMBL IDs) and the gene names that
should be rendered within the app and in output files; this object is
created by |
seed |
the random seed to be set when running GRN inference, to ensure reproducibility of outputs |
targets |
the target genes of interest around which the GRN is built; must be row names of the expression matrix |
condition |
name of the metadata column to select samples from |
samples |
names of the sample groups to select; must appear in
|
inference_method |
method used for GRN inference; only supported method is currently GENIE3. |
Value
The adjacency matrix of the inferred network
Examples
expression.matrix.preproc <- as.matrix(read.csv(
system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"),
row.names = 1
))[1:500, ]
metadata <- data.frame(
srr = colnames(expression.matrix.preproc),
timepoint = rep(c("0h", "12h", "36h"), each = 2)
)
anno <- AnnotationDbi::select(
getExportedValue('org.Mm.eg.db', 'org.Mm.eg.db'),
keys = rownames(expression.matrix.preproc),
keytype = 'ENSEMBL',
columns = 'SYMBOL'
) %>%
dplyr::distinct(ENSEMBL, .keep_all = TRUE) %>%
dplyr::mutate(NAME = ifelse(is.na(SYMBOL), ENSEMBL, SYMBOL))
res <- infer_GRN(
expression.matrix = expression.matrix.preproc,
metadata = metadata,
anno = anno,
seed = 13,
targets = c("Hecw2", "Akr1cl"),
condition = "timepoint",
samples = "0h",
inference_method = "GENIE3"
)
Create a heatmap of the Jaccard similarity index (JSI) between samples of an experiment
Description
This function creates a JSI heatmap between all samples in the expression matrix using the specified number of most abundant genes as input. Metadata columns are used as annotations.
Usage
jaccard_heatmap(
expression.matrix,
metadata,
top.annotation.ids = NULL,
n.abundant = NULL,
show.values = TRUE,
show.row.column.names = TRUE
)
Arguments
expression.matrix |
the expression matrix; rows correspond to genes and
columns correspond to samples; usually preprocessed by
|
metadata |
a data frame containing metadata for the samples contained
in the expression.matrix; must contain at minimum two columns:
the first column must contain the column names of the expression.matrix,
while the last column is assumed to contain the experimental conditions
that will be tested for differential expression; a list (of the same
length as modality) can be provided if #' |
top.annotation.ids |
a vector of column indices denoting which columns of the metadata should become heatmap annotations |
n.abundant |
number of most abundant genes to use for the JSI calculation |
show.values |
whether to show the JSI values within the heatmap squares |
show.row.column.names |
whether to show the row and column names below the heatmap; default is TRUE |
Value
The JSI heatmap as detailed in the ComplexHeatmap package.
Examples
expression.matrix.preproc <- as.matrix(read.csv(
system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"),
row.names = 1
))[1:500,]
metadata <- data.frame(
srr = colnames(expression.matrix.preproc),
timepoint = rep(c("0h", "12h", "36h"), each = 2)
)
print(jaccard_heatmap(expression.matrix.preproc, metadata, n.abundant = 100))
Calculate the Jaccard similarity index (JSI) between two vectors
Description
Calculate the Jaccard similarity index (JSI) between two vectors
Usage
jaccard_index(a, b)
Arguments
a, b |
two vectors |
Value
The JSI of the two vectors, a single value between 0 and 1.
Examples
jaccard_index(1:4, 2:6)
Generate the landing page panel of the shiny app
Description
These are the UI and server components of the landing page
panel of the shiny app. It is generated by including 'Landing' in the
panels.default argument of generateShinyApp.
Usage
landingPanelUI(id, show = TRUE)
landingPanelServer(id)
Arguments
id |
the input slot that will be used to access the value |
show |
whether to show the panel or not; default is TRUE; there for compatibility with specifying panels to show |
Value
The UI and Server components of the shiny module, that can be used within the UI and Server definitions of a shiny app.
Create an MA plot visualising differential expression (DE) results
Description
This function creates an MA plot to visualise the results of a DE analysis.
ma_enhance is called indirectly by
ma_plot to add extra features.
Usage
ma_plot(
genes.de.results,
pval.threshold = 0.05,
lfc.threshold = 1,
alpha = 0.1,
ylims = NULL,
add.colours = TRUE,
add.expression.colour.gradient = TRUE,
add.guide.lines = TRUE,
add.labels.auto = TRUE,
add.labels.custom = FALSE,
...
)
ma_enhance(
p,
df,
pval.threshold,
lfc.threshold,
alpha,
add.colours,
point.colours = c("#bfbfbf", "orange", "red", "blue"),
raster = FALSE,
add.expression.colour.gradient,
colour.gradient.scale = list(left = c("#99e6ff", "#000066"), right = c("#99e6ff",
"#000066")),
colour.gradient.breaks = waiver(),
colour.gradient.limits = NULL,
add.guide.lines,
guide.line.colours = c("green", "blue"),
add.labels.auto,
add.labels.custom,
annotation = NULL,
n.labels.auto = c(5, 5, 5),
genes.to.label = NULL,
seed = 0,
label.force = 1
)
Arguments
genes.de.results |
the table of DE genes, usually generated by
|
pval.threshold, lfc.threshold |
the p-value and/or log2(fold-change) thresholds to determine whether a gene is DE |
alpha |
the transparency of points; ignored for DE genes if add.expression.colour.gradient is TRUE; default is 0.1 |
ylims |
a single value to create (symmetric) y-axis limits; by default inferred from the data |
add.colours |
whether to colour genes based on their log2(fold-change) and -log10(p-value); default is TRUE |
add.expression.colour.gradient |
whether to add a colour gradient for DE genes to present their log2(expression); default is TRUE |
add.guide.lines |
whether to add vertical and horizontal guide lines to the plot to highlight the thresholds; default is TRUE |
add.labels.auto |
whether to automatically label genes with the highest |log2(fold-change)| and expression; default is TRUE |
add.labels.custom |
whether to add labels to user-specified genes; the parameter genes.to.label must also be specified; default is FALSE |
... |
parameters passed on to |
p |
MA plot as a ggplot object (usually passed by |
df |
data frame of DE results for all genes (usually passed by
|
point.colours |
a vector of 4 colours to colour genes with both pval and lfc under thresholds, just pval under threshold, just lfc under threshold, both pval and lfc over threshold (DE genes) respectively; only used if add.colours is TRUE |
raster |
whether to rasterize non-DE genes with ggraster to reduce memory usage; particularly useful when saving plots to files |
colour.gradient.scale |
a vector of two colours to create a colour gradient for colouring the DE genes based on expression; a named list with components left and right can be supplied to use two different colour scales; only used if add.expression.colour.gradient is TRUE |
colour.gradient.breaks, colour.gradient.limits |
parameters to customise the legend of the colour gradient scale; especially useful if creating multiple plots or a plot with two scales; only used if add.expression.colour.gradient is TRUE |
guide.line.colours |
a vector with two colours to be used to colour the guide lines; the first colour is used for the p-value and log2(fold-change) thresholds and the second for double those values |
annotation |
annotation data frame containing a match between the gene field of df (usually ENSEMBL IDs) and the gene names that should be shown in the plot labels; not necessary if df already contains gene names |
n.labels.auto |
a integer vector of length 3 denoting the number of genes that should be automatically labelled; the first entry corresponds to DE genes with the lowest p-value, the second to those with highest absolute log2(fold-change) and the third to those with highest expression; a single integer can also be specified, to be used for all 3 entries; default is 5 |
genes.to.label |
a vector of gene names to be labelled in the plot; if names are present those are shown as the labels (but the values are the ones matched - this is to allow custom gene names to be presented) |
seed |
the random seed to be used for reproducibility; only used for ggrepel::geom_label_repel if labels are present |
label.force |
passed to the force argument of ggrepel::geom_label_repel; higher values make labels overlap less (at the cost of them being further away from the points they are labelling) |
Value
The MA plot as a ggplot object.
The enhanced MA plot as a ggplot object.
Examples
expression.matrix.preproc <- as.matrix(read.csv(
system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"),
row.names = 1
))[1:500, 1:4]
anno <- AnnotationDbi::select(
getExportedValue('org.Mm.eg.db', 'org.Mm.eg.db'),
keys = rownames(expression.matrix.preproc),
keytype = 'ENSEMBL',
columns = 'SYMBOL'
) %>%
dplyr::distinct(ENSEMBL, .keep_all = TRUE) %>%
dplyr::mutate(NAME = ifelse(is.na(SYMBOL), ENSEMBL, SYMBOL))
edger <- DEanalysis_edger(
expression.matrix = expression.matrix.preproc,
condition = rep(c("0h", "12h"), each = 2),
var1 = "0h",
var2 = "12h",
anno = anno
)
mp <- ma_plot(edger)
print(mp)
Create a matrix of the average expression of each gene in each condition
Description
This function reshapes the tibble output of
calculate_condition_mean_sd_per_gene into a matrix of
average expression by condition. Its output can be used by
expression_heatmap.
Usage
make_heatmap_matrix(tbl, genes = NULL)
Arguments
tbl |
the output of |
genes |
gene names to use for the output; if NULL (the default), all genes will be used |
Value
A matrix of averaged expression per gene in each condition.
Examples
expression.matrix.preproc <- as.matrix(read.csv(
system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"),
row.names = 1
))[1:500,]
condition <- factor(rep(c("0h", "12h", "36h"), each = 2))
tbl <- calculate_condition_mean_sd_per_gene(expression.matrix.preproc[1:10, ], condition)
heatmat <- make_heatmap_matrix(tbl)
heatmat
Create a matrix of the patterns between conditions
Description
This function determines the patterns between different
conditions of each gene. It should be applied to the output of
calculate_condition_mean_sd_per_gene.
Usage
make_pattern_matrix(tbl, n_sd = 2)
Arguments
tbl |
the output of |
n_sd |
number of standard deviations from the mean to use to construct the intervals; default is 2 |
Value
A matrix of single character patterns between conditions. The last column is named pattern and is a concatenation of all other columns.
Examples
expression.matrix.preproc <- as.matrix(read.csv(
system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"),
row.names = 1
))[1:500,]
condition <- factor(rep(c("0h", "12h", "36h"), each = 2))
tbl <- calculate_condition_mean_sd_per_gene(expression.matrix.preproc[1:10, ], condition)
patmat <- make_pattern_matrix(tbl)
patmat
Generate an app panel for a modality
Description
These are the UI and server components of a modality
panel of the shiny app. Different modalities can be included by
specifying their inputs in generateShinyApp.
Usage
modalityPanelUI(id, metadata, organism, panels.default)
modalityPanelServer(
id,
expression.matrix,
metadata,
anno,
organism,
panels.default
)
Arguments
id |
the input slot that will be used to access the value |
metadata |
a data frame containing metadata for the samples contained
in the expression.matrix; must contain at minimum two columns:
the first column must contain the column names of the expression.matrix,
while the last column is assumed to contain the experimental conditions
that will be tested for differential expression; a list (of the same
length as modality) can be provided if #' |
organism |
organism name to be passed on to |
panels.default |
argument to control which of the default panels
will be included in the app; default is all, but the enrichment panel
will not appear unless organism is also supplied; note that the 'DE' panel
is required for 'DEplot', 'DEsummary', 'Enrichment', and 'GRNenrichment'; a list (of the same
length as modality) can be provided if |
expression.matrix |
the expression matrix; rows correspond to genes and
columns correspond to samples; usually preprocessed by
|
anno |
annotation data frame containing a match between the row names
of the expression.matrix (usually ENSEMBL IDs) and the gene names that
should be rendered within the app and in output files; this object is
created by |
Value
The UI and Server components of the shiny module, that can be used within the UI and Server definitions of a shiny app.
Apply a modified noisyR counts pipeline printing a plot
Description
This function is identical to the noisyr::noisyr_counts function, with the addition of the option to print a line plot of the similarity against expression for all samples.
Usage
noisyr_counts_with_plot(
expression.matrix,
n.elements.per.window = NULL,
optimise.window.length.logical = FALSE,
similarity.threshold = 0.25,
method.chosen = "Boxplot-IQR",
...,
output.plot = FALSE
)
Arguments
expression.matrix |
the expression matrix; rows correspond to genes and columns correspond to samples |
n.elements.per.window |
number of elements to have in a window passed to calculate_expression_similarity_counts(); default 10% of the number of rows |
optimise.window.length.logical |
whether to call optimise_window_length to try and optimise the value of n.elements.per.window |
similarity.threshold, method.chosen |
parameters passed on to
|
... |
optional arguments passed on to |
output.plot |
whether to create an expression-similarity plot for the noise analysis (printed to the console); default is FALSE |
Value
The denoised expression matrix.
Examples
expression.matrix <- as.matrix(read.csv(
system.file("extdata", "expression_matrix.csv", package = "bulkAnalyseR"),
row.names = 1
))[1:10, 1:4]
expression.matrix.denoised <- noisyr_counts_with_plot(expression.matrix)
Generate the expression patterns panel of the shiny app
Description
These are the UI and server components of the expression patterns
panel of the shiny app. It is generated by including 'Patterns' in the
panels.default argument of generateShinyApp.
Usage
patternPanelUI(id, metadata, show = TRUE)
patternPanelServer(id, expression.matrix, metadata, anno)
Arguments
id |
the input slot that will be used to access the value |
metadata |
a data frame containing metadata for the samples contained
in the expression.matrix; must contain at minimum two columns:
the first column must contain the column names of the expression.matrix,
while the last column is assumed to contain the experimental conditions
that will be tested for differential expression; a list (of the same
length as modality) can be provided if #' |
show |
whether to show the panel or not; default is TRUE; there for compatibility with specifying panels to show |
expression.matrix |
the expression matrix; rows correspond to genes and
columns correspond to samples; usually preprocessed by
|
anno |
annotation data frame containing a match between the row names
of the expression.matrix (usually ENSEMBL IDs) and the gene names that
should be rendered within the app and in output files; this object is
created by |
Value
The UI and Server components of the shiny module, that can be used within the UI and Server definitions of a shiny app.
Plot a GRN
Description
This function creates a network plot of a GRN.
Usage
plot_GRN(
weightMat,
anno,
plotConnections,
plot_position_grid,
n_networks,
recurring_regulators
)
Arguments
weightMat |
the (weighted) adjacency matrix - regulators in rows, targets in columns |
anno |
annotation data frame containing a match between the row names
of the expression.matrix (usually ENSEMBL IDs) and the gene names that
should be rendered within the app and in output files; this object is
created by |
plotConnections |
the number of connections to subset to |
plot_position_grid, n_networks |
the position of the plot in the grid (1-4) and the number of networks shown (1-4); these are solely used for hiding unwanted plots in the shiny app |
recurring_regulators |
targets to be highlighted; usually the
result of |
Value
A network plot. See visNetwork package for more details.
Examples
weightMat1 <- matrix(
c(0.1, 0.4, 0.8, 0.3), nrow = 2, ncol = 2,
dimnames = list("regulators" = c("r1", "r2"), "targets" = c("t1", "t2"))
)
weightMat2 <- matrix(
c(0.1, 0.2, 0.8, 0.3), nrow = 2, ncol = 2,
dimnames = list("regulators" = c("r1", "r2"), "targets" = c("t1", "t2"))
)
anno <- tibble::tibble(ENSEMBL = c("r1", "r2", "t1", "t2"), NAME = ENSEMBL)
recurring_regulators <- find_regulators_with_recurring_edges(list(weightMat1, weightMat2), 2)
plot_GRN(weightMat1, anno, 2, 1, 1, recurring_regulators)
plot_GRN(weightMat2, anno, 2, 1, 1, recurring_regulators)
Create a line plot of average expression across conditions
Description
This function creates a line plot of average expression across conditions for a selection of genes, usually to visualise an expression pattern.
Usage
plot_line_pattern(
tbl,
genes = NULL,
type = c("Mean Scaled", "Log2 Expression", "Expression"),
show.legend = FALSE
)
Arguments
tbl |
the output of |
genes |
gene names to use for the output; if NULL (the default), all genes will be used |
type |
whether the expression values should be scaled using their mean (default), log-transformed, or not adjusted for the plot |
show.legend |
whether to show the gene names in the legend; should be avoided in many genes are plotted |
Value
A matrix of average gene expression per gene in each condition.
Examples
expression.matrix.preproc <- as.matrix(read.csv(
system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"),
row.names = 1
))[1:500,]
condition <- factor(rep(c("0h", "12h", "36h"), each = 2))
tbl <- calculate_condition_mean_sd_per_gene(expression.matrix.preproc[1:10, ], condition)
plot_line_pattern(tbl)
Create a principal component analysis (PCA) plot the samples of an experiment
Description
This function creates a PCA plot between all samples in the expression matrix using the specified number of most abundant genes as input. A metadata column is used as annotation.
Usage
plot_pca(
expression.matrix,
metadata,
annotation.id,
n.abundant = NULL,
show.labels = FALSE,
show.ellipses = TRUE,
label.force = 1
)
Arguments
expression.matrix |
the expression matrix; rows correspond to genes and
columns correspond to samples; usually preprocessed by
|
metadata |
a data frame containing metadata for the samples contained
in the expression.matrix; must contain at minimum two columns:
the first column must contain the column names of the expression.matrix,
while the last column is assumed to contain the experimental conditions
that will be tested for differential expression; a list (of the same
length as modality) can be provided if #' |
annotation.id |
a column index denoting which column of the metadata should be used to colour the points and draw confidence ellipses |
n.abundant |
number of most abundant genes to use for the JSI calculation |
show.labels |
whether to label the points with the sample names |
show.ellipses |
whether to draw confidence ellipses |
label.force |
passed to the force argument of ggrepel::geom_label_repel; higher values make labels overlap less (at the cost of them being further away from the points they are labelling) |
Value
The PCA plot as a ggplot object.
Examples
expression.matrix.preproc <- as.matrix(read.csv(
system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"),
row.names = 1
))[1:500,]
metadata <- data.frame(
srr = colnames(expression.matrix.preproc),
timepoint = rep(c("0h", "12h", "36h"), each = 2)
)
plot_pca(expression.matrix.preproc, metadata, 2)
Visualise the overlap of edges between different networks
Description
This function creates an UpSet plot of the intersections and specific differences of the edges in the input networks.
Usage
plot_upset(weightMatList, plotConnections)
Arguments
weightMatList |
a list of (weighted) adjacency matrices; each list element must be an adjacency matrix with regulators in rows, targets in columns |
plotConnections |
the number of connections to subset to |
Value
An UpSet plot. See UpSetR package for more details.
Examples
weightMat1 <- matrix(
c(0.1, 0.4, 0.8, 0.3), nrow = 2, ncol = 2,
dimnames = list("regulators" = c("r1", "r2"), "targets" = c("t1", "t2"))
)
weightMat2 <- matrix(
c(0.1, 0.2, 0.8, 0.3), nrow = 2, ncol = 2,
dimnames = list("regulators" = c("r1", "r2"), "targets" = c("t1", "t2"))
)
plot_upset(list(weightMat1, weightMat2), 2)
Pre-process the expression matrix before generating the shiny app
Description
This function denoises the expression matrix using the noisyR package
and then normalises it. It is recommended to use this function before using
generateShinyApp.
Usage
preprocessExpressionMatrix(
expression.matrix,
denoise = TRUE,
output.plot = FALSE,
normalisation.method = c("quantile", "rpm", "tmm", "deseq2", "median"),
n_million = 1,
...
)
Arguments
expression.matrix |
the expression matrix; rows correspond to genes and columns correspond to samples |
denoise |
whether to use noisyR to denoise the expression matrix; proceeding without denoising data is not recommended |
output.plot |
whether to create an expression-similarity plot for the noise analysis (printed to the console); default is FALSE |
normalisation.method |
the normalisation method to be used; default is quantile; any unrecognised input will result in no normalisation being applied, but proceeding with un-normalised data is not recommended; currently supported normalisation methods are:
|
n_million |
scaling factor for RPM normalisation; default is 1 million |
... |
optional arguments passed on to |
Value
The denoised, normalised expression matrix; some rows (genes) may have been removed by noisyR.
Examples
expression.matrix <- as.matrix(read.csv(
system.file("extdata", "expression_matrix.csv", package = "bulkAnalyseR"),
row.names = 1
))[1:10, 1:4]
expression.matrix.preproc <- preprocessExpressionMatrix(expression.matrix)
Creates a comparison table for miRTarBase to be used for custom integration
Description
This function downloads the miRTarBase database for the organism
of choice, filters it according to user-specified values and formats ready for
custom integration in generateShinyApp.
Usage
preprocess_miRTarBase(
download.dir = ".",
download.method = "auto",
mirtarbase.file = NULL,
organism.code,
org.db,
support.type = c(),
validation.method = c(),
reference = c("mRNA", "miRNA"),
print.support.types = FALSE,
print.validation.methods = FALSE
)
Arguments
download.dir |
Directory where miRTarBase database will be downloaded. |
download.method |
Method for downloading miRTarBase file through download.file, see download.file documentation for options for your operating system. |
mirtarbase.file |
Path to pre-downloaded miRTarBase file for your organism. If this is left NULL then the file will be downloaded. |
organism.code |
Three letter code for the organism of choice. See miRTarBase website for options. For human, enter 'hsa' and for mouse, 'mmu'. |
org.db |
database for annotations to transform ENSEMBL IDs to
gene names; a list of bioconductor packaged databases can be found with
|
support.type |
Subset of entries of the 'Support Type' field in miRTarBase.
Only these values will be kept. To find the options available for your organism
of choice, run the function once with |
validation.method |
Subset of entries of 'Experiments' field in miRTarBase.
Only these values will be kept. To find the options available for your organism
of choice, run the function once with |
reference |
Should the reference category be mRNA or miRNA? The reference
category chosen here must match the reference category chosen in
|
print.support.types, print.validation.methods |
Should options for Support Type and Experiments be displayed? Default is FALSE. |
Value
A dataframe with Reference_ID/Name and Comparison_ID/Name columns
which can be supplied to custom.integration in generateShinyApp
Examples
comparison.table <- preprocess_miRTarBase(
mirtarbase.file = system.file("extdata", "mmu_MTI_sub.xls", package = "bulkAnalyseR"),
organism.code = "mmu",
org.db = "org.Mm.eg.db",
support.type = "Functional MTI",
validation.method = "Luciferase reporter assay",
reference = "miRNA")
Create a density plot of log2 expression across samples of an experiment
Description
This function creates a density plot between all samples in the expression matrix. Metadata columns are used to group samples.
Usage
qc_density_plot(expression.matrix, metadata, annotation.id)
Arguments
expression.matrix |
the expression matrix; rows correspond to genes and
columns correspond to samples; usually preprocessed by
|
metadata |
a data frame containing metadata for the samples contained
in the expression.matrix; must contain at minimum two columns:
the first column must contain the column names of the expression.matrix,
while the last column is assumed to contain the experimental conditions
that will be tested for differential expression; a list (of the same
length as modality) can be provided if #' |
annotation.id |
name of metadata column on which to group samples |
Value
The density plot as a ggplot object.
Examples
expression.matrix.preproc <- as.matrix(read.csv(
system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"),
row.names = 1
))[1:500,]
metadata <- data.frame(
srr = colnames(expression.matrix.preproc),
timepoint = rep(c("0h", "12h", "36h"), each = 2)
)
print(qc_density_plot(expression.matrix.preproc, metadata, 'timepoint'))
Create a violin/box plot of expression across samples of an experiment
Description
This function creates a combined violin and box plot between all samples in the expression matrix. Metadata columns are used to colour samples.
Usage
qc_violin_plot(
expression.matrix,
metadata,
annotation.id,
log.transformation = TRUE
)
Arguments
expression.matrix |
the expression matrix; rows correspond to genes and
columns correspond to samples; usually preprocessed by
|
metadata |
a data frame containing metadata for the samples contained
in the expression.matrix; must contain at minimum two columns:
the first column must contain the column names of the expression.matrix,
while the last column is assumed to contain the experimental conditions
that will be tested for differential expression; a list (of the same
length as modality) can be provided if #' |
annotation.id |
name of metadata column on which to group samples |
log.transformation |
whether expression should be shown on log (default) or linear scale |
Value
The violin/box plot as a ggplot object.
Examples
expression.matrix.preproc <- as.matrix(read.csv(
system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"),
row.names = 1
))[1:500,]
metadata <- data.frame(
srr = colnames(expression.matrix.preproc),
timepoint = rep(c("0h", "12h", "36h"), each = 2)
)
print(qc_violin_plot(expression.matrix.preproc, metadata, 'timepoint'))
Rescale a matrix
Description
This function rescales the rows of a matrix according to the specified type.
Usage
rescale_matrix(
mat,
type = c("Expression", "Log2 Expression", "Mean Scaled", "Z-score")
)
Arguments
mat |
the matrix to rescale |
type |
type of rescaling; one of "Expression" (defautl, does nothing), "Log2 Expression" (returns log2(x + 1) for every value), "Mean Scaled" (each row is scaled by its average), "Z-score" (each row is centered and scaled to mean = 0 and sd = 1) |
Value
The rescaled matrix.
Examples
mat = matrix(1:10, nrow = 2, ncol = 5)
rescale_matrix(mat, type = "Expression")
rescale_matrix(mat, type = "Log2 Expression")
rescale_matrix(mat, type = "Mean Scaled")
rescale_matrix(mat, type = "Z-score")
Generate the sample select panel of the shiny app
Description
These are the UI and server components of the sample selection
panel of the shiny app. It is generated by including 'SampleSelect' in the
panels.default argument of generateShinyApp.
Usage
sampleSelectPanelUI(id, metadata, show = TRUE)
sampleSelectPanelServer(id, expression.matrix, metadata, modality = "RNA")
Arguments
id |
the input slot that will be used to access the value |
metadata |
a data frame containing metadata for the samples contained
in the expression.matrix; must contain at minimum two columns:
the first column must contain the column names of the expression.matrix,
while the last column is assumed to contain the experimental conditions
that will be tested for differential expression; a list (of the same
length as modality) can be provided if #' |
show |
whether to show the panel or not; default is TRUE; there for compatibility with specifying panels to show |
expression.matrix |
the expression matrix; rows correspond to genes and
columns correspond to samples; usually preprocessed by
|
modality |
the modality, needs to be passed when used within another shiny module for namespacing reasons |
Value
The UI and Server components of the shiny module, that can be used within the UI and Server definitions of a shiny app.
Create a scatter plot of expression between two samples of an experiment
Description
This function creates a scatter plot between two samples.
Usage
scatter_plot(
sub.expression.matrix,
anno,
genes.to.highlight = c(),
log.transformation = TRUE
)
Arguments
sub.expression.matrix |
subset of the expression matrix containing only the two selected samples |
anno |
annotation data frame containing a match between the row names
of the expression.matrix (usually ENSEMBL IDs) and the gene names that
should be rendered within the app and in output files; this object is
created by |
genes.to.highlight |
vector of gene names to highlight. These should match entries in the anno NAME column. |
log.transformation |
whether expression should be shown on log (default) or linear scale |
Value
The scatter plot as a ggplot object.
Examples
expression.matrix.preproc <- as.matrix(read.csv(
system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"),
row.names = 1
))[,1:2]
print(scatter_plot(expression.matrix.preproc, c()))
Create a volcano plot visualising differential expression (DE) results
Description
This function creates a volcano plot to visualise the results of a DE analysis.
volcano_enhance is called indirectly by
volcano_plot to add extra features.
Usage
volcano_plot(
genes.de.results,
pval.threshold = 0.05,
lfc.threshold = 1,
alpha = 0.1,
xlims = NULL,
log10pval.cap = TRUE,
add.colours = TRUE,
add.expression.colour.gradient = TRUE,
add.guide.lines = TRUE,
add.labels.auto = TRUE,
add.labels.custom = FALSE,
...
)
volcano_enhance(
vp,
df,
pval.threshold,
lfc.threshold,
alpha,
add.colours,
point.colours = c("#bfbfbf", "orange", "red", "blue"),
raster = FALSE,
add.expression.colour.gradient,
colour.gradient.scale = list(left = c("#99e6ff", "#000066"), right = c("#99e6ff",
"#000066")),
colour.gradient.breaks = waiver(),
colour.gradient.limits = NULL,
add.guide.lines,
guide.line.colours = c("green", "blue"),
add.labels.auto,
add.labels.custom,
annotation = NULL,
n.labels.auto = c(5, 5, 5),
genes.to.label = NULL,
seed = 0,
label.force = 1
)
Arguments
genes.de.results |
the table of DE genes, usually generated by
|
pval.threshold, lfc.threshold |
the p-value and/or log2(fold-change) thresholds to determine whether a gene is DE |
alpha |
the transparency of points; ignored for DE genes if add.expression.colour.gradient is TRUE; default is 0.1 |
xlims |
a single value to create (symmetric) x-axis limits; by default inferred from the data |
log10pval.cap |
whether to cap the log10(p-value at -10); any p-values lower that 10^(-10) are set to the cap for plotting |
add.colours |
whether to colour genes based on their log2(fold-change) and -log10(p-value); default is TRUE |
add.expression.colour.gradient |
whether to add a colour gradient for DE genes to present their log2(expression); default is TRUE |
add.guide.lines |
whether to add vertical and horizontal guide lines to the plot to highlight the thresholds; default is TRUE |
add.labels.auto |
whether to automatically label genes with the highest |log2(fold-change)| and expression; default is TRUE |
add.labels.custom |
whether to add labels to user-specified genes; the parameter genes.to.label must also be specified; default is FALSE |
... |
parameters passed on to |
vp |
volcano plot as a ggplot object (usually passed by |
df |
data frame of DE results for all genes (usually passed by
|
point.colours |
a vector of 4 colours to colour genes with both pval and lfc under thresholds, just pval under threshold, just lfc under threshold, both pval and lfc over threshold (DE genes) respectively; only used if add.colours is TRUE |
raster |
whether to rasterize non-DE genes with ggraster to reduce memory usage; particularly useful when saving plots to files |
colour.gradient.scale |
a vector of two colours to create a colour gradient for colouring the DE genes based on expression; a named list with components left and right can be supplied to use two different colour scales; only used if add.expression.colour.gradient is TRUE |
colour.gradient.breaks, colour.gradient.limits |
parameters to customise the legend of the colour gradient scale; especially useful if creating multiple plots or a plot with two scales; only used if add.expression.colour.gradient is TRUE |
guide.line.colours |
a vector with two colours to be used to colour the guide lines; the first colour is used for the p-value and log2(fold-change) thresholds and the second for double those values |
annotation |
annotation data frame containing a match between the gene field of df (usually ENSEMBL IDs) and the gene names that should be shown in the plot labels; not necessary if df already contains gene names |
n.labels.auto |
a integer vector of length 3 denoting the number of genes that should be automatically labelled; the first entry corresponds to DE genes with the lowest p-value, the second to those with highest absolute log2(fold-change) and the third to those with highest expression; a single integer can also be specified, to be used for all 3 entries; default is 5 |
genes.to.label |
a vector of gene names to be labelled in the plot; if names are present those are shown as the labels (but the values are the ones matched - this is to allow custom gene names to be presented) |
seed |
the random seed to be used for reproducibility; only used for ggrepel::geom_label_repel if labels are present |
label.force |
passed to the force argument of ggrepel::geom_label_repel; higher values make labels overlap less (at the cost of them being further away from the points they are labelling) |
Value
The volcano plot as a ggplot object.
The enhanced volcano plot as a ggplot object.
Examples
expression.matrix.preproc <- as.matrix(read.csv(
system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"),
row.names = 1
))[1:500, 1:4]
anno <- AnnotationDbi::select(
getExportedValue('org.Mm.eg.db', 'org.Mm.eg.db'),
keys = rownames(expression.matrix.preproc),
keytype = 'ENSEMBL',
columns = 'SYMBOL'
) %>%
dplyr::distinct(ENSEMBL, .keep_all = TRUE) %>%
dplyr::mutate(NAME = ifelse(is.na(SYMBOL), ENSEMBL, SYMBOL))
edger <- DEanalysis_edger(
expression.matrix = expression.matrix.preproc,
condition = rep(c("0h", "12h"), each = 2),
var1 = "0h",
var2 = "12h",
anno = anno
)
vp <- volcano_plot(edger)
print(vp)