Package 'ideal' reference manual

Title:	Interactive Differential Expression AnaLysis
Description:	This package provides functions for an Interactive Differential Expression AnaLysis of RNA-sequencing datasets, to extract quickly and effectively information downstream the step of differential expression. A Shiny application encapsulates the whole package. Support for reproducibility of the whole analysis is provided by means of a template report which gets automatically compiled and can be stored/shared.
Authors:	Federico Marini [aut, cre]
Maintainer:	Federico Marini <[email protected]>
License:	MIT + file LICENSE
Version:	2.1.0
Built:	2024-10-30 09:16:12 UTC
Source:	https://github.com/bioc/ideal

Deprecated functions in ideal

Description

Functions that are on their way to the function afterlife. Their successors are also listed.

Arguments

...

Ignored arguments.

Details

The successors of these functions are likely coming after the rework that led to the creation of the mosdef package. See more into its documentation for more details.

Value

All functions throw a warning, with a deprecation message pointing towards its descendent (if available).

Transitioning to the mosdef framework

goseqTable() is now being replaced by the more flexible mosdef::run_goseq() function (which is even faster)
ggplotCounts() is now being replaced by the more flexible, better designed, and actually even more good looking mosdef::gene_plot() function, with better default behavior and all.
deseqresult2tbl() and deseqresult2DEgenes() are now replaced by the more flexible mosdef::deresult_to_df()
The internally defined functions createLinkENS(), createLinkGeneSymbol(), and createLinkGO() are now replaced by the equivalent functions in mosdef: mosdef::create_link_ENSEMBL(), mosdef::create_link_NCBI() and mosdef::create_link_GO(). Notably, the mosdef package expanded on the concept of automatically generated buttons, taking this to the extreme of efficiency with the mosdef::buttonifier() function

Author(s)

Federico Marini

Examples

# try(goseqtable())

# try(goseqtable())

Generate a tidy table with the DE genes from the results of DESeq

Description

Generate a tidy table with the DE genes from the results of DESeq

Usage

deseqresult2DEgenes(deseqresult, FDR = 0.05)
deseqresult2DEgenes(deseqresult, FDR = 0.05)

Arguments

`deseqresult`	A `DESeqResults()` object
`FDR`	Numeric value, the significance level for thresholding adjusted p-values

Value

A "tidy" data.frame with only genes marked as differentially expressed

Examples


# with simulated data...
library(DESeq2)
dds <- DESeq2::makeExampleDESeqDataSet(n = 100, m = 8, betaSD = 2)
dds <- DESeq(dds)
res <- results(dds)
deseqresult2DEgenes(res)
# with simulated data...
library(DESeq2)
dds <- DESeq2::makeExampleDESeqDataSet(n = 100, m = 8, betaSD = 2)
dds <- DESeq(dds)
res <- results(dds)
deseqresult2DEgenes(res)

Generate a tidy table with the results of DESeq

Description

Generate a tidy table with the results of DESeq

Usage

deseqresult2tbl(deseqresult)
deseqresult2tbl(deseqresult)

Arguments

deseqresult

A DESeqResults() object

Value

A "tidy" data.frame with all genes

Examples


# with simulated data...
library(DESeq2)
dds <- DESeq2::makeExampleDESeqDataSet(n = 100, m = 8, betaSD = 1)
dds <- DESeq2::DESeq(dds)
res <- DESeq2::results(dds)
deseqresult2tbl(res)
# with simulated data...
library(DESeq2)
dds <- DESeq2::makeExampleDESeqDataSet(n = 100, m = 8, betaSD = 1)
dds <- DESeq2::DESeq(dds)
res <- DESeq2::results(dds)
deseqresult2tbl(res)

Plot normalized counts for a gene

Description

Plot for normalized counts of a single gene, with jittered points superimposed on the boxplot

Usage

ggplotCounts(
  dds,
  gene,
  intgroup = "condition",
  annotation_obj = NULL,
  transform = TRUE,
  labels_repel = TRUE
)
ggplotCounts(
  dds,
  gene,
  intgroup = "condition",
  annotation_obj = NULL,
  transform = TRUE,
  labels_repel = TRUE
)

Arguments

`dds`	A `DESeqDataSet()` object.
`gene`	A character, specifying the name of the gene to plot
`intgroup`	Interesting groups: a character vector of names in `colData(dds)` to use for grouping
`annotation_obj`	A `data.frame` object, with `row.names` as gene identifiers (e.g. ENSEMBL ids) and a column, `gene_name`, containing e.g. HGNC-based gene symbols. Optional.
`transform`	Logical value, corresponding whether to have log scale y-axis or not. Defaults to TRUE.
`labels_repel`	Logical value. Whether to use `ggrepel`'s functions to place labels; defaults to TRUE.

Details

Note: this function relies on the plotCounts() function of DESeq2, therefore pseudocounts of 0.5 are added to each point

Value

An object created by ggplot

Examples

library("airway")
data("airway", package = "airway")
airway
dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
  colData = colData(airway),
  design = ~ cell + dex
)
ggplotCounts(dds_airway,
  gene = "ENSG00000103196", # CRISPLD2 in the original publication
  intgroup = "dex"
)
library("airway")
data("airway", package = "airway")
airway
dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
  colData = colData(airway),
  design = ~ cell + dex
)
ggplotCounts(dds_airway,
  gene = "ENSG00000103196", # CRISPLD2 in the original publication
  intgroup = "dex"
)

Extract functional terms enriched in the DE genes, based on goseq

Description

A wrapper for extracting functional GO terms enriched in a list of (DE) genes, based on the algorithm and the implementation in the goseq package

Usage

goseqTable(
  de.genes,
  assayed.genes,
  genome = "hg38",
  id = "ensGene",
  testCats = c("GO:BP", "GO:MF", "GO:CC"),
  FDR_GO_cutoff = 1,
  nTop = 200,
  orgDbPkg = "org.Hs.eg.db",
  addGeneToTerms = TRUE
)
goseqTable(
  de.genes,
  assayed.genes,
  genome = "hg38",
  id = "ensGene",
  testCats = c("GO:BP", "GO:MF", "GO:CC"),
  FDR_GO_cutoff = 1,
  nTop = 200,
  orgDbPkg = "org.Hs.eg.db",
  addGeneToTerms = TRUE
)

Arguments

`de.genes`	A vector of (differentially expressed) genes
`assayed.genes`	A vector of background genes, e.g. all (expressed) genes in the assays
`genome`	A string identifying the genome that genes refer to, as in the `goseq()` function
`id`	A string identifying the gene identifier used by genes, as in the `goseq()` function
`testCats`	A vector specifying which categories to test for over representation amongst DE genes - can be any combination of "GO:CC", "GO:BP", "GO:MF" & "KEGG"
`FDR_GO_cutoff`	Numeric value for subsetting the results
`nTop`	Number of categories to extract, and optionally process for adding genes to the respective terms
`orgDbPkg`	Character string, named as the `org.XX.eg.db` package which should be available in Bioconductor
`addGeneToTerms`	Logical, whether to add a column with all genes annotated to each GO term

Details

Note: the feature length retrieval is based on the goseq() function, and requires that the corresponding TxDb packages are installed and available

Value

A table containing the computed GO Terms and related enrichment scores

Examples


library("airway")
data("airway", package = "airway")
airway
dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
  colData = colData(airway),
  design = ~ cell + dex
)
dds_airway <- DESeq2::DESeq(dds_airway)
res_airway <- DESeq2::results(dds_airway)

res_subset <- mosdef::deresult_to_df(res_airway)[1:100, ]
myde <- res_subset$id
myassayed <- rownames(res_airway)
## Not run: 
mygo <- goseqTable(myde,
  myassayed,
  testCats = "GO:BP",
  addGeneToTerms = FALSE
)
head(mygo)

## End(Not run)

library("airway")
data("airway", package = "airway")
airway
dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
  colData = colData(airway),
  design = ~ cell + dex
)
dds_airway <- DESeq2::DESeq(dds_airway)
res_airway <- DESeq2::results(dds_airway)

res_subset <- mosdef::deresult_to_df(res_airway)[1:100, ]
myde <- res_subset$id
myassayed <- rownames(res_airway)
## Not run: 
mygo <- goseqTable(myde,
  myassayed,
  testCats = "GO:BP",
  addGeneToTerms = FALSE
)
head(mygo)

## End(Not run)

ideal: Interactive Differential Expression Analysis

Description

ideal makes differential expression analysis interactive, easy and reproducible. This function launches the main application included in the package.

Usage

ideal(
  dds_obj = NULL,
  res_obj = NULL,
  annotation_obj = NULL,
  countmatrix = NULL,
  expdesign = NULL,
  gene_signatures = NULL
)
ideal(
  dds_obj = NULL,
  res_obj = NULL,
  annotation_obj = NULL,
  countmatrix = NULL,
  expdesign = NULL,
  gene_signatures = NULL
)

Arguments

`dds_obj`	A `DESeqDataSet()` object. If not provided, then a `countmatrix` and a `expdesign` need to be provided. If none of the above is provided, it is possible to upload the data during the execution of the Shiny App
`res_obj`	A `DESeqResults()` object. If not provided, it can be computed during the execution of the application
`annotation_obj`	A `data.frame` object, with row.names as gene identifiers (e.g. ENSEMBL ids) and a column, `gene_name`, containing e.g. HGNC-based gene symbols. If not provided, it can be constructed during the execution via the org.eg.XX.db packages - these need to be installed
`countmatrix`	A count matrix, with genes as rows and samples as columns. If not provided, it is possible to upload the data during the execution of the Shiny App
`expdesign`	A `data.frame` containing the info on the covariates of each sample. If not provided, it is possible to upload the data during the execution of the Shiny App
`gene_signatures`	A list of vectors, one for each pathway/signature. This is for example the output of the `read_gmt()` function. The provided object can also be replaced during runtime in the dedicated upload widget.

Value

A Shiny App is launched for interactive data exploration and differential expression analysis

Examples

# with simulated data...
library("DESeq2")
dds <- DESeq2::makeExampleDESeqDataSet(n = 100, m = 8)
cm <- counts(dds)
cd <- colData(dds)

# with the well known airway package...
library("airway")
data("airway", package = "airway")
airway
dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
  colData = colData(airway),
  design = ~ cell + dex
)
## Not run: 

ideal()
ideal(dds)
ideal(dds_airway)

dds_airway <- DESeq2::DESeq(dds_airway)
res_airway <- DESeq2::results(dds_airway)
ideal(dds_airway, res_airway)

## End(Not run)

# with simulated data...
library("DESeq2")
dds <- DESeq2::makeExampleDESeqDataSet(n = 100, m = 8)
cm <- counts(dds)
cd <- colData(dds)

# with the well known airway package...
library("airway")
data("airway", package = "airway")
airway
dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
  colData = colData(airway),
  design = ~ cell + dex
)
## Not run: 

ideal()
ideal(dds)
ideal(dds_airway)

dds_airway <- DESeq2::DESeq(dds_airway)
res_airway <- DESeq2::results(dds_airway)
ideal(dds_airway, res_airway)

## End(Not run)

ideal: Interactive Differential Expression Analysis

Description

ideal makes differential expression analysis interactive, easy and reproducible. The analysis of RNA-seq datasets is guided by the Shiny app as main component of the package, which also provides a wide set of functions to efficiently extract information from the existing data. The app can be also deployed on a Shiny server, to allow its usage without any installation on the user's side.

Details

Author(s)

Federico Marini [email protected], 2016-2017

Maintainer: Federico Marini [email protected]

MA-plot from base means and log fold changes

Description

MA-plot from base means and log fold changes, in the ggplot2 framework, with additional support to annotate genes if provided.

Usage

plot_ma(
  res_obj,
  FDR = 0.05,
  point_alpha = 0.2,
  sig_color = "red",
  annotation_obj = NULL,
  draw_y0 = TRUE,
  hlines = NULL,
  title = NULL,
  xlab = "mean of normalized counts - log10 scale",
  ylim = NULL,
  add_rug = TRUE,
  intgenes = NULL,
  intgenes_color = "steelblue",
  labels_intgenes = TRUE,
  labels_repel = TRUE
)
plot_ma(
  res_obj,
  FDR = 0.05,
  point_alpha = 0.2,
  sig_color = "red",
  annotation_obj = NULL,
  draw_y0 = TRUE,
  hlines = NULL,
  title = NULL,
  xlab = "mean of normalized counts - log10 scale",
  ylim = NULL,
  add_rug = TRUE,
  intgenes = NULL,
  intgenes_color = "steelblue",
  labels_intgenes = TRUE,
  labels_repel = TRUE
)

Arguments

`res_obj`	A `DESeqResults()` object
`FDR`	Numeric value, the significance level for thresholding adjusted p-values
`point_alpha`	Alpha transparency value for the points (0 = transparent, 1 = opaque)
`sig_color`	Color to use to mark differentially expressed genes. Defaults to red
`annotation_obj`	A `data.frame` object, with row.names as gene identifiers (e.g. ENSEMBL ids) and a column, `gene_name`, containing e.g. HGNC-based gene symbols. Optional
`draw_y0`	Logical, whether to draw the horizontal line at y=0. Defaults to TRUE.
`hlines`	The y coordinate (in absolute value) where to draw horizontal lines, optional
`title`	A title for the plot, optional
`xlab`	X axis label, defaults to "mean of normalized counts - log10 scale"
`ylim`	Vector of two numeric values, Y axis limits to restrict the view
`add_rug`	Logical, whether to add rug plots in the margins
`intgenes`	Vector of genes of interest. Gene symbols if a `symbol` column is provided in `res_obj`, or else the identifiers specified in the row names
`intgenes_color`	The color to use to mark the genes on the main plot.
`labels_intgenes`	Logical, whether to add the gene identifiers/names close to the marked plots
`labels_repel`	Logical, whether to use `geom_text_repel` for placing the labels on the features to mark

Details

The genes of interest are to be provided as gene symbols if a symbol column is provided in res_obj, or else by using the identifiers specified in the row names

Value

An object created by ggplot

Examples

library("airway")
data("airway", package = "airway")
airway
dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
  colData = colData(airway),
  design = ~ cell + dex
)
# subsetting for quicker run, ignore the next two commands if regularly using the function
gene_subset <- c(
  "ENSG00000103196", # CRISPLD2
  "ENSG00000120129", # DUSP1
  "ENSG00000163884", # KLF15
  "ENSG00000179094", # PER1
  rownames(dds_airway)[rep(c(rep(FALSE, 99), TRUE), length.out = nrow(dds_airway))]
) # 1% of ids
dds_airway <- dds_airway[gene_subset, ]

dds_airway <- DESeq2::DESeq(dds_airway)
res_airway <- DESeq2::results(dds_airway)

plot_ma(res_airway, FDR = 0.05, hlines = 1)

plot_ma(res_airway,
  FDR = 0.1,
  intgenes = c(
    "ENSG00000103196", # CRISPLD2
    "ENSG00000120129", # DUSP1
    "ENSG00000163884", # KLF15
    "ENSG00000179094"  # PER1
  ) 
)
library("airway")
data("airway", package = "airway")
airway
dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
  colData = colData(airway),
  design = ~ cell + dex
)
# subsetting for quicker run, ignore the next two commands if regularly using the function
gene_subset <- c(
  "ENSG00000103196", # CRISPLD2
  "ENSG00000120129", # DUSP1
  "ENSG00000163884", # KLF15
  "ENSG00000179094", # PER1
  rownames(dds_airway)[rep(c(rep(FALSE, 99), TRUE), length.out = nrow(dds_airway))]
) # 1% of ids
dds_airway <- dds_airway[gene_subset, ]

dds_airway <- DESeq2::DESeq(dds_airway)
res_airway <- DESeq2::results(dds_airway)

plot_ma(res_airway, FDR = 0.05, hlines = 1)

plot_ma(res_airway,
  FDR = 0.1,
  intgenes = c(
    "ENSG00000103196", # CRISPLD2
    "ENSG00000120129", # DUSP1
    "ENSG00000163884", # KLF15
    "ENSG00000179094"  # PER1
  ) 
)

Volcano plot for log fold changes and log p-values

Description

Volcano plot for log fold changes and log p-values in the ggplot2 framework, with additional support to annotate genes if provided.

Usage

plot_volcano(
  res_obj,
  FDR = 0.05,
  ylim_up = NULL,
  vlines = NULL,
  title = NULL,
  intgenes = NULL,
  intgenes_color = "steelblue",
  labels_intgenes = TRUE,
  labels_repel = TRUE
)
plot_volcano(
  res_obj,
  FDR = 0.05,
  ylim_up = NULL,
  vlines = NULL,
  title = NULL,
  intgenes = NULL,
  intgenes_color = "steelblue",
  labels_intgenes = TRUE,
  labels_repel = TRUE
)

Arguments

`res_obj`	A `DESeqResults()` object
`FDR`	Numeric value, the significance level for thresholding adjusted p-values
`ylim_up`	Numeric value, Y axis upper limits to restrict the view
`vlines`	The x coordinate (in absolute value) where to draw vertical lines, optional
`title`	A title for the plot, optional
`intgenes`	Vector of genes of interest. Gene symbols if a `symbol` column is provided in `res_obj`, or else the identifiers specified in the row names
`intgenes_color`	The color to use to mark the genes on the main plot.
`labels_intgenes`	Logical, whether to add the gene identifiers/names close to the marked plots
`labels_repel`	Logical, whether to use `geom_text_repel` for placing the labels on the features to mark

Details

The genes of interest are to be provided as gene symbols if a symbol column is provided in res_obj, or else b< using the identifiers specified in the row names

Value

An object created by ggplot

Examples

library("airway")
data("airway", package = "airway")
airway
dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
  colData = colData(airway),
  design = ~ cell + dex
)

# subsetting for quicker run, ignore the next two commands if regularly using the function
gene_subset <- c(
  "ENSG00000103196", # CRISPLD2
  "ENSG00000120129", # DUSP1
  "ENSG00000163884", # KLF15
  "ENSG00000179094", # PER1
  rownames(dds_airway)[rep(c(rep(FALSE, 99), TRUE), length.out = nrow(dds_airway))]
) # 1% of ids
dds_airway <- dds_airway[gene_subset, ]

dds_airway <- DESeq2::DESeq(dds_airway)
res_airway <- DESeq2::results(dds_airway)

plot_volcano(res_airway)
library("airway")
data("airway", package = "airway")
airway
dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
  colData = colData(airway),
  design = ~ cell + dex
)

# subsetting for quicker run, ignore the next two commands if regularly using the function
gene_subset <- c(
  "ENSG00000103196", # CRISPLD2
  "ENSG00000120129", # DUSP1
  "ENSG00000163884", # KLF15
  "ENSG00000179094", # PER1
  rownames(dds_airway)[rep(c(rep(FALSE, 99), TRUE), length.out = nrow(dds_airway))]
) # 1% of ids
dds_airway <- dds_airway[gene_subset, ]

dds_airway <- DESeq2::DESeq(dds_airway)
res_airway <- DESeq2::results(dds_airway)

plot_volcano(res_airway)

Read in a GMT file

Description

Returns a list of pathways from a GMT file.

Usage

read_gmt(gmtfile)
read_gmt(gmtfile)

Arguments

gmtfile

A character value, containing the location of the GMT formatted file. It can also be a file found online

Value

A list of vectors, one for each pathway in the GMT file.

Examples

# this example reads in the freely available pathways from wikipathways
## Not run: 
mysigs <- read_gmt(
  "http://data.wikipathways.org/20240910/gmt/wikipathways-20240910-gmt-Homo_sapiens.gmt"
)
head(mysigs)
# see how the gene identifiers are encoded as ENTREZ id

## End(Not run)
# this example reads in the freely available pathways from wikipathways
## Not run: 
mysigs <- read_gmt(
  "http://data.wikipathways.org/20240910/gmt/wikipathways-20240910-gmt-Homo_sapiens.gmt"
)
head(mysigs)
# see how the gene identifiers are encoded as ENTREZ id

## End(Not run)

Make an educated guess on the separator character

Description

This function tries to guess which separator was used in a text delimited file

Usage

sepguesser(file, sep_list = c(",", "\t", ";", " "))
sepguesser(file, sep_list = c(",", "\t", ";", " "))

Arguments

`file`	The name of the file which the data are to be read from
`sep_list`	A vector containing the candidates for being identified as separators. Defaults to `c(",", "\t", ";"," ")`

Value

A character value, corresponding to the guessed separator. One of "," (comma), "\t" (tab), ";" (semicolon)," " (whitespace)

Examples

sepguesser(system.file("extdata/design_commas.txt", package = "ideal"))
sepguesser(system.file("extdata/design_semicolons.txt", package = "ideal"))
sepguesser(system.file("extdata/design_spaces.txt", package = "ideal"))
mysep <- sepguesser(system.file("extdata/design_tabs.txt", package = "ideal"))

# to be used for reading in the same file, without having to specify the sep
sepguesser(system.file("extdata/design_commas.txt", package = "ideal"))
sepguesser(system.file("extdata/design_semicolons.txt", package = "ideal"))
sepguesser(system.file("extdata/design_spaces.txt", package = "ideal"))
mysep <- sepguesser(system.file("extdata/design_tabs.txt", package = "ideal"))

# to be used for reading in the same file, without having to specify the sep

Plot a heatmap of the gene signature on the data

Description

Plot a heatmap for the selected gene signature on the provided data, with the possibility to compactly display also DE only genes

Usage

sig_heatmap(
  vst_data,
  my_signature,
  res_data = NULL,
  FDR = 0.05,
  de_only = FALSE,
  annovec,
  title = "",
  cluster_rows = TRUE,
  cluster_cols = FALSE,
  anno_colData = NULL,
  center_mean = TRUE,
  scale_row = FALSE
)
sig_heatmap(
  vst_data,
  my_signature,
  res_data = NULL,
  FDR = 0.05,
  de_only = FALSE,
  annovec,
  title = "",
  cluster_rows = TRUE,
  cluster_cols = FALSE,
  anno_colData = NULL,
  center_mean = TRUE,
  scale_row = FALSE
)

Arguments

`vst_data`	A `DESeqTransform()` object - usually the variance stabilized transformed data, which will be used to extract the expression values
`my_signature`	A character vector, usually named, containing the genes which compose the gene signature
`res_data`	A `DESeqResults()` object. If not provided, it can be computed during the execution of the application
`FDR`	Numeric value between 0 and 1, the False Discovery Rate
`de_only`	Logical, whether to display only DE genes belonging to the pathway - defaults to FALSE
`annovec`	A named character vector, with the corresponding annotation across IDs
`title`	Character, title for the heatmap
`cluster_rows`	Logical, whether to cluster rows - defaults to TRUE
`cluster_cols`	Logical, whether to cluster column - defaults to FALSE. Recommended to be set to TRUE if de_only is also set to TRUE
`anno_colData`	Character vector, specifying the elements of the colData information to be displayed as a decoration of the heatmap. Can be a vector of any length, as long as these names are included as colData. Defaults to NULL, which would plot no annotation on the samples.
`center_mean`	Logical, whether to perform mean centering on the expression values. Defaults to TRUE, as it improves the general readability of the heatmap
`scale_row`	Logical, whether to perform row-based standardization of the expression values

Value

A plot based on the pheatmap function

Examples

# with the well known airway package...
library("airway")
data("airway", package = "airway")
airway
dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
  colData = colData(airway),
  design = ~ cell + dex
)
## Not run: 
dds_airway <- DESeq2::DESeq(dds_airway)
res_airway <- DESeq2::results(dds_airway)
vst_airway <- DESeq2::vst(dds_airway)
library(org.Hs.eg.db)
annovec <- mapIds(org.Hs.eg.db, rownames(dds_airway), "ENTREZID", "ENSEMBL")
mysignatures <- read_gmt(
  "http://data.wikipathways.org/20190210/gmt/wikipathways-20190210-gmt-Homo_sapiens.gmt"
)
mysignature_name <- "Lung fibrosis%WikiPathways_20190210%WP3624%Homo sapiens"
library(pheatmap)
sig_heatmap(vst_airway,
  mysignatures[[mysignature_name]],
  res_data = res_airway,
  de_only = TRUE,
  annovec = annovec,
  title = mysignature_name,
  cluster_cols = TRUE
)

## End(Not run)
# with the well known airway package...
library("airway")
data("airway", package = "airway")
airway
dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
  colData = colData(airway),
  design = ~ cell + dex
)
## Not run: 
dds_airway <- DESeq2::DESeq(dds_airway)
res_airway <- DESeq2::results(dds_airway)
vst_airway <- DESeq2::vst(dds_airway)
library(org.Hs.eg.db)
annovec <- mapIds(org.Hs.eg.db, rownames(dds_airway), "ENTREZID", "ENSEMBL")
mysignatures <- read_gmt(
  "http://data.wikipathways.org/20190210/gmt/wikipathways-20190210-gmt-Homo_sapiens.gmt"
)
mysignature_name <- "Lung fibrosis%WikiPathways_20190210%WP3624%Homo sapiens"
library(pheatmap)
sig_heatmap(vst_airway,
  mysignatures[[mysignature_name]],
  res_data = res_airway,
  de_only = TRUE,
  annovec = annovec,
  title = mysignature_name,
  cluster_cols = TRUE
)

## End(Not run)

wrapup_for_iSEE

Description

Combine data from a typical DESeq2 run

Usage

wrapup_for_iSEE(dds, res)
wrapup_for_iSEE(dds, res)

Arguments

`dds`	A `DESeqDataSet()` object.
`res`	A `DESeqResults()` object.

Details

Combines the DESeqDataSet input and DESeqResults into a SummarizedExperiment object, which can be readily explored with iSEE.

A typical usage would be after running the DESeq2 pipeline as specified in one of the workflows which include this package, e.g. in the context of the ideal package.

Value

A SummarizedExperiment object, with raw counts, normalized counts, and variance-stabilizing transformed counts in the assay slots; and with colData and rowData extracted from the corresponding input parameters

Examples

# with simulated data...
library(DESeq2)
dds <- DESeq2::makeExampleDESeqDataSet(n = 10000, m = 8)
dds <- DESeq(dds)
res <- results(dds)
se <- wrapup_for_iSEE(dds, res)
# library(iSEE)
# iSEE(se)
## Not run: 
# or with the well known airway package...
library("airway")
data("airway", package = "airway")
airway
dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
  colData = colData(airway),
  design = ~ cell + dex
)
dds_airway <- DESeq2::DESeq(dds_airway)
res_airway <- DESeq2::results(dds_airway)
se_airway <- wrapup_for_iSEE(dds_airway, res_airway)
# iSEE(se_airway)

## End(Not run)
# with simulated data...
library(DESeq2)
dds <- DESeq2::makeExampleDESeqDataSet(n = 10000, m = 8)
dds <- DESeq(dds)
res <- results(dds)
se <- wrapup_for_iSEE(dds, res)
# library(iSEE)
# iSEE(se)
## Not run: 
# or with the well known airway package...
library("airway")
data("airway", package = "airway")
airway
dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
  colData = colData(airway),
  design = ~ cell + dex
)
dds_airway <- DESeq2::DESeq(dds_airway)
res_airway <- DESeq2::results(dds_airway)
se_airway <- wrapup_for_iSEE(dds_airway, res_airway)
# iSEE(se_airway)

## End(Not run)

Package 'ideal'

Help Index

Deprecated functions in ideal

Description

Arguments

Details

Value

Transitioning to the mosdef framework

Author(s)

Examples

Generate a tidy table with the DE genes from the results of DESeq

Description

Usage

Arguments

Value

Examples

Generate a tidy table with the results of DESeq

Description

Usage

Arguments

Value

Examples

Plot normalized counts for a gene

Description

Usage

Arguments

Details

Value

Examples

Extract functional terms enriched in the DE genes, based on goseq

Description

Usage

Arguments

Details

Value

Examples

ideal: Interactive Differential Expression Analysis

Description

Usage

Arguments

Value

Examples

ideal: Interactive Differential Expression Analysis

Description

Details

Author(s)

See Also

MA-plot from base means and log fold changes

Description

Usage

Arguments

Details

Value

Examples

Volcano plot for log fold changes and log p-values

Description

Usage

Arguments

Details

Value

Examples

Read in a GMT file

Description

Usage

Arguments

Value

Examples

Make an educated guess on the separator character

Description

Usage

Arguments

Value

Examples

Plot a heatmap of the gene signature on the data

Description

Usage

Arguments

Value

Examples

wrapup_for_iSEE