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.
Authors:	Federico Marini [aut, cre]
Maintainer:	Federico Marini <[email protected]>
License:	MIT + file LICENSE
Version:	1.29.0
Built:	2024-07-05 02:51:52 UTC
Source:	https://github.com/bioc/ideal

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)
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)
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)
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 <- deseqresult2DEgenes(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)
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 <- deseqresult2DEgenes(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)
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)
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)
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)
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)
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)
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/20180910/gmt/wikipathways-20180910-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/20180910/gmt/wikipathways-20180910-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)
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)
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)
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)
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

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

Description

Usage

Arguments

Details

Value

Examples