EGSEAResults-methods: The EGSEAResults class

In malhamdoosh/EGSEA: Ensemble of Gene Set Enrichment Analyses

The EGSEAResults class

Description

The EGSEAResults class stores the results of an EGSEA analysis.

The opertator $ extracts a slot from an object of class EGSEAResults.

topSets extracts a table of the top-ranked gene sets from an EGSEA analysis.

show displays the parameters of an EGSEAResults object

summary displays a brief summary of the analysis results stored in an EGSEAResults object

limmaTopTable returns a dataframe of the top table of the limma analysis for a given contrast.

generateReport creates an HTML report for the EGSEA analysis that enables users to seamlessly browse the test results.

getlimmaResults returns the linear model fit produced by limma::eBayes.

plotHeatmap generates a heatmap of fold changes for a selected gene set.

plotSummaryHeatmap generates a summary heatmap for the top n gene sets of the comparative analysis across multiple contrasts.

plotPathway generates a visual map for a selected KEGG pathway with the gene fold changes overalid on it.

plotMethods generates a multi-dimensional scaling (MDS) plot for the gene set rankings of different base GSE methods

plotSummary generates a Summary plot for EGSEA analysis.

plotGOGraph generates a graph of the top significant GO terms in a GO term collection, which could be c5 from MSigDB or Gene Ontolog from the GeneSetDB.

plotBars generates a multi-dimensional scaling (MDS) plot for the gene set rankings of different base GSE methods

showSetByname shows the details of a given gene set indicated by name.

showSetByID shows the details of a given gene set indicated by ID.

getSetScores returns a dataframe of the gene set enrichment scores per sample. This can be only calculated using specific base methods, namely, "ssgsea".

Usage

## S4 method for signature 'EGSEAResults'
x$name

topSets(
  object,
  gs.label = 1,
  contrast = 1,
  sort.by = NULL,
  number = 10,
  names.only = TRUE,
  verbose = TRUE
)

## S4 method for signature 'EGSEAResults'
show(object)

## S4 method for signature 'EGSEAResults'
summary(object)

limmaTopTable(object, contrast = 1)

generateReport(
  object,
  number = 20,
  sort.by = NULL,
  report.dir = NULL,
  kegg.dir = NULL,
  x.axis = NULL,
  x.cutoff = NULL,
  num.threads = 4,
  print.base = FALSE,
  interactive = FALSE,
  verbose = FALSE
)

getlimmaResults(object)

plotHeatmap(
  object,
  gene.set,
  gs.label = 1,
  contrast = 1,
  file.name = "heatmap",
  format = "pdf",
  fc.colors = c("#67A9CF", "#F7F7F7", "#EF8A62"),
  verbose = TRUE
)

plotSummaryHeatmap(
  object,
  gs.label = 1,
  number = 20,
  sort.by = NULL,
  hm.vals = NULL,
  show.vals = NULL,
  file.name = "sum_heatmap",
  format = "pdf",
  verbose = TRUE
)

plotPathway(
  object,
  gene.set,
  gs.label = 1,
  contrast = 1,
  file.name = "pathway",
  verbose = TRUE
)

plotMethods(
  object,
  gs.label = 1,
  contrast = 1,
  file.name = "methods.mds",
  format = "pdf",
  verbose = TRUE
)

plotSummary(
  object,
  gs.label = 1,
  contrast = 1,
  file.name = "summary",
  format = "pdf",
  x.axis = "p.adj",
  x.cutoff = NULL,
  sort.by = NULL,
  use.names = FALSE,
  interactive = FALSE,
  verbose = TRUE
)

plotGOGraph(
  object,
  gs.label = "c5",
  contrast = 1,
  sort.by = NULL,
  noSig = 5,
  file.name = "c5-top-",
  format = "pdf",
  verbose = TRUE
)

plotBars(
  object,
  gs.label = 1,
  contrast = 1,
  number = 20,
  sort.by = NULL,
  bar.vals = "p.adj",
  file.name = "bars_plot",
  format = "pdf",
  verbose = TRUE
)

showSetByName(object, gs.label = 1, set.name)

showSetByID(object, gs.label = 1, id)

getSetScores(object, gs.label = 1)

Arguments

x	EGSEAResults object, the analysis result object from egsea, egsea.cnt or egsea.ora.
name	character, the slot name
object	EGSEAResults object, the analysis result object from egsea, egsea.cnt or egsea.ora.
gs.label	the number or label of the gene set collection of interest.
contrast	contrast column number or column name specifying which contrast is of interest. if contrast = 0 or "comparison" and the number of contrasts greater than 1, the comparative gene sets are retruned.
sort.by	character, determines how to order the analysis results in the stats table. The accepted values depend on the function used to generate the EGSEA results.
number	integer, maximum number of gene sets to list
names.only	logical, whether to display the EGSEA statistics or not.
verbose	logical, whether to print out progress messages and warnings.
report.dir	character, directory into which the analysis results are written out.
kegg.dir	character, the directory of KEGG pathway data file (.xml) and image file (.png). Default kegg.dir=paste0(report.dir, "/kegg-dir/").
x.axis	character, the x-axis of the summary plot. All the values accepted by the sort.by parameter can be used. Default x.axis="p.value".
x.cutoff	numeric, cut-off threshold to filter the gene sets of the summary plots based on the values of the x.axis. Default x.cutoff=NULL.
num.threads	numeric, number of CPU cores to be used. Default num.threads=4.
print.base	logical, whether to write out the analysis results of the base methods. Default is False.
interactive	logical, whether to generate interactive tables and plots. Note this might dramatically increase the size of the EGSEA report.
gene.set	character, the name of the gene set. See the output of topSets.
file.name	character, the prefix of the output file name.
format	character, takes "pdf" or "png".
fc.colors	vector, determines the fold change colors of the heatmap. Three colors of the negative, zero and positive log fold changes, respectively, should be assigned. Default is c( "#67A9CF", "#F7F7F7", "#EF8A62"). These colors were generated using rev(RColorBrewer::brewer.pal(3, "RdBu"))
hm.vals	character, determines which EGSEA score values are used to draw the map. Default is NULL which implies using the sort.by score.
show.vals	character, determines which EGSEA score values are displayed on the map. Default is NULL which does not show anything.
use.names	logical, determines whether to display the GeneSet IDs or GeneSet Names. Default is FALSE.
noSig	numeric, number of significant GO terms to be displayed. A number larger than 5 might not work due to the size of the generated graph.
bar.vals	character, determines which EGSEA score values are used to draw the bars. Default is NULL which implies using the sort.by score.
set.name	character, a vector of gene set names as they appear in topSets.
id	character, a vector of gene set IDs as they appears in the plotSummary.

Details

The EGSEAResults class is used by egsea, egsea.cnt and egsea.ora to store the results of an EGSEA analysis. This helps in mining the analysis results and generating customized tables and plots.

limmaTopTable output can be understood from limma::topTable.

EGSEA report is an interactive HTML report that is generated to enable a swift navigation through the results of an EGSEA analysis. The following pages are generated for each gene set collection and contrast/comparison:
1. Stats Table page shows the detailed statistics of the EGSEA analysis for the display.top gene sets. It shows the EGSEA scores, individual rankings and additional annotation for each gene set. Hyperlinks to the source of each gene set can be seen in this table when they are available. The "Direction" column shows the regulation direction of a gene set which is calculated based on the logFC, which is either calculated from the limma differential expression analysis or provided by the user. The method topSets can be used to generate custom Stats Table.
2. Heatmaps page shows the heatmaps of the gene fold changes for the gene sets that are presented in the Stats Table page. Red indicates up-regulation while blue indicates down-regulation. Only genes that appear in the input expression/count matrix are visualized in the heat map. Gene names are coloured based on their statistical significance in the limma differential expression analysis. The "Interpret Results" link below each heat map allows the user to download the original heat map values along with additional statistics from limma DE analysis ( if available) so that they can be used to perform further analysis in R, e.g., customizing the heat map visualization. Additional heat maps can be generated and customized using the method plotHeatmap.
3. Summary Plots page shows the methods ranking plot along with the summary plots of EGSEA analysis. The method plot uses multidimensional scaling (MDS) to visualize the ranking of individual methods on a given gene set collection. The summary plots are bubble plots that visualize the distribution of gene sets based on the EGSEA Significance Score and another EGSEA score (default, p-value). Two summary plots are generated: ranking and directional plots. Each gene set is reprersented with a bubble which is coloured based on the EGSEA ranking (in ranking plots ) or gene set regulation direction (in directional plots) and sized based on the gene set cardinality (in ranking plots) or EGSEA Significance score (in directional plots). Since the EGSEA "Significance Score" is proportional to the p-value and the absolute fold changes, it could be useful to highlight gene sets that have high Significance scores. The blue labels on the summary plot indicate gene sets that do not appear in the top 10 list of gene sets based on the "sort.by" argument (black labels) yet they appear in the top 5 list of gene sets based on the EGSEA "Significance Score". If two contrasts are provided, the rank is calculated based on the "comparison" analysis results and the "Significance Score" is calculated as the mean. The method plotSummary can be used to customize the Summary plots by changing the x-axis score and filtering bubbles based on the values of the x-axis. The method plotMethods can be used to generate Method plots.
4. Pathways page shows the KEGG pathways for the gene sets that are presented in the Stats Table of a KEGG gene set collection. The gene fold changes are overlaid on the pathway maps and coloured based on the gene regulation direction: blue for down-regulation and red for up-regulation. The method plotPathway can be used to generate additional pathway maps. Note that this page only appears if a KEGG gene set collection is used in the EGSEA analysis.
5. Go Graphs page shows the Gene Ontology graphs for top 5 GO terms in each of three GO categories: Biological Processes (BP), Molecular Functions (MF), and Cellular Components (CC). Nodes are coloured based on the default sort.by score where red indicates high significance and yellow indicates low significance. The method plotGOGraph can be used to customize GO graphs by changing the default sorting score and the number of significance nodes that can be visualized. It is recommended that a small number of nodes is selected. Note that this page only appears if a Gene Ontology gene set collection is used, i.e., for the c5 collection from MSigDB or the gsdbgo collection from GeneSetDB.

Finally, the "Interpret Results" hyperlink in the EGSEA report allows the user to download the fold changes and limma analysis results and thus improve the interpretation of the results.

getlimmaResults's output can be manipulated using limma::topTable and limma::topTreat.

plotHeatmap fold changes are colored based on the fc.colors and only genes that appear in the EGSEA analysis are visualized in the heatmap. Gene names are coloured based on the statistical significance level from limma DE analysis.

plotSummaryHeatmap creates a summary heatmap for the rankings of top number gene sets of the comparative analysis across all the contrasts. The show.vals score can be displayed on the heatmap for each gene set. This can help to identify gene sets that are highly ranked/sgnificant across multiple contrasts.

plotSummary generates a Summmary Plot for an EGSEA analysis. Since the EGSEA "Significance Score" is proportional to the p-value and the absolute fold changes, it could be useful to highlight gene sets that have high Significance scores. The blue labels on the summary plot indicate gene sets that do not apear in the top 10 list of gene sets based on the "sort.by" argument (black labels) yet they appear in the top 5 list of gene sets based on the EGSEA "Significance Score". If two contrasts are provided, the rank is calculated based on the "comparison" analysis results and the "Significance Score" is calculated as the mean. If sort.by = NULL, the slot sort.by of the object is used to order gene sets.

Value

$ returns the selected slot.

topSets returns a dataframe of top gene sets with the calculated statistics for each if names.only = FALSE.

show does not return data.

summary does not return data.

limmaTopTable returns a dataframe.

generateReport does not return data but creates an HTML report.

getlimmaResults returns an MArrayLM object.

plotHeatmap does not return data but creates image and CSV files.

plotSummaryHeatmap does not return data but creates image and CSV files.

plotPathway does not return data but creates a file.

plotMethods does not reutrn data but creates an image file.

plotSummary does not return data but creates an image file.

plotGOGraph does not return data but creates an image file.

plotBars does not reutrn data but creates an image file.

showSetByName does not return data

showSetByID does not return data.

getSetScores returnsa a dataframe where rows are gene sets and columns are samples.

Slots

results

list, EGSEA analysis results

limmaResults

MArrayLM, is a limma linear fit model

contr.names

character, the contrasts defined in the analysis

contrast

double, an N x L matrix indicates the contrasts of the linear model coefficients for which the test is required. N is the number of columns of the design matrix and L is number of contrasts. Can be also a vector of integers that specify the columns of the design matrix.

sampleSize

numeric, number of samples

gs.annots

list, the gene set collection annotation index

baseMethods

character, vector of base GSE methods

baseInfo

list, additional information on the base methods (e.g., version).

combineMethod

character, the p-value combining method

sort.by

character, the results ordering argument

symbolsMap

data.frame, the mapping between Entrez IDs and Gene Symbols

logFC

matrix, the logFC matrix of contrasts

logFC.calculated

character, indicates whether the logFC was calculated using limma DE analysis.

sum.plot.axis

character, the x-axis of the summary plot

sum.plot.cutoff

numeric, the cut-off threshold for the summary plot x-axis

report

logical, whether the report was generated

report.dir

character, the directory of the EGSEA HTML report

egsea.version

character, the version of EGSEA package

egseaData.version

character, the version of EGSEAdata package

Examples

# Exampple of EGSEAResults
library(EGSEAdata)
data(il13.gsa)
gsa = il13.gsa
class(gsa)
print(gsa$baseMethods)

# Example of topSets
library(EGSEAdata)
data(il13.gsa)
gsa = il13.gsa
class(gsa)
topSets(gsa,  gs.label="kegg",contrast=1, number = 10)
topSets(gsa,  gs.label=1, contrast=1, sort.by="ora", number = 10, 
names.only=FALSE)
topSets(gsa,  gs.label="kegg",contrast=0, number = 10)

# Example of show
library(EGSEAdata)
data(il13.gsa)
gsa = il13.gsa
class(gsa)
show(gsa)

# Example of summary
library(EGSEAdata)
data(il13.gsa)
gsa = il13.gsa
class(gsa)
summary(gsa)

# Example of limmaTopTable
library(EGSEAdata)
data(il13.gsa)
gsa = il13.gsa
class(gsa)
colnames(limmaTopTable(gsa))
head(limmaTopTable(gsa))

# Example of generateReport
library(EGSEAdata)
data(il13.gsa)
gsa = il13.gsa
# generateReport(gsa)

# Example of getlimmaResults
library(EGSEAdata)
data(il13.gsa)
gsa = il13.gsa
class(gsa)
fit = getlimmaResults(gsa)
class(fit)
names(fit)

# Example of plotHeatmap
library(EGSEAdata)
data(il13.gsa)
gsa = il13.gsa
class(gsa)
plotHeatmap(gsa, "Asthma", gs.label="kegg")
plotHeatmap(gsa, "Asthma", gs.label="kegg", contrast = "comparison", 
file.name = "asthma.hm.cmp")

# Example of plotSummaryHeatmap
library(EGSEAdata)
data(il13.gsa)
gsa = il13.gsa
class(gsa)
plotSummaryHeatmap(gsa, gs.label="kegg")

# Example of plotPathway
library(EGSEAdata)
data(il13.gsa)
gsa = il13.gsa
class(gsa)
plotPathway(gsa, gs.label="kegg", "Asthma")
plotPathway(gsa, gs.label="kegg", "Asthma", contrast="comparison", 
file.name = "asthma.map.cmp")

# Example of plotMethods
library(EGSEAdata)
data(il13.gsa)
gsa = il13.gsa
class(gsa)
plotMethods(gsa)

# Example of plotSummary
library(EGSEAdata)
data(il13.gsa)
gsa = il13.gsa
class(gsa)
plotSummary(gsa)
plotSummary(gsa, contrast=c(1,2), file.name = "summary.cmp")

# Example of plotGOGraph
library(EGSEAdata)
data(il13.gsa)
gsa = il13.gsa
class(gsa)
plotGOGraph(gsa, sort.by="avg.rank")

# Example of plotBars
library(EGSEAdata)
data(il13.gsa)
gsa = il13.gsa
class(gsa)
plotBars(gsa)

# Example of showSetByName
library(EGSEAdata)
data(il13.gsa)
gsa = il13.gsa
class(gsa)
showSetByName(gsa, "kegg", "Asthma")

# Example of showSetByID
library(EGSEAdata)
data(il13.gsa)
gsa = il13.gsa
class(gsa)
showSetByID(gsa, "kegg", "hsa04060")

# Example of getSetScores
library(EGSEAdata)
data(il13.gsa)
gsa = il13.gsa
class(gsa)
head(getSetScores(gsa, "kegg"))

malhamdoosh/EGSEA documentation built on Jan. 28, 2024, 1:17 p.m.