Package 'GseaVis' reference manual

Title:	Implement for 'GSEA' Enrichment Visualization
Description:	Mark your interesting genes on plot and support more parameters to handle your own gene set enrichment analysis plot.
Authors:	Jun Zhang [aut, cre] , Guangchuang Yu [ctb]
Maintainer:	Jun Zhang <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.1
Built:	2025-02-11 06:00:34 UTC
Source:	https://github.com/junjunlab/gseavis

Circular Gene Set Enrichment Analysis Plot

Description

This function creates a circular plot for gene set enrichment analysis.

Usage

circGsea(
  object = NULL,
  geneSetID = NULL,
  htCol = c("#08519C", "#A50F15"),
  segmentsCol = c("#0099CC", "#FF0033"),
  bgCol = "grey80",
  curveCol = "#CC3333",
  pointSize = 0.5,
  addPoints = FALSE,
  type = c("c", "h", "m"),
  addGeneRank = FALSE,
  markGene = NULL,
  markGeneSize = 0.5,
  GoIDfacing = "inside",
  GoIDsize = 0.75,
  addDescription = FALSE,
  descriptionFacing = "bending.outside",
  descripShift = 1,
  descripGap = 0.3,
  descripLength = 40,
  descripSize = 0.75
)
circGsea(
  object = NULL,
  geneSetID = NULL,
  htCol = c("#08519C", "#A50F15"),
  segmentsCol = c("#0099CC", "#FF0033"),
  bgCol = "grey80",
  curveCol = "#CC3333",
  pointSize = 0.5,
  addPoints = FALSE,
  type = c("c", "h", "m"),
  addGeneRank = FALSE,
  markGene = NULL,
  markGeneSize = 0.5,
  GoIDfacing = "inside",
  GoIDsize = 0.75,
  addDescription = FALSE,
  descriptionFacing = "bending.outside",
  descripShift = 1,
  descripGap = 0.3,
  descripLength = 40,
  descripSize = 0.75
)

Arguments

`object`	An object containing enrichment results from clusterProfiler.
`geneSetID`	A vector of gene set IDs to be plotted.
`htCol`	A vector of two colors for the heatmap gradient. Default is c("#08519C", "#A50F15").
`segmentsCol`	A vector of two colors for the segment gradient. Default is c("#0099CC", "#FF0033").
`bgCol`	Background color for the sectors. Default is "grey80".
`curveCol`	Color for the runningScore curve. Default is "#CC3333".
`pointSize`	Size of the points on the runningScore curve. Default is 0.5.
`addPoints`	Logical indicating whether to add points to the runningScore curve. Default is FALSE.
`type`	Type of plot to display: 'c' for heatmap only, 'h' for segments and heatmap, 'm' for segments only. Default is "c".
`addGeneRank`	Logical indicating whether to add a track for gene ranks. Default is FALSE.
`markGene`	A vector of gene symbols to be marked on the plot. Default is NULL.
`markGeneSize`	Size of the marked gene symbols. Default is 0.5.
`GoIDfacing`	Facing direction of the gene set ID labels. Default is "inside". Additional choice are "inside", "outside", "reverse.clockwise", "clockwise","downward", "bending", "bending.inside", "bending.outside".
`GoIDsize`	Size of the gene set ID labels. Default is 0.75.
`addDescription`	Logical indicating whether to add term descriptions. Default is FALSE.
`descriptionFacing`	Facing direction of the term description text. Default is "bending.outside". Additional choice are "inside", "outside", "reverse.clockwise", "clockwise","downward", "bending", "bending.inside", "bending.outside".
`descripShift`	Shift of the term description text. Default is 1.
`descripGap`	Gap between lines of the term description text. Default is 0.3.
`descripLength`	Maximum line length for the term description text. Default is 40.
`descripSize`	Size of the term description text. Default is 0.75.

Value

A circular gene set enrichment analysis plot.

Create gseaResult Object from Enrichment Analysis Output Dataframe

Description

This function creates a gseaResult object from the results of enrichment analysis using gene set enrichment analysis (GSEA) for a given gene list and Gene Ontology (GO) data.

Usage

dfGO2gseaResult(
  enrich.df = NULL,
  geneList = NULL,
  OrgDb = NULL,
  own_termSet = NULL,
  keytype = "ENTREZID",
  setType = c("BP", "CC", "MF", "ALL"),
  pvalueCutoff = 0.05,
  eps = 1e-10,
  pAdjustMethod = "BH",
  exponent = 1,
  minGSSize = 10,
  maxGSSize = 500
)
dfGO2gseaResult(
  enrich.df = NULL,
  geneList = NULL,
  OrgDb = NULL,
  own_termSet = NULL,
  keytype = "ENTREZID",
  setType = c("BP", "CC", "MF", "ALL"),
  pvalueCutoff = 0.05,
  eps = 1e-10,
  pAdjustMethod = "BH",
  exponent = 1,
  minGSSize = 10,
  maxGSSize = 500
)

Arguments

`enrich.df`	A data frame containing the results of enrichment analysis.
`geneList`	A decreasing sorted vector of gene list.
`OrgDb`	The organism-specific annotation database.
`own_termSet`	The own term-gene list if the enrichment analysis not rely on OrgDb package.
`keytype`	The type of gene identifier used in the analysis (default is "ENTREZID").
`setType`	The type of GO ontology to use ("BP" for Biological Process, "CC" for Cellular Component, "MF" for Molecular Function, or "ALL" for all ontologies).
`pvalueCutoff`	The p-value cutoff for significance (default is 0.05).
`eps`	A small value to avoid division by zero (default is 1e-10).
`pAdjustMethod`	The p-value adjustment method (default is "BH" for Benjamini-Hochberg).
`exponent`	The exponent for weighting p-values (default is 1).
`minGSSize`	The minimum gene set size (default is 10).
`maxGSSize`	The maximum gene set size (default is 500).

Value

A gseaResult object containing the results of GSEA.

Create gseaResult Object from KEGG Enrichment Analysis Output Dataframe

Description

This function creates a gseaResult object from the results of KEGG enrichment analysis for a given gene list and organism.

Usage

dfKEGG2gseaResult(
  enrich.df = NULL,
  geneList = NULL,
  organism = "hsa",
  keytype = "kegg",
  setType = "KEGG",
  use_internal_data = FALSE,
  pvalueCutoff = 0.05,
  eps = 1e-10,
  pAdjustMethod = "BH",
  exponent = 1,
  minGSSize = 120,
  maxGSSize = 500
)
dfKEGG2gseaResult(
  enrich.df = NULL,
  geneList = NULL,
  organism = "hsa",
  keytype = "kegg",
  setType = "KEGG",
  use_internal_data = FALSE,
  pvalueCutoff = 0.05,
  eps = 1e-10,
  pAdjustMethod = "BH",
  exponent = 1,
  minGSSize = 120,
  maxGSSize = 500
)

Arguments

`enrich.df`	A data frame containing the results of KEGG enrichment analysis.
`geneList`	A decreasing sorted vector of gene identifiers.
`organism`	The organism for KEGG enrichment analysis (default is "hsa" for Homo sapiens).
`keytype`	The type of gene identifier used in the analysis (default is "kegg").
`setType`	The type of enrichment analysis ("KEGG" for KEGG pathways).
`use_internal_data`	Logical value indicating whether to use internal KEGG data (default is FALSE).
`pvalueCutoff`	The p-value cutoff for significance (default is 0.05).
`eps`	A small value to avoid division by zero (default is 1e-10).
`pAdjustMethod`	The p-value adjustment method (default is "BH" for Benjamini-Hochberg).
`exponent`	The exponent for weighting p-values (default is 1).
`minGSSize`	The minimum gene set size (default is 120).
`maxGSSize`	The maximum gene set size (default is 500).

Value

A gseaResult object containing the results of KEGG enrichment analysis.

dotplotGsea

Description

dotplotGsea

Arguments

`data`	GSEA enrich object from clusterProfiler, defalut is NULL.
`pval`	pvalue cutoff to select significant terms, defalut is NULL.
`pajust`	adjusted pvalue cutoff to select significant terms, defalut is 0.05.
`order.by`	the X axis, defalut is "GeneRatio".
`str.width`	the width of term name, defalut is 50.
`base_size`	theme base size, defalut is 12.
`topn`	show the top terms, defalut is NULL.
`scales`	facet scales, defalut is "free_x".
`add.seg`	whether add segment line to point, defalut is "FALSE".
`line.col`	segment line color, defalut is "grey80".
`line.size`	segment line size, defalut is 1.5.
`line.type`	segment line type, defalut is "solid".

Value

a ggplot object.

Author(s)

Jun Zhang

Generate Enrichment Ridge Plot

Description

This function creates a ridge plot for visualizing gene enrichment analysis results using either enrichResult or gseaResult objects. Ridge plots allow for the visualization of the distribution of genes across multiple terms and enriched pathways.

Usage

enrich_ridge_plot(
  object = NULL,
  terms_ID = NULL,
  gene_list = NULL,
  term_name_width = 60,
  geom_density_ridges_params = list()
)
enrich_ridge_plot(
  object = NULL,
  terms_ID = NULL,
  gene_list = NULL,
  term_name_width = 60,
  geom_density_ridges_params = list()
)

Arguments

`object`	An `enrichResult` or `gseaResult` object obtained from enrichment analysis.
`terms_ID`	A vector of term IDs (corresponding to enriched pathways or terms) to be included in the plot.
`gene_list`	A data frame with two columns("id","logfc") providing the gene fold changes (log fold changes).
`term_name_width`	(optional) Maximum width of the term name for wrapping text in the y-axis. Default is 60.
`geom_density_ridges_params`	(optional) A list of additional parameters passed to `geom_density_ridges` to customize the ridge plot appearance.

Details

This function accepts two types of enrichment result objects: enrichResult and gseaResult. For both types, the function handles gene identifiers (either raw gene IDs or human-readable names) and generates the appropriate data frames with corresponding log fold change values. It then filters and plots the specified terms.

The ridge plot helps visualize how genes for each term distribute across log fold change values, and it also highlights the statistical significance of the terms using a color gradient based on the p-value.

Value

A ggplot object which contains the ridge plot visualizing the log fold change distribution of enriched terms.

Perform GSEA Multi-group Plotting

Description

This function generates a multi-group plot for Gene Set Enrichment Analysis (GSEA) results.

Usage

GSEAmultiGP(
  gsea_list = NULL,
  geneSetID = NULL,
  exp_name = NULL,
  addPval = FALSE,
  curve.col = NULL,
  curve.linewidth = 1,
  kegg = FALSE,
  lineSize = 1,
  base_size = 12,
  nesDigit = 2,
  pDigit = 2,
  pvalX = 0.9,
  pvalY = 0.9,
  rect.bm.col = c("#CC3333", "white", "#003366"),
  subplot.heights = c(0.4, 0.2, 0.08),
  legend.position = c(0.85, 0.85),
  rect.bm.label = c("Up regulated", "Down regulated"),
  breaks.n = 6
)
GSEAmultiGP(
  gsea_list = NULL,
  geneSetID = NULL,
  exp_name = NULL,
  addPval = FALSE,
  curve.col = NULL,
  curve.linewidth = 1,
  kegg = FALSE,
  lineSize = 1,
  base_size = 12,
  nesDigit = 2,
  pDigit = 2,
  pvalX = 0.9,
  pvalY = 0.9,
  rect.bm.col = c("#CC3333", "white", "#003366"),
  subplot.heights = c(0.4, 0.2, 0.08),
  legend.position = c(0.85, 0.85),
  rect.bm.label = c("Up regulated", "Down regulated"),
  breaks.n = 6
)

Arguments

`gsea_list`	A list of GSEA results for multiple experiments.
`geneSetID`	The ID of the gene set to be visualized.
`exp_name`	Names of the experiments corresponding to the GSEA results.
`addPval`	Logical, indicating whether to add NES (Normalized Enrichment Score) and p-value labels to the plot. Default is FALSE.
`curve.col`	A vector of colors for the curves representing different experiments. If NULL, random colors are assigned.
`curve.linewidth`	The curve linewidth. Default is 1.
`kegg`	Logical, indicating whether the gene set is a KEGG pathway. Default is FALSE.
`lineSize`	The size of the lines in the enrichment score curve plot. Default is 1.
`base_size`	The base font size for the plot. Default is 12.
`nesDigit`	The number of digits to round NES values to. Default is 2.
`pDigit`	The number of digits to round p-values to. Default is 2.
`pvalX`	The X-coordinate for placing p-value labels on the plot. Default is 0.9.
`pvalY`	The Y-coordinate for placing p-value labels on the plot. Default is 0.9.
`rect.bm.col`	A vector of colors for the bottom rectangle representing up-regulated and down-regulated genes. Default colors c("#CC3333","white","#003366") are provided.
`subplot.heights`	Heights of subplots in the multi-group plot. Default values c(0.4,0.2,0.08) are provided.
`legend.position`	The position of the legend in the plot. Default is (0.85, 0.85).
`rect.bm.label`	Labels for the bottom rectangle, specifying "Up regulated" and "Down regulated". Default labels are provided.
`breaks.n`	The number of X axis breaks. Default is 6.

Value

Returns a multi-panel plot for GSEA results.

Author(s)

Jun Zhang

Examples

## Not run: 
# Example Usage
result <- GSEAmultiGP(gsea_list = gsea_results,
                      geneSetID = "gene_set_1",
                      exp_name = c("Exp1", "Exp2"),
                      addPval = TRUE,
                      curve.col = c("red", "blue"),
                      pvalX = 0.9,
                      pvalY = 0.9,
                      rect.bm.col = c("#CC3333", "white", "#003366"),
                      subplot.heights = c(0.4, 0.2, 0.08),
                      legend.position = c(0.85, 0.85),
                      rect.bm.label = c("Up regulated", "Down regulated"))
                      
## End(Not run)

## Not run: 
# Example Usage
result <- GSEAmultiGP(gsea_list = gsea_results,
                      geneSetID = "gene_set_1",
                      exp_name = c("Exp1", "Exp2"),
                      addPval = TRUE,
                      curve.col = c("red", "blue"),
                      pvalX = 0.9,
                      pvalY = 0.9,
                      rect.bm.col = c("#CC3333", "white", "#003366"),
                      subplot.heights = c(0.4, 0.2, 0.08),
                      legend.position = c(0.85, 0.85),
                      rect.bm.label = c("Up regulated", "Down regulated"))
                      
## End(Not run)

gseaNb

Description

gseaNb

Arguments

`object`	GSEA enrich results.
`filePath`	filePath the path of the GSEA software enrichment outputs or "readGseaFile" object, defalut is NULL.
`subPlot`	which plot to show, 1/2/3, default is 3.
`lineSize`	curve line size. default is 0.8.
`geneSetID`	which pathway name to plot.
`rmSegment`	whether to remove segment on the curve plot, default is FALSE.
`termWidth`	the width or the term name, defalut is 40.
`segCol`	segment color on the curves, defalut is "red".
`addGene`	whether add gene name on the curve, defalut is FALSE.
`geneCol`	gene name label color, defalut is NULL.
`arrowAngle`	arrow angle, defalut is 20.
`arrowLength`	arrow line length, defalut is 0.2.
`arrowEnd`	arrow end, defalut is "last".
`arrowType`	arrow type, defalut is "closed".
`curveCol`	curve color, defalut is c("#76BA99", "#EB4747", "#996699").
`htCol`	heatmap color, defalut is c("#08519C", "#A50F15").
`rankCol`	gene rank fill color, defalut is c("grey").
`rankSeq`	gene rank plot X axis breaks, defalt is 5000.
`htHeight`	the relative height when "subplot = 2" to the vertical line plot, defalut is 0.3.
`force`	the gene label force, refer to geom_text_repel function, defalut is 20.
`max.overlaps`	refer to geom_text_repel function, defalut is 50.
`geneSize`	gene label text size, defalut is 4.
`newGsea`	whether show new style of plot, defalut is FALSE.
`addPoint`	new style plot with point layer, defalut is TRUE.
`newCurveCol`	new style plot curve color, defalut is c("#336699", "white", "#993399").
`newHtCol`	new style plot heatmap color, defalut is c("#336699", "white", "#993399").
`rmHt`	whether remove new style plot heatmap, defalut is FALSE.
`addPval`	whether add pvalue and NES, defalut is FALSE.
`pvalX`	set pvalue label x position, defalut is 0.9.
`pvalY`	set pvalue label y position, defalut is 0.9.
`pvalSize`	set pvalue label text size, defalut is 4.
`pCol`	pvalue label color, defalut is "grey30".
`pHjust`	pvalue label hjust, defalut is 1.
`rmPrefix`	whether remove GO term prefix like "GOBP/KEGG/CC/MF_*", defalut is TRUE.
`nesDigit`	the NES score digits retained, defalut is 2.
`pDigit`	the pvalue and pajust value digits retained, defalut is 2.
`markTopgene`	whether add top n genes on plot, defalut is FALSE.
`topGeneN`	the number of genes to be marked on plot, defalut is 5.
`kegg`	whether input is gseKEGG object, defalut is FALSE.
`legend.position`	the legend position, defalut is "right".
`whether`	add target gene expression heatmap, defalut is FALSE.
`exp`	the expression matrix,tpm/fpkm/rpkm format, defalut is NULL.
`scale.exp`	whether scale the expression matrix, defalut is TRUE.
`sample.order`	the expression matrix sample orders, defalut is NULL.
`exp.col`	the expression colors, defalut is c('blue','white','red').
`ht.legend`	whether show the heatmap legend, defalut is TRUE.
`ght.relHight`	the relative height to the main plot, defalut is 0.4.
`ght.geneText.size`	the gene label text size, defalut is 6.
`ght.sampleText.size`	the sample label text size, defalut is 6.
`ght.facet`	whether facet expression heatmap, defalut is FALSE.
`ght.facet.scale`	the facet plot scale argumrnt, defalut is "free".
`termID.order`	the facet term ID orders, defalut is NULL.
`rank.gene`	add your gene label on rank plot, defalut is NULL.
`rank.gene.nudgey`	the gene label nudge y on rank plot, defalut is 2.
`rm.newGsea.ticks`	whether remove right axis when you plot multiple terms with newGsea plot, defalut is TRUE.
`pFill`	the pvalue table fill color when you plot multiple terms with newGsea plot, defalut is transparent.
`base_size`	the plot theme font size, defalut is 12.
`ncol`	the columns for newGSEA plot with multiple terms, defalut is 1
`rm_ht`	whether remove classic middle heatmap plot, defalut is FALSE.

Value

ggplot2 object

Author(s)

Jun Zhang

Examples

# load data
test_data <- system.file("extdata", "gseaRes.RDS", package = "GseaVis")
gseaRes <- readRDS(test_data)

# all plot
gseaNb(object = gseaRes,
      geneSetID = 'GOBP_NUCLEOSIDE_DIPHOSPHATE_METABOLIC_PROCESS',
      subPlot = 2)
# load data
test_data <- system.file("extdata", "gseaRes.RDS", package = "GseaVis")
gseaRes <- readRDS(test_data)

# all plot
gseaNb(object = gseaRes,
      geneSetID = 'GOBP_NUCLEOSIDE_DIPHOSPHATE_METABOLIC_PROCESS',
      subPlot = 2)

gsInfo

Description

gsInfo

Usage

gsInfo(object, geneSetID)
gsInfo(object, geneSetID)

Arguments

`object`	gseaResult object
`geneSetID`	gene set ID

Value

data.frame

Author(s)

Guangchuang Yu

gsInfoNew

Description

gsInfoNew

Usage

gsInfoNew(geneList = NULL, geneSetID = NULL, geneSet = NULL, exponent = 1)
gsInfoNew(geneList = NULL, geneSetID = NULL, geneSet = NULL, exponent = 1)

Arguments

`geneList`	geneList for GSEA software outputs which is saved in "gseaOutputs/ranked_gene_list_treat_versus_control*.tsv".
`geneSetID`	gene set ID.
`geneSet`	enrichment term sets for GSEA software outputs which is saved in "*/edb/gene_sets".
`exponent`	weight of each step, defalut is 1.

Value

a data.frame

Author(s)

Guangchuang Yu, modified by JunZhang

This is a test data for this package test data describtion

Description

This is a test data for this package test data describtion

Usage

intergrated
intergrated

Format

An object of class list of length 3.

Author(s)

JunZhang

readGseaFile

Description

readGseaFile

Arguments

filePath

the path of the GSEA software enrichment outputs, defalut is NULL.

Value

a list contains meta(intergated enrichment results), glist(the ordered gene lists), gset(all background enrichment terms).

sankeyGoPlot

Description

sankeyGoPlot

Usage

sankeyGoPlot(
  goData = NULL,
  topGenes = 5,
  keep_all_gene = FALSE,
  sankeyExpand = c(0.5, 1),
  flow_fill = "grey",
  nodeSize = 2.5,
  nodeColor = NULL,
  goCol = NULL,
  xShift = 0.05,
  downShift = 4.5,
  upShift = 0.25,
  geom_layer = NULL,
  enrich_type = c("go", "gsea")
)
sankeyGoPlot(
  goData = NULL,
  topGenes = 5,
  keep_all_gene = FALSE,
  sankeyExpand = c(0.5, 1),
  flow_fill = "grey",
  nodeSize = 2.5,
  nodeColor = NULL,
  goCol = NULL,
  xShift = 0.05,
  downShift = 4.5,
  upShift = 0.25,
  geom_layer = NULL,
  enrich_type = c("go", "gsea")
)

Arguments

`goData`	GO data frame from Clusterprofiler.
`topGenes`	the top genes to be shown for each term, default 5.
`keep_all_gene`	Whether keep all genes, default "FALSE", `topGenes` will be useless if TRUE.
`sankeyExpand`	the sankey plot expand for left and side, default c(0.5,1).
`flow_fill`	The flow fill color, default `grey`.
`nodeSize`	node text size for sankey plot, default 2.5.
`nodeColor`	node fill color for sankey plot, default NULL.
`goCol`	go color for go plot, default NULL.
`xShift`	the shift on horizontal for go plot, default 0.05.
`downShift`	the shift on vertical for go plot, default 4.5.
`upShift`	the shift on vertical for go plot, default 0.25.
`geom_layer`	Custom geom_layers, default NULL.
`enrich_type`	The enrichment type, "go" or "gsea".

Value

ggplot obeject

volcanoGsea

Description

volcanoGsea

Usage

volcanoGsea(
  data = NULL,
  NES.cutoff = 1,
  pvalue.cutoff = NULL,
  p.adjust.CUTOFF = 0.05,
  nudge.y = c(0, 0),
  topN = 5,
  point.size = 3,
  point.color = c("#CC3333", "#CCCCCC", "#0099CC"),
  ...
)
volcanoGsea(
  data = NULL,
  NES.cutoff = 1,
  pvalue.cutoff = NULL,
  p.adjust.CUTOFF = 0.05,
  nudge.y = c(0, 0),
  topN = 5,
  point.size = 3,
  point.color = c("#CC3333", "#CCCCCC", "#0099CC"),
  ...
)

Arguments

`data`	GSEA enrich object from clusterProfiler, defalut is NULL.
`NES.cutoff`	NES cutoff to select significant terms, defalut is 1.
`pvalue.cutoff`	pvalue cutoff to select significant terms, defalut is NULL.
`p.adjust.CUTOFF`	adjusted pvalue cutoff to select significant terms, defalut is 0.05.
`nudge.y`	y shift to ajust label, defalut is c(0,0).
`topN`	top term to show, defalut is 5.
`point.size`	point size, defalut is 3.
`point.color`	point color, defalut is c('#CC3333','#CCCCCC','#0099CC').
`...`	other arguments passed by geom_text_repel.

Value

a ggplot object.

Author(s)

Jun Zhang

Package 'GseaVis'

Help Index

Circular Gene Set Enrichment Analysis Plot

Description

Usage

Arguments

Value

Create gseaResult Object from Enrichment Analysis Output Dataframe

Description

Usage

Arguments

Value

Create gseaResult Object from KEGG Enrichment Analysis Output Dataframe

Description

Usage

Arguments

Value

dotplotGsea

Description

Arguments

Value

Author(s)

Generate Enrichment Ridge Plot

Description

Usage

Arguments

Details

Value

Perform GSEA Multi-group Plotting

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

gseaNb

Description

Arguments

Value

Author(s)

Examples

gsInfo

Description

Usage

Arguments

Value

Author(s)

gsInfoNew

Description

Usage

Arguments

Value

Author(s)

This is a test data for this package test data describtion

Description

Usage

Format

Author(s)

readGseaFile

Description

Arguments

Value

sankeyGoPlot

Description

Usage

Arguments

Value

volcanoGsea

Description

Usage

Arguments

Value

Author(s)