Package 'genekitr' reference manual

Title:	Gene Analysis Toolkit
Description:	Provides features for searching, converting, analyzing, plotting, and exporting data effortlessly by inputting feature IDs. Enables easy retrieval of feature information, conversion of ID types, gene enrichment analysis, publication-level figures, group interaction plotting, and result export in one Excel file for seamless sharing and communication.
Authors:	Yunze Liu [aut, cre]
Maintainer:	Yunze Liu <[email protected]>
License:	GPL-3
Version:	1.2.8
Built:	2024-11-09 02:53:52 UTC
Source:	https://github.com/ganglilab/genekitr

Modify dataframe for enrichment plot

Description

To make sure colname contains Description, Count, FoldEnrich/GeneRatio, pvalue/qvalue/p.adjust

Usage

as.enrichdat(enrich_df)
as.enrichdat(enrich_df)

Arguments

enrich_df

Enrichment analysis 'data.frame' result.

Value

'data.frame'

Datasets geneList entrez gene list with decreasing fold change value

Description

Datasets geneList entrez gene list with decreasing fold change value

Datasets Differential expression analysis result of GSE42872

Datasets msig_species contains msigdb species information

Datasets msig_category contains msigdb category information

Datasets biocOrg_name contains organism name of bioconductor

Datasets keggOrg_name contains organism name of KEGG https://www.genome.jp/kegg/catalog/org_list.html

Datasets ensOrg_name contains organism name of ensembl

Datasets hsapiens_probe_platform contains human probe platforms

Export list of data sets into different 'Excel' sheets

Description

Export list of data sets into different 'Excel' sheets

Usage

expoSheet(
  data_list,
  data_name,
  filename = NULL,
  dir = tempdir(),
  overwrite = TRUE
)
expoSheet(
  data_list,
  data_name,
  filename = NULL,
  dir = tempdir(),
  overwrite = TRUE
)

Arguments

`data_list`	List of datasets.
`data_name`	Character of data names.
`filename`	A character string naming an xlsx file.
`dir`	A character string naming output directory.
`overwrite`	If TRUE, overwrite any existing file.

Value

An Excel file.

Examples

library(openxlsx)
expoSheet(
  data_list = list(mtcars, ToothGrowth),
  data_name = c("mtcars", "tooth"),
  filename = "test.xlsx", dir = tempfile()
)
library(openxlsx)
expoSheet(
  data_list = list(mtcars, ToothGrowth),
  data_name = c("mtcars", "tooth"),
  filename = "test.xlsx", dir = tempfile()
)

Gene Set Enrichment Analysis

Description

Gene Set Enrichment Analysis

Usage

genGSEA(
  genelist,
  geneset,
  padj_method = "BH",
  p_cutoff = 0.05,
  q_cutoff = 0.05,
  min_gset_size = 10,
  max_gset_size = 500,
  set_seed = FALSE
)
genGSEA(
  genelist,
  geneset,
  padj_method = "BH",
  p_cutoff = 0.05,
  q_cutoff = 0.05,
  min_gset_size = 10,
  max_gset_size = 500,
  set_seed = FALSE
)

Arguments

`genelist`	Pre-ranked genelist with decreasing order, gene can be entrez, ensembl or symbol.
`geneset`	Gene set is a two-column data.frame with term id and gene id. Please use package 'geneset' to select available gene set or make new one.
`padj_method`	One of "BH", "BY", "bonferroni","fdr","hochberg", "holm", "hommel", "none"
`p_cutoff`	Numeric of cutoff for both unadjusted and adjusted pvalue, default is 0.05.
`q_cutoff`	Numeric of cutoff for qvalue, default is 0.05.
`min_gset_size`	Numeric of minimal size of each geneset for analyzing, default is 10.
`max_gset_size`	Numeric of maximal size of each geneset for analyzing, default is 500.
`set_seed`	GSEA permutations are performed using random reordering, which causes slightly difference results after every time running. If user want to get same result every time for same input, please set 'set_seed = TRUE' or 'set.seed()' prior to running.

Value

A 'data.frame'.

Examples


if(requireNamespace("geneset",quietly = TRUE)){
# only gene ids
data(geneList, package = "genekitr")
gs <- geneset::getGO(org = "human",ont = "mf",data_dir = tempdir())
gse <- genGSEA(genelist = geneList, geneset = gs)
  }

if(requireNamespace("geneset",quietly = TRUE)){
# only gene ids
data(geneList, package = "genekitr")
gs <- geneset::getGO(org = "human",ont = "mf",data_dir = tempdir())
gse <- genGSEA(genelist = geneList, geneset = gs)
  }

Get gene related information

Description

Get gene related information

Usage

genInfo(
  id = NULL,
  org = "hs",
  unique = FALSE,
  keepNA = TRUE,
  hgVersion = c("v38", "v19")
)
genInfo(
  id = NULL,
  org = "hs",
  unique = FALSE,
  keepNA = TRUE,
  hgVersion = c("v38", "v19")
)

Arguments

`id`	Gene id (symbol, ensembl or entrez id) or uniprot id. If this argument is NULL, return all gene info.
`org`	Latin organism shortname from 'ensOrg_name'. Default is human.
`unique`	Logical, if one-to-many mapping occurs, only keep one record with fewest NA. Default is FALSE.
`keepNA`	If some id has no match at all, keep it or not. Default is TRUE.
`hgVersion`	Select human genome build version from "v38" (default) and "v19".

Value

A 'data.frame'.

Examples


# example1: input list with fake id and one-to-many mapping id
x <- genInfo(id = c(
  "MCM10", "CDC20", "S100A9", "MMP1", "BCC7",
  "FAKEID", "TP53", "HBD", "NUDT10"
))

# example2: statistics of human gene biotypes
genInfo(org = "hs") %>%
  {
    table(.$gene_biotype)
  }

# example3: use hg19 data
x <- genInfo(id = c("TP53","BCC7"), hgVersion = "v19")

# example4: search genes with case-insensitive
x <- genInfo(id = c("tp53","nc886","FAke","EZh2"), org = "hs", unique = TRUE)


# example1: input list with fake id and one-to-many mapping id
x <- genInfo(id = c(
  "MCM10", "CDC20", "S100A9", "MMP1", "BCC7",
  "FAKEID", "TP53", "HBD", "NUDT10"
))

# example2: statistics of human gene biotypes
genInfo(org = "hs") %>%
  {
    table(.$gene_biotype)
  }

# example3: use hg19 data
x <- genInfo(id = c("TP53","BCC7"), hgVersion = "v19")

# example4: search genes with case-insensitive
x <- genInfo(id = c("tp53","nc886","FAke","EZh2"), org = "hs", unique = TRUE)

Gene Over-Representation Enrichment Analysis

Description

Gene Over-Representation Enrichment Analysis

Usage

genORA(
  id,
  geneset,
  group_list = NULL,
  padj_method = "BH",
  p_cutoff = 0.05,
  q_cutoff = 0.15,
  min_gset_size = 10,
  max_gset_size = 500,
  universe
)
genORA(
  id,
  geneset,
  group_list = NULL,
  padj_method = "BH",
  p_cutoff = 0.05,
  q_cutoff = 0.15,
  min_gset_size = 10,
  max_gset_size = 500,
  universe
)

Arguments

`id`	A vector of gene id which can be entrezid, ensembl, symbol or uniprot.
`geneset`	Gene set is a two-column data.frame with term id and gene id. Please use package 'geneset' to select available gene set or make new one.
`group_list`	A list of gene group information, default is NULL.
`padj_method`	One of "BH", "BY", "bonferroni","fdr","hochberg", "holm", "hommel", "none"
`p_cutoff`	Numeric of cutoff for both unadjusted and adjusted pvalue, default is 0.05.
`q_cutoff`	Numeric of cutoff for qvalue, default is 0.15.
`min_gset_size`	Numeric of minimal size of each geneset for analyzing, default is 10.
`max_gset_size`	Numeric of maximal size of each geneset for analyzing, default is 500.
`universe`	Character of background genes. If missing, all genes in geneset will be used as background.

Value

A 'data.frame'.

Examples


# only gene ids
data(geneList, package = "genekitr")
id <- names(geneList)[abs(geneList) > 1]
gs <- geneset::getGO(org = "human",ont = "mf",data_dir = tempdir())
ora <- genORA(id, geneset = gs)

# gene id with groups
id <- c(head(names(geneList), 50), tail(names(geneList), 50))
group <- list(
  group1 = c(rep("up", 50), rep("down", 50)),
  group2 = c(rep("A", 20), rep("B", 30))
)
gora <- genORA(id, geneset = gs, group_list = group)


# only gene ids
data(geneList, package = "genekitr")
id <- names(geneList)[abs(geneList) > 1]
gs <- geneset::getGO(org = "human",ont = "mf",data_dir = tempdir())
ora <- genORA(id, geneset = gs)

# gene id with groups
id <- c(head(names(geneList), 50), tail(names(geneList), 50))
group <- list(
  group1 = c(rep("up", 50), rep("down", 50)),
  group2 = c(rep("A", 20), rep("B", 30))
)
gora <- genORA(id, geneset = gs, group_list = group)

Get 'PubMed' paper records by searching abstract

Description

PubMed<https://pubmed.ncbi.nlm.nih.gov/> is a free search engine accessing primarily the database of references and abstracts on life ciences and biomedical topics.

Usage

getPubmed(term, add_term = NULL, num = 100)
getPubmed(term, add_term = NULL, num = 100)

Arguments

`term`	query terms e.g. gene id, GO/KEGG pathway
`add_term`	other searching terms Default is NULL
`num`	limit the number of records . Default is 100.

Value

A list of 'tibble' for pubmed records

Examples


term <- c("Tp53", "Brca1", "Tet2")
add_term <- c("stem cell", "mouse")
l <- getPubmed(term, add_term, num = 30)
# very easy to output
expoSheet(l, data_name = term, filename = "test.xlsx", dir = tempfile())

term <- c("Tp53", "Brca1", "Tet2")
add_term <- c("stem cell", "mouse")
l <- getPubmed(term, add_term, num = 30)
# very easy to output
expoSheet(l, data_name = term, filename = "test.xlsx", dir = tempfile())

Import 'clusterProfiler' result

Description

Import 'clusterProfiler' result

Usage

importCP(object, type = c("go", "gsea", "other"))
importCP(object, type = c("go", "gsea", "other"))

Arguments

`object`	clusterProfiler object.
`type`	object type from "go", "gsea" and "other". "other" includes ORA (over-representation analysis) of KEGG, DOSE,...

Value

'data.frame'

Import 'Panther' web result

Description

Import 'Panther' web result

Usage

importPanther(panther_file)
importPanther(panther_file)

Arguments

panther_file

Panther result file.

Value

'data.frame'

Import 'shinyGO' web result

Description

Import 'shinyGO' web result

Usage

importShinygo(shinygo_file)
importShinygo(shinygo_file)

Arguments

shinygo_file

ShinyGO result file.

Value

'data.frame'

Themes for all plots

Description

Change ggplot text, font, legend and border

Usage

plot_theme(
  main_text_size = 8,
  legend_text_size = 6,
  font_type = "sans",
  border_thick = 1.5,
  remove_grid = TRUE,
  remove_border = FALSE,
  remove_main_text = FALSE,
  remove_legend_text = FALSE,
  remove_legend = FALSE
)
plot_theme(
  main_text_size = 8,
  legend_text_size = 6,
  font_type = "sans",
  border_thick = 1.5,
  remove_grid = TRUE,
  remove_border = FALSE,
  remove_main_text = FALSE,
  remove_legend_text = FALSE,
  remove_legend = FALSE
)

Arguments

`main_text_size`	Numeric, main text size
`legend_text_size`	Numeric, legend text size
`font_type`	Character, specify the plot text font family, default is "sans".
`border_thick`	Numeric, border thickness, default is 1. If set 0, remove both border and ticks.
`remove_grid`	Logical, remove background grid lines, default is FALSE.
`remove_border`	Logical, remove border line, default is FALSE.
`remove_main_text`	Logical, remove all axis text, default is FALSE.
`remove_legend_text`	Logical, remove all legend text, default is FALSE.
`remove_legend`	Logical, remove entire legend, default is FALSE.

Value

ggplot theme

Examples

library(ggplot2)
ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  plot_theme(font_type = "Times", border_thick = 2)
library(ggplot2)
ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  plot_theme(font_type = "Times", border_thick = 2)

Plot for gene enrichment analysis of ORA method

Description

Over-representation analysis (ORA) is a simple method for objectively deciding whether a set of variables of known or suspected biological relevance, such as a gene set or pathway, is more prevalent in a set of variables of interest than we expect by chance.

Usage

plotEnrich(
  enrich_df,
  fold_change = NULL,
  plot_type = c("bar", "wego", "dot", "bubble", "lollipop", "geneheat", "genechord",
    "network", "gomap", "goheat", "gotangram", "wordcloud", "upset"),
  term_metric = c("FoldEnrich", "GeneRatio", "Count", "RichFactor"),
  stats_metric = c("p.adjust", "pvalue", "qvalue"),
  sim_method = c("Resnik", "Lin", "Rel", "Jiang", "Wang", "JC"),
  up_color = "#E31A1C",
  down_color = "#1F78B4",
  show_gene = "all",
  xlim_left = 0,
  xlim_right = NA,
  wrap_length = NULL,
  org = NULL,
  ont = NULL,
  scale_ratio,
  layout,
  n_term,
  ...
)
plotEnrich(
  enrich_df,
  fold_change = NULL,
  plot_type = c("bar", "wego", "dot", "bubble", "lollipop", "geneheat", "genechord",
    "network", "gomap", "goheat", "gotangram", "wordcloud", "upset"),
  term_metric = c("FoldEnrich", "GeneRatio", "Count", "RichFactor"),
  stats_metric = c("p.adjust", "pvalue", "qvalue"),
  sim_method = c("Resnik", "Lin", "Rel", "Jiang", "Wang", "JC"),
  up_color = "#E31A1C",
  down_color = "#1F78B4",
  show_gene = "all",
  xlim_left = 0,
  xlim_right = NA,
  wrap_length = NULL,
  org = NULL,
  ont = NULL,
  scale_ratio,
  layout,
  n_term,
  ...
)

Arguments

`enrich_df`	Enrichment analysis 'data.frame' result.
`fold_change`	Fold change or logFC values with gene IDs as names. Used in "heat" and "chord" plot.
`plot_type`	Choose from "bar", "wego","bubble","dot", "lollipop","geneheat", "genechord", "network","gomap","goheat","gotangram","wordcloud","upset".
`term_metric`	Pathway term metric from one of 'GeneRatio','Count','FoldEnrich' and 'RichFactor'.
`stats_metric`	Statistic metric from one of "pvalue", "p.adjust", "qvalue".
`sim_method`	Method of calculating the similarity between nodes, one of one of "Resnik", "Lin", "Rel", "Jiang" , "Wang" or "JC" (Jaccard’s similarity index). Only "JC" supports KEGG data. Used in "map","goheat","gotangram","wordcloud".
`up_color`	Color of higher statistical power (e.g. Pvalue 0.01) or higher logFC, default is "red".
`down_color`	Color of lower statistical power (e.g. Pvalue 1) or lower logFC, default is "blue".
`show_gene`	Select genes to show. Default is "all". Used in "heat" and "chord" plot.
`xlim_left`	X-axis left limit, default is 0.
`xlim_right`	X-axis right limit, default is NA.
`wrap_length`	Numeric, wrap text if longer than this length. Default is NULL.
`org`	Organism name from 'biocOrg_name'.
`ont`	One of "BP", "MF", and "CC".
`scale_ratio`	Numeric, scale of node and line size.
`layout`	Grapgh layout in "map" plot, e,g, "circle", "dh", "drl", "fr","graphopt", "grid", "lgl", "kk", "mds", "nicely" (default),"randomly", "star".
`n_term`	Number of terms (used in WEGO plot)
`...`	other arguments from 'plot_theme' function

Value

A ggplot object

Examples


## example data
## More examples please refer to https://www.genekitr.fun/plot-ora-1.html
library(ggplot2)
data(geneList, package = "genekitr")
id <- names(geneList)[abs(geneList) > 1.5]
logfc <- geneList[id]

gs <- geneset::getGO(org = "human",ont = "bp",data_dir = tempdir())
ego <- genORA(id, geneset = gs)
ego <- ego[1:10, ]

## example plots
plotEnrich(ego, plot_type = "dot")

#plotEnrich(ego, plot_type = "bubble", scale_ratio = 0.4)

#plotEnrich(ego, plot_type = "bar")



## example data
## More examples please refer to https://www.genekitr.fun/plot-ora-1.html
library(ggplot2)
data(geneList, package = "genekitr")
id <- names(geneList)[abs(geneList) > 1.5]
logfc <- geneList[id]

gs <- geneset::getGO(org = "human",ont = "bp",data_dir = tempdir())
ego <- genORA(id, geneset = gs)
ego <- ego[1:10, ]

## example plots
plotEnrich(ego, plot_type = "dot")

#plotEnrich(ego, plot_type = "bubble", scale_ratio = 0.4)

#plotEnrich(ego, plot_type = "bar")

Advanced Plot for gene enrichment analysis of ORA method

Description

Usage

plotEnrichAdv(
  up_enrich_df,
  down_enrich_df,
  plot_type = c("one", "two"),
  term_metric = c("FoldEnrich", "GeneRatio", "Count", "RichFactor"),
  stats_metric = c("p.adjust", "pvalue", "qvalue"),
  wrap_length = NULL,
  xlim_left = NULL,
  xlim_right = NULL,
  color,
  ...
)
plotEnrichAdv(
  up_enrich_df,
  down_enrich_df,
  plot_type = c("one", "two"),
  term_metric = c("FoldEnrich", "GeneRatio", "Count", "RichFactor"),
  stats_metric = c("p.adjust", "pvalue", "qvalue"),
  wrap_length = NULL,
  xlim_left = NULL,
  xlim_right = NULL,
  color,
  ...
)

Arguments

`up_enrich_df`	Enrichment analysis 'data.frame' for up-regulated genes.
`down_enrich_df`	Enrichment analysis 'data.frame' for down-regulated genes.
`plot_type`	Choose from "one" and "two". "One" represents both up and down pathways are plotted together; "two" represents up and down are plotted seperately.
`term_metric`	Pathway term metric from one of 'GeneRatio','Count','FoldEnrich' and 'RichFactor'.
`stats_metric`	Statistic metric from one of "pvalue", "p.adjust", "qvalue".
`wrap_length`	Numeric, wrap text if longer than this length. Default is NULL.
`xlim_left`	X-axis left limit
`xlim_right`	X-axis right limit
`color`	Plot colors.
`...`	other arguments from 'plot_theme' function

Details

Both up and down regulated pathways could be plotted in one figure as two-side barplot

Value

A ggplot object

Plot for gene enrichment analysis of GSEA method

Description

Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes).

Usage

plotGSEA(
  gsea_list,
  plot_type = c("volcano", "classic", "fgsea", "ridge", "bar"),
  stats_metric = c("p.adjust", "pvalue", "qvalue"),
  show_pathway = NULL,
  show_gene = NULL,
  colour = NULL,
  wrap_length = NULL,
  label_by = c("id", "description"),
  ...
)
plotGSEA(
  gsea_list,
  plot_type = c("volcano", "classic", "fgsea", "ridge", "bar"),
  stats_metric = c("p.adjust", "pvalue", "qvalue"),
  show_pathway = NULL,
  show_gene = NULL,
  colour = NULL,
  wrap_length = NULL,
  label_by = c("id", "description"),
  ...
)

Arguments

`gsea_list`	GSEA result from 'genGSEA' function
`plot_type`	GSEA plot type, one of 'volcano', 'classic', 'fgsea', 'ridge' or 'bar'.
`stats_metric`	Statistic metric from one of "pvalue", "p.adjust", "qvalue".
`show_pathway`	Select plotting pathways by number (will choose top N pathways) or pathway name (choose from ID column).
`show_gene`	Select genes to show. Default is "all". Used in "classic" plot.
`colour`	Colour vector. Deafault is NULL. Used in volcano, ridge and bar plot.
`wrap_length`	Numeric, wrap text if longer than this length. Default is NULL.
`label_by`	Select which column as the label. If user wants to modify labels in plot, please modify the "Description" column and set the argument as "description". Default is by 'id'.
`...`	other arguments transfer to 'plot_theme' function

Value

A ggplot object

Examples


k1 = requireNamespace("cowplot",quietly = TRUE)
k2 = requireNamespace("fgsea",quietly = TRUE)
k3 = requireNamespace("ggplotify",quietly = TRUE)
k4 = requireNamespace("ggridges",quietly = TRUE)
if(k1&k2&k3&k4){
library(ggplot2)
## get GSEA result
data(geneList, package = "genekitr")
gs <- geneset::getMsigdb(org = "human",category = "H")
gse <- genGSEA(genelist = geneList, geneset = gs)

## volcano plot
# get top3 of up and down pathways
plotGSEA(gse, plot_type = "volcano", show_pathway = 3)
# choose pathway by character
pathways <- c('HALLMARK_KRAS_SIGNALING_UP','HALLMARK_P53_PATHWAY','HALLMARK_GLYCOLYSIS')
plotGSEA(gse, plot_type = "volcano", show_pathway = pathways)

## classic pathway plot
genes <- c('ENG','TP53','MET')
plotGSEA(gse, plot_type = "classic", show_pathway = pathways, show_gene = genes)

## fgsea table plot
plotGSEA(gse, plot_type = "fgsea", show_pathway = 3)

## ridgeplot
plotGSEA(gse,
  plot_type = "ridge",
  show_pathway = 10, stats_metric = "p.adjust"
)

## two-side barplot
plotGSEA(gse,
  plot_type = "bar", main_text_size = 8,
  colour = c("navyblue", "orange")
)
}


k1 = requireNamespace("cowplot",quietly = TRUE)
k2 = requireNamespace("fgsea",quietly = TRUE)
k3 = requireNamespace("ggplotify",quietly = TRUE)
k4 = requireNamespace("ggridges",quietly = TRUE)
if(k1&k2&k3&k4){
library(ggplot2)
## get GSEA result
data(geneList, package = "genekitr")
gs <- geneset::getMsigdb(org = "human",category = "H")
gse <- genGSEA(genelist = geneList, geneset = gs)

## volcano plot
# get top3 of up and down pathways
plotGSEA(gse, plot_type = "volcano", show_pathway = 3)
# choose pathway by character
pathways <- c('HALLMARK_KRAS_SIGNALING_UP','HALLMARK_P53_PATHWAY','HALLMARK_GLYCOLYSIS')
plotGSEA(gse, plot_type = "volcano", show_pathway = pathways)

## classic pathway plot
genes <- c('ENG','TP53','MET')
plotGSEA(gse, plot_type = "classic", show_pathway = pathways, show_gene = genes)

## fgsea table plot
plotGSEA(gse, plot_type = "fgsea", show_pathway = 3)

## ridgeplot
plotGSEA(gse,
  plot_type = "ridge",
  show_pathway = 10, stats_metric = "p.adjust"
)

## two-side barplot
plotGSEA(gse,
  plot_type = "bar", main_text_size = 8,
  colour = c("navyblue", "orange")
)
}

Venn plot for groups of genes

Description

If gene group over 4, plot will be visulized using UpSet plot.

Usage

plotVenn(
  venn_list,
  use_venn = TRUE,
  color = NULL,
  alpha_degree = 0.3,
  venn_percent = FALSE,
  ...
)
plotVenn(
  venn_list,
  use_venn = TRUE,
  color = NULL,
  alpha_degree = 0.3,
  venn_percent = FALSE,
  ...
)

Arguments

`venn_list`	A list of gene id.
`use_venn`	Logical, use venn to plot, default is 'TRUE', the other option is upsetplot for large list.
`color`	Colors for gene lists, default is NULL.
`alpha_degree`	Alpha transparency of each circle's area, default is 0.3.
`venn_percent`	Logical to show both number and percentage in venn plot.
`...`	other arguments transfer to 'plot_theme' function

Value

A ggplot object

Examples

k1 = requireNamespace("ComplexUpset",quietly = TRUE)
k2 = requireNamespace("futile.logger",quietly = TRUE)
k3 = requireNamespace("ggsci",quietly = TRUE)
k4 = requireNamespace("RColorBrewer",quietly = TRUE)
if(k1&k2&k3&k4){
library(ggplot2)
set1 <- paste0(rep("gene", 30), sample(1:1000, 30))
set2 <- paste0(rep("gene", 40), sample(1:1000, 40))
set3 <- paste0(rep("gene", 50), sample(1:1000, 50))
set4 <- paste0(rep("gene", 60), sample(1:1000, 60))
set5 <- paste0(rep("gene", 70), sample(1:1000, 70))
sm_gene_list <- list(gset1 = set1, gset2 = set2, gset3 = set3)
la_gene_list <- list(
  gset1 = set1, gset2 = set2, gset3 = set3,
  gset4 = set4, gset5 = set5
)
plotVenn(sm_gene_list,
  use_venn = TRUE,
  alpha_degree = 0.5,
  main_text_size = 3,
  border_thick = 0,
  venn_percent = TRUE
)
plotVenn(la_gene_list,
  use_venn = FALSE,
  main_text_size = 15,
  legend_text_size = 8,
  legend_position = 'left'
)
}

k1 = requireNamespace("ComplexUpset",quietly = TRUE)
k2 = requireNamespace("futile.logger",quietly = TRUE)
k3 = requireNamespace("ggsci",quietly = TRUE)
k4 = requireNamespace("RColorBrewer",quietly = TRUE)
if(k1&k2&k3&k4){
library(ggplot2)
set1 <- paste0(rep("gene", 30), sample(1:1000, 30))
set2 <- paste0(rep("gene", 40), sample(1:1000, 40))
set3 <- paste0(rep("gene", 50), sample(1:1000, 50))
set4 <- paste0(rep("gene", 60), sample(1:1000, 60))
set5 <- paste0(rep("gene", 70), sample(1:1000, 70))
sm_gene_list <- list(gset1 = set1, gset2 = set2, gset3 = set3)
la_gene_list <- list(
  gset1 = set1, gset2 = set2, gset3 = set3,
  gset4 = set4, gset5 = set5
)
plotVenn(sm_gene_list,
  use_venn = TRUE,
  alpha_degree = 0.5,
  main_text_size = 3,
  border_thick = 0,
  venn_percent = TRUE
)
plotVenn(la_gene_list,
  use_venn = FALSE,
  main_text_size = 15,
  legend_text_size = 8,
  legend_position = 'left'
)
}

Volcano plot for differential expression analysis

Description

Volcano plot for differential expression analysis

Usage

plotVolcano(
  deg_df,
  stat_metric = c("p.adjust", "pvalue"),
  stat_cutoff = 0.05,
  logFC_cutoff = 1,
  up_color = "#E31A1C",
  down_color = "#1F78B4",
  show_gene = NULL,
  dot_size = 1.75,
  ...
)
plotVolcano(
  deg_df,
  stat_metric = c("p.adjust", "pvalue"),
  stat_cutoff = 0.05,
  logFC_cutoff = 1,
  up_color = "#E31A1C",
  down_color = "#1F78B4",
  show_gene = NULL,
  dot_size = 1.75,
  ...
)

Arguments

`deg_df`	DEG dataframe with gene id, logFC and stat(e.g. pvalue/qvalue).
`stat_metric`	Statistic metric from "pvalue" or "p.adjust".
`stat_cutoff`	Statistic cutoff, default is 0.05.
`logFC_cutoff`	Log2 fold change cutoff, default is 1 which is actually 2 fold change.
`up_color`	Color of up-regulated genes, default is "dark red".
`down_color`	Color of down-regulated genes, default is "dark blue".
`show_gene`	Select genes to show, default is no genes to show.
`dot_size`	Volcano dot size, default is 1.75.
`...`	other arguments from 'plot_theme' function

Value

A ggplot object

Examples


if(requireNamespace("ggrepel",quietly = T)){
library(ggplot2)
data(deg, package = "genekitr")
plotVolcano(deg, "p.adjust", remove_legend = TRUE, dot_size = 3)

# show some genes
plotVolcano(deg, "p.adjust",
  remove_legend = TRUE,
  show_gene = c("CD36", "DUSP6", "IER3","CDH7")
)
}


if(requireNamespace("ggrepel",quietly = T)){
library(ggplot2)
data(deg, package = "genekitr")
plotVolcano(deg, "p.adjust", remove_legend = TRUE, dot_size = 3)

# show some genes
plotVolcano(deg, "p.adjust",
  remove_legend = TRUE,
  show_gene = c("CD36", "DUSP6", "IER3","CDH7")
)
}

Simplify GO enrichment result

Description

The Gene Ontology (GO) is a major bioinformatics initiative to unify the representation of gene and gene product attributes across all species.

Usage

simGO(
  enrich_df,
  sim_method = c("Resnik", "Lin", "Rel", "Jiang", "Wang"),
  org = NULL,
  ont = NULL
)
simGO(
  enrich_df,
  sim_method = c("Resnik", "Lin", "Rel", "Jiang", "Wang"),
  org = NULL,
  ont = NULL
)

Arguments

`enrich_df`	GO enrichment analysis of 'genORA()' result.
`sim_method`	Method of calculating the similarity between nodes, one of one of "Resnik", "Lin", "Rel", "Jiang" , "Wang" methods.
`org`	Organism name from 'biocOrg_name'.
`ont`	One of "bp", "mf", and "cc".

Value

A 'data.frame' contains simplified GO terms.

Transform id among symbol, entrezid, ensembl and uniprot.

Description

Transform id among symbol, entrezid, ensembl and uniprot.

Usage

transId(
  id,
  transTo,
  org = "hs",
  unique = FALSE,
  keepNA = FALSE,
  hgVersion = c("v38", "v19")
)
transId(
  id,
  transTo,
  org = "hs",
  unique = FALSE,
  keepNA = FALSE,
  hgVersion = c("v38", "v19")
)

Arguments

`id`	Gene ids or protein ids.
`transTo`	Transform to what type. User could select one or more from "symbol", "entrez", "ensembl" or "uniprot."
`org`	Latin organism shortname from 'ensOrg_name'. Default is human.
`unique`	Logical, if one-to-many mapping occurs, only keep one record with fewest NA. Default is FALSE.
`keepNA`	If some id has no match at all, keep it or not. Default is FALSE.
`hgVersion`	Select human genome build version from "v38" (default) and "v19".

Value

data frame, first column is input id and others are converted id.

Examples


# example1:
transId(
  id = c("Cyp2c23", "Fhit", "Gal3st2b", "Trp53", "Tp53"),
  transTo = "ensembl", org = "mouse", keepNA = FALSE
)

## example2: input id with one-to-many mapping and fake one
transId(
  id = c("MMD2", "HBD", "RNR1", "TEC", "BCC7", "FAKEID", "TP53"),
  transTo = c("entrez", "ensembl"), keepNA = TRUE
)

# example3: auto-recognize ensembl version number
transId("ENSG00000141510.11", "symbol")

# example4: search genes with case-insensitive
transId(c('nc886','ezh2','TP53'),transTo = "ensembl",org = 'hs',unique = TRUE)


# example1:
transId(
  id = c("Cyp2c23", "Fhit", "Gal3st2b", "Trp53", "Tp53"),
  transTo = "ensembl", org = "mouse", keepNA = FALSE
)

## example2: input id with one-to-many mapping and fake one
transId(
  id = c("MMD2", "HBD", "RNR1", "TEC", "BCC7", "FAKEID", "TP53"),
  transTo = c("entrez", "ensembl"), keepNA = TRUE
)

# example3: auto-recognize ensembl version number
transId("ENSG00000141510.11", "symbol")

# example4: search genes with case-insensitive
transId(c('nc886','ezh2','TP53'),transTo = "ensembl",org = 'hs',unique = TRUE)

Transform probe id to symbol, entrezid, ensembl or uniprot.

Description

Transform probe id to symbol, entrezid, ensembl or uniprot.

Usage

transProbe(id, transTo, org = "human", platform = NULL)
transProbe(id, transTo, org = "human", platform = NULL)

Arguments

`id`	probe ids.
`transTo`	Transform to what type. User could select one or more from "symbol", "entrez", "ensembl" or "uniprot."
`org`	'human'.
`platform`	Probe platform. If NULL, program will detect automatically.

Value

data frame, first column is probe id and others are converted id.

Package 'genekitr'

Help Index

Modify dataframe for enrichment plot

Description

Usage

Arguments

Value

Datasets geneList entrez gene list with decreasing fold change value

Description

Export list of data sets into different 'Excel' sheets

Description

Usage

Arguments

Value

Examples

Gene Set Enrichment Analysis

Description

Usage

Arguments

Value

Examples

Get gene related information

Description

Usage

Arguments

Value

Examples

Gene Over-Representation Enrichment Analysis

Description

Usage

Arguments

Value

Examples

Get 'PubMed' paper records by searching abstract

Description

Usage

Arguments

Value

Examples

Import 'clusterProfiler' result

Description

Usage

Arguments

Value

Import 'Panther' web result

Description

Usage

Arguments

Value

Import 'shinyGO' web result

Description

Usage

Arguments

Value

Themes for all plots

Description

Usage

Arguments

Value

Examples

Plot for gene enrichment analysis of ORA method

Description

Usage

Arguments

Value

Examples

Advanced Plot for gene enrichment analysis of ORA method

Description

Usage

Arguments

Details

Value

Plot for gene enrichment analysis of GSEA method

Description

Usage

Arguments

Value

Examples

Venn plot for groups of genes

Description