### Install tinyarray from Online Sources

Source: https://github.com/xjsun1221/tinyarray/blob/master/README.md

Use these commands to install the package from CRAN or GitHub.

```R
if(!require(tinyarray))install.packages("tinyarray")
if(!require(devtools))install.packages("devtools")
if(!require(tinyarray))devtools::install_github("xjsun1221/tinyarray",upgrade = FALSE,dependencies = TRUE)
```

--------------------------------

### Example usage of plcortest

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/plcortest.Rd

Placeholder example for the plcortest function.

```R
# to update
```

--------------------------------

### Generate Heatmaps with ggheat

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/ggheat.Rd

Examples demonstrating basic heatmap generation, clustering, and customization of labels and visibility.

```R
exp_dat = matrix(sample(100:1000,40),ncol = 4)
exp_dat[1:(nrow(exp_dat)/2),] =  exp_dat[1:(nrow(exp_dat)/2),]-1000
rownames(exp_dat) = paste0("sample",1:nrow(exp_dat))
colnames(exp_dat) = paste0("gene",1:ncol(exp_dat))
group = rep(c("A","B"),each = nrow(exp_dat)/2)
group = factor(group,levels = c("A","B"))
ggheat(exp_dat,group)
ggheat(exp_dat,group,cluster_rows = TRUE)
ggheat(exp_dat,group,cluster_rows = TRUE,show_rownames = FALSE,
       show_colnames = FALSE,groupname = "risk",expname = "expression")
```

--------------------------------

### find_anno(gpl, install = FALSE, update = FALSE)

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/find_anno.Rd

Finds the annotation package or files for a given GPL accession. Optionally installs and loads the package or updates it.

```APIDOC
## find_anno

### Description
Finds gpl annotation package or files based on the provided accession number.

### Usage
find_anno(gpl, install = FALSE, update = FALSE)

### Arguments
- **gpl** (string) - A GPL accession identifier.
- **install** (boolean) - Whether to install and library the package. Default is FALSE.
- **update** (boolean) - Whether to update the package. Default is FALSE.

### Returns
A list containing deg data.frame, a volcano plot, and a list of DEGs.

### Example
find_anno("GPL570")
```

--------------------------------

### Differential Expression Analysis Example

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/multi_deg_all.Rd

Example usage of multi_deg_all with GSE data, requiring the Biobase and AnnoProbe packages.

```R
if(requireNamespace("Biobase",quietly = TRUE)&
   requireNamespace("AnnoProbe",quietly = TRUE)){
gse = "GSE474"
geo = geo_download(gse,destdir=tempdir())
geo$exp[1:4,1:4]
geo$exp=log2(geo$exp+1)
group_list=ifelse(stringr::str_detect(geo$pd$title,"MObese"),"MObese",
ifelse(stringr::str_detect(geo$pd$title,"NonObese"),"NonObese","Obese"))
group_list=factor(group_list,levels = c("NonObese","Obese","MObese"))
find_anno(geo$gpl)
ids = AnnoProbe::idmap(geo$gpl,destdir = tempdir())
dcp = multi_deg_all(geo$exp,
group_list,ids,adjust = FALSE,entriz = FALSE)
dcp[[3]]
}else{
  if(!requireNamespace("AnnoProbe",quietly = TRUE)) {
    warning("Package 'AnnoProbe' needed for this function to work.
         Please install it by install.packages('AnnoProbe')",call. = FALSE)
  }
  if(!requireNamespace("Biobase",quietly = TRUE)) {
    warning("Package 'Biobase' needed for this function to work.
         Please install it by BiocManager::install('Biobase')",call. = FALSE)
  }
}
```

--------------------------------

### Perform enrichment analysis with quick_enrich

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/quick_enrich.Rd

Example usage of quick_enrich requiring the org.Hs.eg.db package for human data.

```R
if(requireNamespace("org.Hs.eg.db",quietly = TRUE)){
  head(genes)
  g = quick_enrich(genes,destdir = tempdir())
  names(g)
  g[[1]][1:4,1:4]
  g[[3]]
  g[[4]]
}else{
  warning("Package 'org.Hs.eg.db' needed for this function to work.
         Please install it by BiocManager::install('org.Hs.eg.db')",call. = FALSE)
  }
```

--------------------------------

### Calculate log-rank p-values with surv_KM

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/surv_KM.Rd

Examples demonstrating basic usage, p-value threshold adjustment, and enabling cut-point analysis.

```R
surv_KM(exprSet_hub1,meta1)
surv_KM(exprSet_hub1,meta1,pvalue_cutoff = 1)
surv_KM(exprSet_hub1,meta1,cut.point = TRUE)
```

--------------------------------

### Generate Survival Plots with exp_surv

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/exp_surv.Rd

Examples demonstrating how to generate survival plots using default median grouping and custom cut-point grouping, visualized with patchwork.

```R
tmp = exp_surv(exprSet_hub1,meta1)
patchwork::wrap_plots(tmp)+patchwork::plot_layout(guides = "collect")
tmp2 = exp_surv(exprSet_hub1,meta1,cut.point = TRUE)
patchwork::wrap_plots(tmp2)+patchwork::plot_layout(guides = "collect")
```

--------------------------------

### Generate 3D PCA plots

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/draw_pca.Rd

Example demonstrating how to generate a 3D PCA plot, requiring scatterplot3d and FactoMineR packages.

```R
if(requireNamespace("scatterplot3d",quietly = TRUE)&
   requireNamespace("FactoMineR",quietly = TRUE)){
  draw_pca(t(iris[,1:4]),iris$Species,style = "3D")
}else{
  if(!requireNamespace("scatterplot3d",quietly = TRUE)){
    warning("Package 'scatterplot3d' needed for this function to work.
         Please install it by install.packages('scatterplot3d')")
  }
  if(!requireNamespace("FactoMineR",quietly = TRUE)){
    warning("Package 'FactoMineR' needed for this function to work.
         Please install it by install.packages('FactoMineR')")
  }
}
```

--------------------------------

### Perform differential expression analysis with get_deg_all

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/get_deg_all.Rd

This example demonstrates how to download GEO data, prepare group lists and probe IDs, and execute the differential expression analysis pipeline.

```R
\dontrun{
if(requireNamespace("Biobase",quietly = TRUE)&
   requireNamespace("AnnoProbe",quietly = TRUE)){
  gse = "GSE42872"
  geo = geo_download(gse,destdir=tempdir())
  group_list = rep(c("A","B"),each = 3)
  group_list = factor(group_list)
  find_anno(geo$gpl)
  ids <- AnnoProbe::idmap(geo$gpl,destdir = tempdir())
  dcp = get_deg_all(geo$exp,group_list,ids,entriz = FALSE)
  head(dcp$deg)
  dcp$plots
}else{
  if(!requireNamespace("AnnoProbe",quietly = TRUE)) {
    warning("Package 'AnnoProbe' needed for this function to work.
         Please install it by install.packages('AnnoProbe')",call. = FALSE)
  }
  if(!requireNamespace("Biobase",quietly = TRUE)) {
    warning("Package 'Biobase' needed for this function to work.
         Please install it by BiocManager::install('Biobase')",call. = FALSE)
  }
}
}
```

--------------------------------

### Generate box and survival plots with box_surv

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/box_surv.Rd

Demonstrates the usage of box_surv, requiring the ggpubr package to be installed.

```R
if(requireNamespace("ggpubr",quietly = TRUE)) {
k = box_surv(log2(exp_hub1+1),exprSet_hub1,meta1);k[[1]]
}else{
 warning("Package 'ggpubr' needed for this function to work.
        Please install it by install.packages('ggpubr')")
}
```

--------------------------------

### Generate enrichment bar plots with double_enrich

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/double_enrich.Rd

This example demonstrates how to call double_enrich while checking for required dependencies org.Hs.eg.db and labeling.

```R
if(requireNamespace("org.Hs.eg.db",quietly = TRUE)&
   requireNamespace("labeling",quietly = TRUE)){
   double_enrich(deg)
}else{
  if(!requireNamespace("org.Hs.eg.db",quietly = TRUE)) {
    warning("Package 'org.Hs.eg.db' needed for this function to work.
        Please install it by BiocManager::install('org.Hs.eg.db')",call. = FALSE)
  }
  if(!requireNamespace("labeling",quietly = TRUE)) {
    warning("Package 'labeling' needed for this function to work.
        Please install it by install.packages('labeling')",call. = FALSE)
  }
}
```

--------------------------------

### Generate a heatmap plot

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/draw_heatmap2.Rd

This example demonstrates how to download GEO data, perform differential expression analysis, and generate a heatmap using draw_heatmap2.

```R
if(requireNamespace("Biobase",quietly = TRUE)&
   requireNamespace("AnnoProbe",quietly = TRUE)){
  gse = "GSE474"
  geo = geo_download(gse,destdir=tempdir())
  geo$exp[1:4,1:4]
  geo$exp=log2(geo$exp+1)
  group_list=ifelse(stringr::str_detect(geo$pd$title,"MObese"),"MObese",
  ifelse(stringr::str_detect(geo$pd$title,"NonObese"),"NonObese","Obese"))
  group_list=factor(group_list,levels = c("NonObese","Obese","MObese"))
  find_anno(geo$gpl)
  ids <- AnnoProbe::idmap(geo$gpl,destdir = tempdir())
  deg = multi_deg(geo$exp,group_list,ids,adjust = FALSE,entriz = FALSE)
  draw_heatmap2(geo$exp,group_list,deg)
}else{
  if(!requireNamespace("AnnoProbe",quietly = TRUE)) {
    warning("Package 'AnnoProbe' needed for this function to work.
         Please install it by install.packages('AnnoProbe')",call. = FALSE)
  }
  if(!requireNamespace("Biobase",quietly = TRUE)) {
    warning("Package 'Biobase' needed for this function to work.
         Please install it by BiocManager::install('Biobase')",call. = FALSE)
  }
}
```

--------------------------------

### Generate PCA plots with draw_pca

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/draw_pca.Rd

Examples demonstrating how to generate PCA plots using different styles and custom colors, requiring FactoMineR and factoextra packages.

```R
if(requireNamespace("FactoMineR",quietly = TRUE)&
   requireNamespace("factoextra",quietly = TRUE)){
  draw_pca(t(iris[,1:4]),iris$Species)
  draw_pca(t(iris[,1:4]),iris$Species,style = "ggplot2")
  #change color
  draw_pca(t(iris[,1:4]),iris$Species,color = c("#E78AC3", "#A6D854", "#FFD92F"))
}else{
  if(!requireNamespace("FactoMineR",quietly = TRUE)){
    warning("Package 'FactoMineR' needed for this function to work.
         Please install it by install.packages('FactoMineR')")
  }
  if(!requireNamespace("factoextra",quietly = TRUE)){
    warning("Package 'factoextra' needed for this function to work.
         Please install it by install.packages('factoextra')")
  }
}
```

--------------------------------

### Generate a heatmap with draw_heatmap

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/draw_heatmap.Rd

Example usage of draw_heatmap with synthetic data and the iris dataset, demonstrating basic and customized plotting.

```R
#example data
exp = matrix(abs(rnorm(60,sd = 16)),nrow = 10)
exp[,4:6] <- exp[,4:6]+20
colnames(exp) <- paste0("sample",1:6)
rownames(exp) <- paste0("gene",1:10)
exp[1:4,1:4]
group_list = factor(rep(c("A","B"),each = 3))
if(requireNamespace("ggplotify",quietly = TRUE)){
  draw_heatmap(exp,group_list)
  #use iris
  n = t(iris[,1:4]);colnames(n) = 1:150
  group_list = iris$Species
  draw_heatmap(n,group_list)
  draw_heatmap(n,group_list,color = colorRampPalette(c("green","black","red"))(100),
               color_an = c("red","blue","pink") )
}else{
  warning("Package 'ggplotify' needed for this function to work.
    Please install it by install.packages('ggplotify')")
}
```

--------------------------------

### Generate a Kaplan-Meier plot

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/draw_KM.Rd

Example usage of draw_KM using the lung dataset from the survival package.

```R
require("survival")
x = survival::lung
draw_KM(meta = x,
       group_list = x$sex,event_col = "status")
```

--------------------------------

### Generate a Venn Diagram

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/draw_venn.Rd

Creates a Venn diagram from a list of sets. Requires the VennDiagram, ggplotify, and cowplot packages to be installed.

```R
if(requireNamespace("VennDiagram",quietly = TRUE)&
  requireNamespace("ggplotify",quietly = TRUE)&
  requireNamespace("cowplot",quietly = TRUE)){
 x = list(Deseq2=sample(1:100,30),edgeR = sample(1:100,30),limma = sample(1:100,30))
 draw_venn(x,"test")
 draw_venn(x,"test",color = c("darkgreen", "darkblue", "#B2182B"))
}else{
 if(!requireNamespace("VennDiagram",quietly = TRUE)) {
   warning("Package 'VennDiagram' needed for this function to work.
   Please install it by install.packages('VennDiagram')")
 }
 if(!requireNamespace("ggplotify",quietly = TRUE)) {
   warning("Package 'ggplotify' needed for this function to work.
   Please install it by install.packages('ggplotify')")
 }
 if(!requireNamespace("cowplot",quietly = TRUE)) {
   warning("Package 'cowplot' needed for this function to work.
   Please install it by install.packages('cowplot')")
 }
}
```

--------------------------------

### Perform differential expression analysis with multi_deg

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/multi_deg.Rd

Example usage of multi_deg involving data download, preprocessing, and annotation mapping using Biobase and AnnoProbe.

```R
if(requireNamespace("Biobase",quietly = TRUE)&
   requireNamespace("AnnoProbe",quietly = TRUE)){
  gse = "GSE474"
  geo = geo_download(gse,destdir=tempdir())
  geo$exp[1:4,1:4]
  geo$exp=log2(geo$exp+1)
  group_list=ifelse(stringr::str_detect(geo$pd$title,"MObese"),
  "MObese",ifelse(stringr::str_detect(geo$pd$title,"NonObese"),
  "NonObese","Obese"))
  group_list=factor(group_list,levels = c("NonObese","Obese","MObese"))
  find_anno(geo$gpl)
  ids <- AnnoProbe::idmap(geo$gpl,destdir = tempdir())
  deg = multi_deg(geo$exp,group_list,ids,adjust = FALSE,entriz = FALSE)
  names(deg)
  head(deg[[1]])
  head(deg[[2]])
  head(deg[[3]])
}else{
  if(!requireNamespace("AnnoProbe",quietly = TRUE)) {
    warning("Package 'AnnoProbe' needed for this function to work.
         Please install it by install.packages('AnnoProbe')",call. = FALSE)
  }
  if(!requireNamespace("Biobase",quietly = TRUE)) {
    warning("Package 'Biobase' needed for this function to work.
         Please install it by BiocManager::install('Biobase')",call. = FALSE)
  }
}
```

--------------------------------

### Generate Differential Expression Plots

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/plot_deg.Rd

Example usage of plot_deg with data downloaded via geo_download and processed with AnnoProbe.

```R
if(requireNamespace("Biobase",quietly = TRUE)&
   requireNamespace("AnnoProbe",quietly = TRUE)){
gse = "GSE474"
geo = geo_download(gse,destdir=tempdir())
geo$exp[1:4,1:4]
geo$exp=log2(geo$exp+1)
group_list=ifelse(stringr::str_detect(geo$pd$title,"MObese"),"MObese",
ifelse(stringr::str_detect(geo$pd$title,"NonObese"),"NonObese","Obese"))
group_list=factor(group_list,levels = c("NonObese","Obese","MObese"))
find_anno(geo$gpl)
ids = AnnoProbe::idmap(geo$gpl,destdir = tempdir())
deg = get_deg(geo$exp,group_list,ids,adjust = FALSE,entriz = FALSE)
plot_deg(geo$exp,group_list,deg)
}else{
  if(!requireNamespace("AnnoProbe",quietly = TRUE)) {
    warning("Package 'AnnoProbe' needed for this function to work.
         Please install it by install.packages('AnnoProbe')",call. = FALSE)
  }
  if(!requireNamespace("Biobase",quietly = TRUE)) {
    warning("Package 'Biobase' needed for this function to work.
         Please install it by BiocManager::install('Biobase')",call. = FALSE)
  }
}
```

--------------------------------

### Transform Entrez IDs to Gene Symbols

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/trans_entrezexp.Rd

Example usage of trans_entrezexp on a matrix with Entrez ID rownames. Requires the org.Hs.eg.db package for human data.

```R
exp = matrix(rnorm(200),ncol = 10)
rownames(exp) = c("79691", "56271", "8662", "10394", "55630", "159162", "23541",
                  "79723", "54413", "22927", "92342", "23787", "5550", "8924",
                  "55274", "866", "8844", "353299", "587", "1473")
colnames(exp) = paste0("s",1:10)
if(requireNamespace("org.Hs.eg.db",quietly = TRUE)){
exp2 = trans_entrezexp(exp)
}else{
    warning("Package \"org.Hs.eg.db\" needed for this function to work.
        Please install it by BiocManager::install('org.Hs.eg.db')",call. = FALSE)
}
```

--------------------------------

### Inspect the pkg_all dataset

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/pkg_all.Rd

Display the first few rows of the pkg_all data frame to verify its structure.

```R
head(pkg_all)
```

--------------------------------

### get_gpl_txt(gpl, destdir = getwd(), download = FALSE)

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/get_gpl_txt.Rd

Retrieves a GPL annotation text file from the GEO database. Users can specify the accession ID, destination directory, and whether to trigger a download.

```APIDOC
## get_gpl_txt

### Description
Retrieves a GPL annotation text file from the GEO database.

### Signature
`get_gpl_txt(gpl, destdir = getwd(), download = FALSE)`

### Parameters
- **gpl** (string) - Required - The GPL accession ID from the GEO database.
- **destdir** (string) - Optional - The destination directory for data downloads. Defaults to the current working directory.
- **download** (boolean) - Optional - Whether to download the txt file. Defaults to FALSE.

### Returns
- **list** - A list containing the deg data.frame, a volcano plot, and a list of DEGs.

### Example
`get_gpl_txt("GPL23270", destdir = tempdir())`
```

--------------------------------

### Download GSE data using geo_download

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/geo_download.Rd

Demonstrates how to download a specific GSE dataset while checking for required dependencies like Biobase and AnnoProbe.

```R
if(requireNamespace("Biobase",quietly = TRUE)&
   requireNamespace("AnnoProbe",quietly = TRUE)){
  gse = "GSE42872"
  a = geo_download(gse,destdir=tempdir())
}else{
  if(!requireNamespace("AnnoProbe",quietly = TRUE)) {
    print("Package 'AnnoProbe' needed for this function to work.
         Please install it by install.packages('AnnoProbe')"print)
  }
  if(!requireNamespace("Biobase",quietly = TRUE)) {
    print("Package 'Biobase' needed for this function to work.
         Please install it by BiocManager::install('Biobase')"print)
  }
}
```

--------------------------------

### Perform differential expression analysis with get_deg

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/get_deg.Rd

Example usage of get_deg requiring the Biobase and AnnoProbe packages to process GEO data.

```R
if(requireNamespace("Biobase",quietly = TRUE)&
   requireNamespace("AnnoProbe",quietly = TRUE)){
  gse = "GSE42872"
  a = geo_download(gse,destdir=tempdir())
  find_anno(geo$gpl)
  ids <- AnnoProbe::idmap(geo$gpl,destdir = tempdir())
  Group = rep(c("control","treat"),each = 3)
  Group = factor(Group)
  deg = get_deg(geo$exp,Group,ids,entriz = FALSE)
  head(deg)
}else{
  if(!requireNamespace("AnnoProbe",quietly = TRUE)) {
    warning("Package 'AnnoProbe' needed for this function to work.
         Please install it by install.packages('AnnoProbe')",call. = FALSE)
  }
  if(!requireNamespace("Biobase",quietly = TRUE)) {
    warning("Package 'Biobase' needed for this function to work.
         Please install it by BiocManager::install('Biobase')",call. = FALSE)
  }
}
```

--------------------------------

### quick_enrich(genes, kkgo_file = "kkgo_file.Rdata", destdir = getwd(), species = "human")

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/quick_enrich.Rd

Performs enrichment analysis and generates results and dotplots.

```APIDOC
## quick_enrich

### Description
Performs enrichment analysis based on a provided vector of gene symbols or Entrez IDs. The function saves KEGG and GO results to a specified directory.

### Parameters
- **genes** (vector) - Required - A gene symbol or Entrez ID vector.
- **kkgo_file** (string) - Optional - Rdata filename for KEGG and GO results. Default: "kkgo_file.Rdata".
- **destdir** (string) - Optional - Directory to save the result file. Default: current working directory.
- **species** (string) - Optional - Species selection: "human", "mouse", or "rat". Default: "human".

### Returns
- **results** (list) - Enrichment results and dotplots.
```

--------------------------------

### Find annotation for a GPL accession

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/find_anno.Rd

Locate the annotation package or files for a specific GPL accession.

```R
find_anno("GPL570")
```

--------------------------------

### geo_download(gse, by_annopbrobe = TRUE, simpd = TRUE, colon_remove = FALSE, destdir = getwd(), n = 1)

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/geo_download.Rd

Downloads GSE data and returns a list containing expression data (exp), phenotypic data (pd), and GPL information.

```APIDOC
## geo_download

### Description
Downloads GSE data and retrieves associated information. Returns a list containing expression data, phenotypic data, and GPL information.

### Signature
`geo_download(gse, by_annopbrobe = TRUE, simpd = TRUE, colon_remove = FALSE, destdir = getwd(), n = 1)`

### Parameters
- **gse** (string) - Required - The GSE accession number.
- **by_annopbrobe** (boolean) - Optional - Whether to download data by geoquery or annoprobe. Defaults to TRUE.
- **simpd** (boolean) - Optional - Whether to get simplified pdata by dropping columns with all identical values. Defaults to TRUE.
- **colon_remove** (boolean) - Optional - Whether to remove duplicated columns with colons. Defaults to FALSE.
- **destdir** (string) - Optional - The destination directory for data downloads. Defaults to current working directory.
- **n** (integer) - Optional - For data with more than one ExpressionSet, specify which one to analyze. Defaults to 1.

### Returns
- **list** - A list containing 'exp' (expression data), 'pd' (phenotypic data), and 'gpl' (GPL information).
```

--------------------------------

### Generate a correlation scatter plot

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/corscatterplot.Rd

Creates a scatter plot using the iris dataset with marginal density plots. Requires the ggpubr package to be installed.

```R
if(requireNamespace("ggpubr",quietly = TRUE)){
corscatterplot(iris,"Sepal.Length","Sepal.Width")
}else{
  warning("Package 'ggpubr' needed for this function to work.
           Please install it by install.packages('ggpubr')")
}
```

--------------------------------

### Inspect mRNA_anno dataset

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/mRNA_anno.Rd

Display the first few rows of the mRNA_anno data frame to verify its structure.

```R
head(mRNA_anno)
```

--------------------------------

### Perform differential expression analysis with t_choose

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/t_choose.Rd

Demonstrates setting up a sample expression matrix and group list to identify differentially expressed genes.

```R
exp = matrix(rnorm(1000),ncol = 10)
rownames(exp) = sample(mRNA_annov23$gene_id,100)
colnames(exp) = c(paste0("TCGA",1:5),paste0("GTEX",1:5))
exp2  = trans_exp(exp)
exp2[,1:5] = exp2[,1:5]+10
group_list = rep(c("A","B"),each = 5)
genes = sample(rownames(exp2),3)
t_choose(genes,exp2,group_list)
```

--------------------------------

### draw_venn(x, main, ...)

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/draw_venn.Rd

Generates a Venn diagram based on the provided list of sets.

```APIDOC
## draw_venn

### Description
Creates a Venn diagram visualization for a list of sets, typically used for comparing differential expression results.

### Parameters
- **x** (list) - Required - A list of sets to plot.
- **main** (character) - Required - The main title of the diagram.
- **color** (vector) - Optional - Color vector for the diagram.
- **imagetype** (string) - Optional - Image format (e.g., tiff, png, svg).
- **filename** (string) - Optional - Filename for output; if NULL, returns the grid object.
- **lwd** (numeric) - Optional - Width of the circle's circumference.
- **lty** (numeric) - Optional - Dash pattern of the circle's circumference.
- **col** (vector) - Optional - Colour of the circle's circumference.
- **fill** (vector) - Optional - Colour of the circle's area.
- **cat.col** (vector) - Optional - Colour of the category name.
- **cat.cex** (numeric) - Optional - Size of the category name.
- **cat.dist** (numeric) - Optional - Distance of the category name from the edge.
- **rotation.degree** (numeric) - Optional - Degrees to rotate the diagram.
- **main.cex** (numeric) - Optional - Font size of the main title.
- **cex** (numeric) - Optional - Size of the area label.
- **alpha** (numeric) - Optional - Alpha transparency of the circle's area.
- **reverse** (logical) - Optional - Reflect the three-set Venn diagram along its vertical axis.
- **...** (dots) - Optional - Other parameters passed to venn.diagram.

### Returns
A venn plot or a grid object if filename is NULL.
```

--------------------------------

### Calculate intersection of vectors

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/intersect_all.Rd

Demonstrates calculating the intersection of multiple vectors using individual arguments or a list, and shows the related union_all function.

```R
x1 = letters[1:4]
x2 = letters[3:6]
x3 = letters[3:4]
re =intersect_all(x1,x2,x3)
re2 = intersect_all(list(x1,x2,x3))
re3 = union_all(x1,x2,x3)
```

--------------------------------

### Accessing the cod dataset

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/cod.Rd

Displays the cod matrix object in the R console.

```R
  cod
```

--------------------------------

### Inspect lnc_anno data

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/lnc_anno.Rd

Display the first few rows of the lnc_anno dataset to verify its structure.

```R
head(lnc_anno)
```

--------------------------------

### Generate box plots for hub genes

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/exp_boxplot.Rd

Demonstrates how to generate box plots from an expression matrix, checking for the required ggpubr package.

```R
if(requireNamespace("ggpubr",quietly = TRUE)) {
k = exp_boxplot(log2(exp_hub1+1));k[[1]]
}else{
 warning("Package 'ggpubr' needed for this function to work.
        Please install it by install.packages('ggpubr')")
}
```

--------------------------------

### Transform Ensembl IDs to Gene Symbols

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/trans_exp_new.Rd

Demonstrates how to prepare an expression matrix and apply the trans_exp_new function, requiring the AnnoProbe package.

```R
exp = matrix(rnorm(1000),ncol = 10)
rownames(exp) = sample(mRNA_annov23$gene_id,100)
colnames(exp) = c(paste0("TCGA",1:5),paste0("GTEX",1:5))
if(requireNamespace("AnnoProbe")){
k  = trans_exp_new(exp)
}else{
  warning("Package \"AnnoProbe\" needed for this function to work.
         Please install it by install.packages('AnnoProbe')")
}
```

--------------------------------

### Inspect meta1 dataset

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/meta1.Rd

Displays the first few rows of the meta1 data.frame.

```R
head(meta1)
```

--------------------------------

### Inspect deg dataset

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/deg.Rd

Display the first few rows of the deg data frame.

```R
  head(deg)
```

--------------------------------

### Retrieve GPL annotation text

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/get_gpl_txt.Rd

Downloads or accesses a GPL annotation file from the GEO database using a specific accession ID.

```R
get_gpl_txt("GPL23270",destdir = tempdir())
```

--------------------------------

### exp_surv(exprSet_hub, meta, cut.point = FALSE, color = c("#2874C5", "#f87669"))

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/exp_surv.Rd

Draws survival plots for a hub gene expression matrix for tumor samples. Users can specify whether to use a cut point or the median for survival analysis.

```APIDOC
## exp_surv

### Description
Draws survival plots for a hub gene expression matrix for tumor samples.

### Signature
`exp_surv(exprSet_hub, meta, cut.point = FALSE, color = c("#2874C5", "#f87669"))`

### Parameters
- **exprSet_hub** (object) - Required - A tumor expression set for hubgenes.
- **meta** (object) - Required - Meta data corresponding to the expression set.
- **cut.point** (logical) - Optional - Whether to use a cut point; if FALSE, uses the median by default.
- **color** (vector) - Optional - Color palette for the boxplot.

### Returns
- **list** - A list of survival plots for all genes.

### Example
```r
tmp = exp_surv(exprSet_hub1, meta1)
patchwork::wrap_plots(tmp) + patchwork::plot_layout(guides = "collect")

tmp2 = exp_surv(exprSet_hub1, meta1, cut.point = TRUE)
patchwork::wrap_plots(tmp2) + patchwork::plot_layout(guides = "collect")
```
```

--------------------------------

### Inspect deseq_data

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/deseq_data.Rd

Display the first few rows of the deseq_data dataset.

```R
head(deseq_data)
```

--------------------------------

### Inspect mRNA_annov23 data

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/mRNA_annov23.Rd

Display the first few rows of the mRNA_annov23 dataset to verify its structure.

```R
head(mRNA_annov23)
```

--------------------------------

### Inspect exprSet_hub1 data subset

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/exprSet_hub1.Rd

Displays the first four rows and columns of the exprSet_hub1 matrix.

```R
exprSet_hub1[1:4,1:4]
```

--------------------------------

### cor.full(x, drop = min(x) - 0.001, min.obs = 10)

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/cor.full.Rd

Performs cor.test for all pairs of columns in the provided input.

```APIDOC
## cor.full(x, drop, min.obs)

### Description
Performs cor.test for all variables (each two columns) in a numeric matrix or data.frame.

### Parameters
- **x** (numeric matrix or data.frame) - Required - The input data to analyze.
- **drop** (numeric) - Optional - Values to drop. Defaults to min(x) - 0.001.
- **min.obs** (numeric) - Optional - Minimum number of observations after dropping. Defaults to 10.

### Returns
- **data.frame** - A data frame containing the cor.test p-value and estimate for each pair of columns.

### Example
```r
x = iris[,-5]
cor.full(x)
```
```

--------------------------------

### Calculate Cox p-values and HR

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/surv_cox.Rd

Basic usage of surv_cox with default parameters.

```R
surv_cox(exprSet_hub1,meta1)
```

--------------------------------

### Generate a correlation heatmap

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/corheatmap.Rd

Creates a heatmap visualization based on correlation data from a provided expression matrix.

```R
x = rownames(exprSet_hub1)[1:3]
y = rownames(exprSet_hub1)[4:7]
corheatmap(exprSet_hub1,x,y)
```

--------------------------------

### Filter duplicated samples from expression data

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/sam_filter.Rd

Demonstrates how to inspect the dimensions of an expression matrix, apply the sam_filter function, and verify the removal of duplicates.

```R
cod[1:4,1:4]
dim(cod)
cod2 = sam_filter(cod)
dim(cod2)
g = make_tcga_group(cod);table(g)
library(stringr)
table(!duplicated(str_sub(colnames(cod[,g=="tumor"]),1,12)))
```

--------------------------------

### Accessing exp_hub1 data

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/exp_hub1.Rd

Displays a subset of the expression matrix by indexing the first four rows and columns.

```R
exp_hub1[1:4,1:4]
```

--------------------------------

### draw_volcano(deg, lab = NA, xlab.package = TRUE, pvalue_cutoff = 0.05, logFC_cutoff = 1, pkg = 1, adjust = FALSE, symmetry = FALSE, color = c("#2874C5", "grey", "#f87669"))

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/draw_volcano.Rd

Generates a volcano plot for differential analysis results.

```APIDOC
## draw_volcano

### Description
Generates a volcano plot for differential analysis results stored in a data.frame.

### Arguments
- **deg** (data.frame) - Required - A data.frame created by differential analysis.
- **lab** (character) - Optional - Label for the x-axis; if NA, uses package-specific naming.
- **xlab.package** (logical) - Optional - Whether to use the package name as the x-axis label.
- **pvalue_cutoff** (numeric) - Optional - Cutoff value for p-value (default 0.05).
- **logFC_cutoff** (numeric) - Optional - Cutoff value for logFC (default 1).
- **pkg** (integer) - Optional - Identifier for the differential analysis package used (1: DESeq2, 2: edgeR, 3: limma(voom), 4: limma).
- **adjust** (logical) - Optional - Whether to use adjusted p-values (default FALSE).
- **symmetry** (logical) - Optional - Whether to make the plot symmetrical.
- **color** (vector) - Optional - Color vector for the plot.

### Returns
A volcano plot visualization based on logFC and P-value (or adjusted P-value).

### Example
```R
draw_volcano(deseq_data, pvalue_cutoff = 0.01, logFC_cutoff = 2)
```
```

--------------------------------

### Accessing exists_anno_list

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/exists_anno_list.Rd

Displays the contents of the exists_anno_list character vector.

```R
  exists_anno_list
```

--------------------------------

### Inspect lnc_annov23 data

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/lnc_annov23.Rd

Display the first few rows of the lnc_annov23 data frame to verify its structure.

```R
head(lnc_annov23)
```

--------------------------------

### box_surv(exp_hub, exprSet_hub, meta)

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/box_surv.Rd

Draws a box plot for a hub gene expression matrix and a corresponding survival plot.

```APIDOC
## box_surv(exp_hub, exprSet_hub, meta)

### Description
Draws a box plot for a hub gene expression matrix and a corresponding survival plot.

### Parameters
- **exp_hub** (matrix) - Required - An expression matrix for hubgenes.
- **exprSet_hub** (matrix) - Required - A tumor expression set for hubgenes.
- **meta** (data.frame) - Required - Meta data corresponding to the expression set.

### Returns
- **patchwork** - A patchwork result containing the hub genes boxplot and survival plot.

### Example
```r
if(requireNamespace("ggpubr",quietly = TRUE)) {
k = box_surv(log2(exp_hub1+1),exprSet_hub1,meta1);k[[1]]
}else{
 warning("Package 'ggpubr' needed for this function to work.")
}
```
```

--------------------------------

### Convert interactions to edges in R

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/interaction_to_edges.Rd

Demonstrates transforming a data frame with delimited interaction strings into a tidy edges format.

```R
df = data.frame(a = c("gene1","gene2","gene3"),
b = c("d,f,a,b",
"c,e,g",
"a,b,d"))
interaction_to_edges(df)
```

--------------------------------

### draw_pca(exp, group_list, color, addEllipses, style, color.label, title, ...)

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/draw_pca.Rd

Performs PCA analysis and generates a PCA plot based on the provided expression matrix and grouping information.

```APIDOC
## draw_pca

### Description
Performs PCA analysis and generates a PCA plot. The function supports multiple styles including default, ggplot2, and 3D.

### Parameters
- **exp** (numeric matrix) - Required - The input data matrix.
- **group_list** (factor) - Required - A factor with duplicated character or factor values for grouping.
- **color** (vector) - Optional - A vector of colors to be used in the plot.
- **addEllipses** (logical) - Optional - Whether to add ellipses to the plot. Defaults to TRUE.
- **style** (string) - Optional - The plot style, options are "default", "ggplot2", or "3D".
- **color.label** (string) - Optional - The label for the color legend.
- **title** (string) - Optional - The title of the plot.
- **...** (dots) - Optional - Additional parameters passed to fviz_pca_ind.

### Returns
- A PCA plot object based on the input expression matrix and grouping.
```

--------------------------------

### draw_KM(meta, group_list, time_col = "time", event_col = "event", ...)

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/draw_KM.Rd

Generates a Kaplan-Meier survival plot using survival data and grouping information.

```APIDOC
## draw_KM

### Description
Draws a Kaplan-Meier survival plot for two or more groups.

### Parameters
- **meta** (data.frame) - Required - Survival data containing time and event columns.
- **group_list** (factor) - Required - A factor with duplicated character or factor values for grouping.
- **time_col** (string) - Optional - Column name for time. Default is "time".
- **event_col** (string) - Optional - Column name for event. Default is "event".
- **legend.title** (string) - Optional - Title for the plot legend.
- **legend.labs** (vector) - Optional - Character vector specifying legend labels.
- **color** (vector) - Optional - Color vector for the plot lines.
- **...** (dots) - Optional - Additional parameters passed to ggsurvplot.

### Returns
- **KM-plot** (object) - A Kaplan-Meier plot object.

### Example
```R
require("survival")
x = survival::lung
draw_KM(meta = x, group_list = x$sex, event_col = "status")
```
```

--------------------------------

### Calculate Cox p-values with cut-point and continuous options

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/surv_cox.Rd

Usage of surv_cox with cut-point enabled and continuous expression data.

```R
surv_cox(exprSet_hub1,meta1,cut.point = TRUE,continuous = TRUE)
```

--------------------------------

### draw_volcano2(deg, pkg = 4, lab, ...)

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/draw_volcano2.Rd

Generates volcano plots for differential analysis results. Supports multiple differential analysis packages including DESeq2, edgeR, and limma.

```APIDOC
## draw_volcano2

### Description
Prints one or more volcano plots for differential analysis results provided in a data.frame format.

### Signature
`draw_volcano2(deg, pkg = 4, lab, ...)`

### Parameters
- **deg** (data.frame) - Required - A data.frame created by differential analysis.
- **pkg** (integer) - Optional - Specifies the differential analysis package used. Supported values: 1 (DESeq2), 2 (edgeR), 3 (limma(voom)), 4 (limma). Default is 4.
- **lab** (string) - Optional - Label for the x-axis in the volcano plot. If NA, the x-axis is named by the package.
- **...** (parameters) - Optional - Additional parameters passed to the underlying draw_volcano function.

### Returns
- **plot** (object) - One or more volcano plots.
```

--------------------------------

### Transform expression matrix rownames

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/trans_array.Rd

Demonstrates how to map probe IDs to gene symbols in an expression matrix using a mapping data frame.

```R
exp = matrix(1:50,nrow = 10)
rownames(exp) = paste0("g",1:10)
ids = data.frame(probe_id = paste0("g",1:10),
                symbol = paste0("G",c(1:9,9)))
trans_array(exp,ids)
```

--------------------------------

### Accessing the genes dataset

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/genes.Rd

Display the contents of the genes character vector.

```R
  genes
```

--------------------------------

### surv_cox(exprSet_hub, meta, cut.point = FALSE, pvalue_cutoff = 0.05, HRkeep = "all", continuous = FALSE, min_gn = 0.1)

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/surv_cox.Rd

Calculates Cox p-values and HR for genes based on provided expression sets and metadata.

```APIDOC
## surv_cox

### Description
Calculate cox p values and HR for genes.

### Parameters
- **exprSet_hub** (matrix/data.frame) - Required - A tumor expression set for hubgenes.
- **meta** (data.frame) - Required - Meta data corresponds to expression set.
- **cut.point** (logical) - Optional - Use cut_point or not, if FALSE, use median by default.
- **pvalue_cutoff** (numeric) - Optional - p value cut off, 0.05 by default.
- **HRkeep** (string) - Optional - One of "all", "protect" or "risk".
- **continuous** (logical) - Optional - Gene expression or gene expression group.
- **min_gn** (numeric) - Optional - If a smaller group of samples is less than 10 percent (default) of all, the gene will be discarded.

### Returns
- **matrix** - A matrix with gene names, cox p value and HR.

### Examples
```r
surv_cox(exprSet_hub1, meta1)
surv_cox(exprSet_hub1, meta1, cut.point = TRUE, continuous = TRUE)
surv_cox(exprSet_hub1, meta1, cut.point = TRUE, continuous = TRUE, pvalue_cutoff = 1)
```
```

--------------------------------

### Generate a t-SNE plot

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/draw_tsne.Rd

Demonstrates how to prepare a numeric matrix and group list to visualize data using the draw_tsne function.

```R
exp <-  matrix(rnorm(10000),nrow = 50)
colnames(exp) <- paste0("sample",1:200)
rownames(exp) <- paste0("gene",1:50)
exp[1:4,1:4]
exp[,1:100] = exp[,1:100]+10
group_list <- factor(rep(c("A","B"),each = 100))
if(requireNamespace("Rtsne",quietly = TRUE)){
draw_tsne(exp,group_list)
}else{
 warning("Package 'Rtsne' needed for this function to work.
        Please install it by install.packages('Rtsne')")
}
```

--------------------------------

### ggheat(dat, group, ...)

Source: https://github.com/xjsun1221/tinyarray/blob/master/man/ggheat.Rd

Draws a heatmap plot with annotation using ggplot2.

```APIDOC
## ggheat(dat, group, cluster = FALSE, color = c("#2874C5", "white", "#f87669"), legend_color = ..., show_rownames = TRUE, show_colnames = TRUE, cluster_rows = FALSE, cluster_cols = FALSE, groupname = "group", expname = "exp", fill_mid = TRUE)

### Description
Draws a heatmap plot with annotation by ggplot2.

### Parameters
- **dat** (matrix) - Required - Expression matrix for plot.
- **group** (vector) - Required - Group for expression colnames.
- **cluster** (logical) - Optional - Cluster in both rows and column or not, default F (replaced by cluster_rows and cluster_cols).
- **color** (vector) - Optional - Color for heatmap.
- **legend_color** (vector) - Optional - Color for legend.
- **show_rownames** (logical) - Optional - Show rownames in plot or not, default T.
- **show_colnames** (logical) - Optional - Show colnames in plot or not, default T.
- **cluster_rows** (logical) - Optional - If rows (on the plot) should be clustered, default F.
- **cluster_cols** (logical) - Optional - If column (on the plot) should be clustered, default F.
- **groupname** (string) - Optional - Name of group legend.
- **expname** (string) - Optional - Name of exp legend.
- **fill_mid** (logical) - Optional - Use median value as geom_tile fill midpoint.

### Returns
- **ggplot object** - A ggplot object representing the heatmap.
```