chromoMap

https://rdrr.io/cran/chromoMap/
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chromoMap is an R package for creating interactive, configurable chromosome visualizations with...
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### Install and Load ChromoMap

Source: https://rdrr.io/cran/chromoMap/f/vignettes/chromoMap.Rmd

Installs and loads the chromoMap package. Ensure you have devtools installed for the install_github function.

```r
if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")

devtools::install_github("Hic-Tools/chromomap")

```

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

### Display Guides and Set Guide Color

Source: https://rdrr.io/cran/chromoMap/f/vignettes/chromoMap.Rmd

Show background guides to separate genomic windows using the `guides` argument. Set the guide color with `guides_color`.

```r
chromoMap("chromosome_file.txt","annotation_file.txt",
          guides = T,
          guides_color = "black")
```

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

### ChromoMap with Guides

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Adds guides to the ChromoMap plot with a specified color.

```r
chromoMap("chromosome_file.txt","annotation_file.txt",
            guides = T,
            guides_color = "black")
```

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

### Polyploidy Example in chromoMap

Source: https://rdrr.io/cran/chromoMap/man/chromoMap.html

Demonstrates plotting with polyploidy support. This is useful when dealing with genomes having multiple sets of chromosomes.

```R
#polyploidy example
chromoMap(c("chromosome_set1.txt","chromosome_set2.txt"),
          c("annotation_set1.txt","annotation_set2.txt"), ploidy=2)
```

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

### Install chromoMap Package

Source: https://rdrr.io/cran/chromoMap

Install the latest version of the chromoMap package from CRAN. This is the initial step before using the package's functionalities.

```r
install.packages("chromoMap")
```

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

### Accessing ChromoMap Help

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Demonstrates how to access the help documentation for the chromoMap function in R. This is a standard way to get information about function usage and parameters.

```R
library(chromoMap) help(chromoMap)
```

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

### ChromoMap with DataFrames

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Illustrates passing R data.frames directly to chromoMap instead of file paths. Shows basic usage and polyploidy example.

```R
# passing data.frames directly instead of files
chromoMap(list(chr.data),list(anno.data))
# for polyploidy
chromoMap(list(chr.data1,chr.data2),
          list(anno.data1,anno.data2),ploidy = 2)
```

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

### Example Usage of renderChromoMap

Source: https://rdrr.io/cran/chromoMap/src/R/chromoMap.R

This R code demonstrates how to use the renderChromoMap function with various parameters to create a chromoMap visualization. It includes options for grid text, scatter mapping, links, annotations, and scaling.

```R
renderChromoMap(
    grid_text_y = grid_text_y,
    scatter_mapping = is.plotting.scatter.map,
    scatter_lg_x = ch2D.lg_x,
    scatter_lg_y = ch2D.lg_y,
    show_links = show.links,
    seg_anno = segment_annotation,
    directed_edges = directed.edges,
    y_chr_scale = y_chr_scale,
    links_colors = links.colors,
    links_lg_x = links.lg_x,
    links_lg_y = links.lg_y,
    links_color_maps=links.color.maps,
    win_scale = win.scale,
    scale_ticks = chr.scale.ticks,
    export_options = export.options,
    guides = guides,
    guides_color = guides_color,
    ann_h = ann.h,
    display_chr = display.chr,
    plot_shift = plot.shift,
    plot_legend_label = plot.legend.labels,
    cat_legend_lab = cat.legend.label,
    plot_y_labs = plot.y.labels,
    plot_y_lab_x = plot.y.lab.x,
    plot_y_lab_y = plot.y.lab.y,
    plot_y_lab_size = plot.y.lab.size,
    scale_suffix = scale.suffix,
    interactivity = interactivity
    
  )
  
  
  # create widget
  htmlwidgets::createWidget(
    name = 'chromoMap',
    x,
    width = width,
    height = height,
    package = 'chromoMap',
    htmlwidgets::sizingPolicy(padding = 10, browser.fill = TRUE)
    
    
  )
  
  
}
```

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

### ChromoMap in a Shiny Application

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Shows how to integrate chromoMap into a Shiny web application. This example defines a simple UI and server logic to render a chromoMap plot within a Shiny app.

```R
library(shiny)
library(chromoMap)

# Define UI for application that draws chromoMap
ui <- fluidPage(

   # Application title
   titlePanel("An example of chromoMap in Shiny"),

   # you can use GUI controls for your chromoMap
   sidebarLayout(
      sidebarPanel(
         #some code
      ),

      # Show a plot of the generated distribution
      mainPanel(
         chromoMapOutput("myChromoMap")
      )
   )
)

# Define server logic required to draw chromoMap
server <- function(input, output) {

   output$myChromoMap <- renderChromoMap({
     chromoMap("chromosome_file.txt","annotation_file.txt")
   })
}

# Run the application
shinyApp(ui = ui, server = server)
```

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

### Visualize Directed Edges in ChromoMap

Source: https://rdrr.io/cran/chromoMap/f/inst/doc/chromoMap.Rmd

Use the 'directed.edges = T' argument to visualize arrow-heads on links, signifying direction. This example shows basic link visualization with directed edges.

```r
chromoMap(chr_file, anno_file,
          # main arguments 
          show.links = T,
          loci_links = link_data,
          links.colors = c("red2","blue2","orange3","purple"),
          directed.edges = T,
          #plot adjustments
          ch_gap = 50,
          y_chr_scale = 100,
          top_margin = 100,
          )


```

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

### Heatmap with Different Aggregation Functions per Ploidy

Source: https://rdrr.io/cran/chromoMap/f/vignettes/chromoMap.Rmd

This example shows how to apply different aggregation functions ('avg' and 'sum') for each ploidy set. Pass a vector of aggregation functions to the aggregate_func argument when dealing with multiple chromosome/annotation file sets.

```R
chromoMap(c("chromosome_file_set_1.txt","chromosome_file_set_2.txt")
          ,c("annotation_file_set_1.txt","annotation_file_set_2.txt"), 
          ploidy = 2, data_based_color_map = T,data_type = "numeric" 
          ,aggregate_func = c("avg","sum"))


```

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

### Tag Filter Example (Less Than 0)

Source: https://rdrr.io/cran/chromoMap/f/inst/doc/chromoMap.Rmd

Filters and tags loci with values less than 0. This demonstrates applying a simple mathematical condition for selective tagging.

```R
chromoMap(chr_file_1,anno_file_2,
          data_based_color_map = T,
          data_type = "numeric",
          plots = "tags",
          tag_filter = list(c("lt",0)))


```

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

### Customized Tag Filter Example (Less Than 0)

Source: https://rdrr.io/cran/chromoMap/f/inst/doc/chromoMap.Rmd

Applies a 'less than 0' tag filter with custom canvas height and ID. Useful for managing and identifying specific data subsets.

```R
chromoMap(chr_file_1,anno_file_2,data_based_color_map = T,data_type = "numeric",canvas_height = 300,id="cmap8",
          plots = "tags",tag_filter = list(c("lt",0)))


```

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

### R kable Table for Operation Types

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Displays a formatted table of operation types, their descriptions, and usage examples using the kable function from the knitr package.

```R
library(knitr)
OPERATION_TYPE_LABEL = c("eq","lt","gt","lte","gte","gtalt","gtolt","gtealte","gteolte")
DESCRIPTION = c("equal","less than","greater than","less than equal","greater than equal","greater than AND less than","greater than OR less than","greater than equal AND less than equal","greater than equal OR less than equal")
USAGE_EXAMPLE = c('list(c("eq",5))','list(c("lt",-5))','list(c("gt",6))','list(c("lte",6))','list(c("gte",6))','list(c("gtalt",5,-5))','list(c("gtolt",5,-5))','list(c("gtealte",5,-5))','list(c("gteolte",5,-5))')
gggg = data.frame(OPERATION_TYPE_LABEL,DESCRIPTION,USAGE_EXAMPLE)
colnames(gggg)=c("operation type label","description","usage example")
kable(gggg)
```

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

### Visualize Links as Chords in ChromoMap

Source: https://rdrr.io/cran/chromoMap/f/inst/doc/chromoMap.Rmd

Use 'segment_annotation = T' to visualize links as chords, suitable for large features annotated as chromosome segments. This example includes custom link colors.

```r
chromoMap(chr_file, anno_file,
          # main arguments 
          segment_annotation = T,
          show.links = T,
          loci_links = link_data,
          links.colors = c("orange3","purple","green2"),
          #plot adjustments
          ch_gap = 50,
          y_chr_scale = 100,
          top_margin = 100,
          )


```

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

### Loading and Help for chromoMap

Source: https://rdrr.io/cran/chromoMap/src/R/chromoMap.R

This R command loads the chromoMap library and then displays the help documentation for the renderChromoMap function.

```R
library(chromoMap) help(renderChromoMap)
```

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

### Defining Chromosome Domain for Plotting

Source: https://rdrr.io/cran/chromoMap/src/R/chromoMap.R

This R code determines the domain (start and end points) for the longest chromosome, which is used for setting up the plotting canvas. It iterates through a list of pre-defined ranges and extracts the start and end positions for the specified longest chromosome.

```r
ch.domain = c()
#print(mega.list.of.ranges[[as.numeric(long.chr[1,2])]])
tmp.list.obj <- mega.list.of.ranges[[as.numeric(long.chr[1,2])]]
longest.chr.df <- data.frame()
for(f in 1:length(tmp.list.obj)){
  if(tmp.list.obj[[f]]$ch_name[1] == as.character(long.chr[1,1])){
    longest.chr.df <- tmp.list.obj[[f]]
  }
}
#print(longest.chr.df)
dom.idx = 1
for(ff in 1:nrow(longest.chr.df)){
  if(ff != nrow(longest.chr.df)){
    ch.domain[dom.idx] = longest.chr.df[ff,1]
    dom.idx = dom.idx + 1
  } else {
    
      ch.domain[dom.idx] = longest.chr.df[ff,1]
      dom.idx = dom.idx + 1
      ch.domain[dom.idx] = longest.chr.df[ff,2]
    }
  }

ch.domain <- as.character(ch.domain)
#print(ch.domain)
```

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

### Load chromoMap and Access Help

Source: https://rdrr.io/cran/chromoMap

Load the chromoMap library into your R session and access its main help documentation. This is useful for understanding the package's functions and parameters.

```r
library(chromoMap)
help(chromoMap)
```

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

### Add Title to ChromoMap Plot

Source: https://rdrr.io/cran/chromoMap/f/inst/doc/chromoMap.Rmd

Add a title to the plot using the 'title' argument. This example shows a basic title addition.

```r
chromoMap("chromosome_file.txt","annotation_file.txt",
          title = "my first chromoMap plot")


```

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

### Prepare Input Data for chromoMap

Source: https://rdrr.io/cran/chromoMap/src/R/chromoMap.R

This code prepares input data for chromoMap by ensuring the correct number of columns and setting default values for hyperlinks and categories. It handles cases with and without existing hyperlinks.

```R
rep(NA,nrow(inputData[[h]])),
                                              rep("http://",nrow(inputData[[h]])),
                                              rep(NA,nrow(inputData[[h]])));
              colnames(inputData[[h]])=c("name","ch_name","ch_start","ch_end","data","hlink","cate")
              } else {
                inputData[[h]]=cbind.data.frame(inputData[[h]],
                                                inputData[[h]][,3],
                                                rep("http://",nrow(inputData[[h]])),
                                                rep(NA,nrow(inputData[[h]])));
                colnames(inputData[[h]])=c("name","ch_name","ch_start","ch_end","data","hlink","cate")
                color_map = T
                plots[h] = "2d"
                data_based_color_map = T
                color_scale = "linear"
                heat_map[h] = F
                legend[h] = F
              }
            },
            '5' = {
              if(!hlinks){
                if(!chr.2D.plot[h]){
                inputData[[h]]=cbind.data.frame(inputData[[h]],
                                                rep("http://",nrow(inputData[[h]])),
                                                rep(NA,nrow(inputData[[h]])));
                colnames(inputData[[h]])=c("name","ch_name","ch_start","ch_end","data","hlink","cate")
                } else {
                  inputData[[h]]=cbind.data.frame(inputData[[h]],
                                                  rep("http://",nrow(inputData[[h]])),
                                                  inputData[[h]][,5]);
                  inputData[[h]][,5] = inputData[[h]][,3]
                  colnames(inputData[[h]])=c("name","ch_name","ch_start","ch_end","data","hlink","cate")
                  color_map = T
                  plots[h] = "2d"
                  data_based_color_map = T
                  color_scale = "linear"
                  heat_map[h] = F
                  legend[h] = F
                }
              } else {
                if(!chr.2D.plot[h]){
                inputData[[h]]=cbind.data.frame(inputData[[h]],
                                                rep(NA,nrow(inputData[[h]])),
                                                rep(NA,nrow(inputData[[h]])));
                inputData[[h]]=inputData[[h]][,c(1,2,3,4,6,5,7)]
                colnames(inputData[[h]])=c("name","ch_name","ch_start","ch_end","data","hlink","cate")
                } else {
                  inputData[[h]]=cbind.data.frame(inputData[[h]],
                                                  rep(NA,nrow(inputData[[h]])),
                                                  rep(NA,nrow(inputData[[h]])));
                  inputData[[h]]=inputData[[h]][,c(1,2,3,4,6,5,7)]
                  inputData[[h]][,5] = inputData[[h]][,3]
                  colnames(inputData[[h]])=c("name","ch_name","ch_start","ch_end","data","hlink","cate")
                  color_map = T
                  plots[h] = "2d"
                  data_based_color_map = T
                  color_scale = "linear"
                  heat_map[h] = F
                  legend[h] = F
                }
                
              }
            },
            '6' = {
              if(!hlinks){
                inputData[[h]]=cbind.data.frame(inputData[[h]],
                                                rep("http://",nrow(inputData[[h]])));
                inputData[[h]]=inputData[[h]][,c(1,2,3,4,5,7,6)]
                colnames(inputData[[h]])=c("name","ch_name","ch_start","ch_end","data","hlink","cate")
              } else {
                if(!chr.2D.plot[h]){
                inputData[[h]]=cbind.data.frame(inputData[[h]],
                                                rep(NA,nrow(inputData[[h]])));
                colnames(inputData[[h]])=c("name","ch_name","ch_start","ch_end","data","hlink","cate")
                } else {
                  inputData[[h]]=cbind.data.frame(inputData[[h]],
                                                  inputData[[h]][,5]);
                  inputData[[h]] = inputData[[h]][,3]
                  colnames(inputData[[h]])=c("name","ch_name","ch_start","ch_end","data","hlink","cate")
                  color_map = T
                  plots[h] = "2d"
                  data_based_color_map = T
                  color_scale = "linear"
                  heat_map[h] = F
                  legend[h] = F
                }
                
              }
            },
            '7' = {
              if(hlinks){
                colnames(inputData[[h]])=c("name","ch_name","ch_start","ch_end","data","hlink","cate")
              }else {stop(message("set the 'hlinks' property to TRUE."))}
            },
            {stop(message("The Input data contains insufficient columns. Please check the vignette for more detail."))}
    )
    #file select ends
```

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

### ChromoMap with Window Summary Display (n_win.factor = 1)

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Displays a window summary for the chromosome data. Uses `n_win.factor = 1` and `win.summary.display = T`.

```R
chromoMap(chr_file_1,anno_file_1,canvas_height = 200,
          id="cmapRR1",n_win.factor = 1,
          win.summary.display=T)
```

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

### chromoMap with Multiple Files and Ploidy

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Illustrates using chromoMap with multiple chromosome and annotation files, specifying ploidy and different aggregation functions for each set.

```R
#  chromoMap(c("chromosome_file_set_1.txt","chromosome_file_set_2.txt")
#            ,c("annotation_file_set_1.txt","annotation_file_set_2.txt"),
#            ploidy = 2, data_based_color_map = T,data_type = "numeric"
#            ,aggregate_func = c("avg","sum"))
```

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

### Process Genomic Loci Data in R

Source: https://rdrr.io/cran/chromoMap/src/R/chromoMap.R

Iterates through input data to assign genomic loci, extract start and end positions, and generate labels. Handles large datasets by displaying progress.

```R
cat("Number of annotations in data set ",h,":",nrow(inputData[[h]]),"\n")
assigned.loci=c()
loci.start=c()
loci.end=c()
label=c()

for(i in 1:nrow(inputData[[h]])){
  
  temp.list=mega.list.of.ranges[[h]]
  names(temp.list)=ch.name.list[[h]]
  
  temp.df=temp.list[[inputData[[h]]$ch_name[i]]]
  
  if(nrow(inputData[[h]]) >= 1000){
  message(paste0('mapping progress: ',round(i/nrow(inputData[[h]])*100,2),"%"),"\r",appendLF=FALSE)
  flush.console()
  }
  #for(j in 1:nrow(temp.df)){
    
    temp.df = temp.df[(temp.df[,1] <= abs(as.integer(inputData[[h]]$ch_start[i])) & temp.df[,2] >= abs(as.integer(inputData[[h]]$ch_start[i]))),]
    #if(abs(as.integer(inputData[[h]]$ch_start[i]))>=temp.df[j,1] & abs(as.integer(inputData[[h]]$ch_start[i]))<=temp.df[j,2]){
      
      assigned.loci[i]=paste(id,"-",inputData[[h]]$ch_name[i],"-",rownames(temp.df)[1],"-",h,sep = "")
      loci.start[i]=temp.df[1,1]
      loci.end[i]=temp.df[1,2]
      label[i]=paste(id,"-L",inputData[[h]]$ch_name[i],"-",rownames(temp.df)[1],"-",h,sep = "")
      
    #}
    
  #}
  
  
  
  
}

new.input=data.frame(assigned.loci,loci.start,loci.end)
colnames(new.input)=c("loci","loci_start","loci_end")
inputData[[h]]=cbind.data.frame(inputData[[h]][,c(1,5,6,7)],new.input)
labels.ids[[h]]=label
}

}

```

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

### Read Chromosome and Annotation Files

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Demonstrates reading the head of chromosome and annotation files using `read.table`. This is useful for inspecting the data structure before plotting.

```R
head(read.table(chr_file_1,sep = "\t"))
```

```R
head(read.table(chr_file_2,sep = "\t"))
```

```R
head(read.table(anno_file_1,sep = "\t"))
```

```R
head(read.table(anno_file_2,sep = "\t"))
```

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

### Highlight a Region

Source: https://rdrr.io/cran/chromoMap/f/inst/doc/chromoMap.Rmd

Highlights specific regions on a chromosome. This is useful for drawing attention to areas of interest without altering the main chromosome data. The `grid_array` argument specifies the start and end points for highlighting.

```R
chromoMap(chr_file_1,anno_file_2,
          data_based_color_map = T,
          data_type = "numeric",
          plots = "scatter",
          #highlighting a region on chr1
          vertical_grid = T,
          grid_array = c(550,754))
```

```R
chromoMap(chr_file_1,anno_file_2,data_based_color_map = T,data_type = "numeric",canvas_height = 300,id="new7777",lg_y = 50,
          plots = "scatter",left_margin = 60,plot_height = 40,
          vertical_grid = T,
          grid_array = c(550,754))
```

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

### High-Resolution Map with Fixed Window Size

Source: https://rdrr.io/cran/chromoMap/f/inst/doc/chromoMap.Rmd

Visualize maps with a fixed window size by setting `fixed.window = T` and specifying `window.size`. `win.summary.display=T` outputs window summary to the console.

```R
library(chromoMap)
chromoMap(chr_file_1,anno_file_1,
          fixed.window = T,
          window.size = 5,
          win.summary.display = T)
```

```R
chromoMap(chr_file_1,anno_file_1,canvas_height = 200,id="cmapRRu2",
          fixed.window = T,window.size = 5,win.summary.display = T)
```

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

### High-Resolution Map with Default Window Scaling

Source: https://rdrr.io/cran/chromoMap/f/vignettes/chromoMap.Rmd

Generate a high-resolution map using default window scaling. The 'n_win.factor' is set to 1, and 'win.summary.display' is TRUE to output window summary information to the console.

```r
# default value
library(chromoMap)
chromoMap(chr_file_1,anno_file_1,
          n_win.factor = 1,
          win.summary.display=T)
```

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

### Zoom In on a Region

Source: https://rdrr.io/cran/chromoMap/f/inst/doc/chromoMap.Rmd

Zooms into a specified region of a chromosome, updating the chromosome scale accordingly. The `region` argument takes a character vector defining the chromosome, ploidy, start, and stop of the region. Multiple regions can be specified.

```R
chromoMap(chr_file_1,anno_file_2,
          data_based_color_map = T,
          data_type = "numeric",
          plots = "scatter",
          #zoom in
          region = c("chr1:1:550:754"))
```

```R
chromoMap(chr_file_1,anno_file_2,data_based_color_map = T,data_type = "numeric",canvas_height = 300,id="new77894",lg_y = 50,
          plots = "scatter",left_margin = 60,plot_height = 40,
           region = c("chr1:1:550:754"))
```

```R
chromoMap(chr_file_1,anno_file_2,
          data_based_color_map = T,
          data_type = "numeric",
          plots = "scatter",
          #zoom in
          region = c("chr1:1:550:754","chr2:1:221:450"))
```

```R
chromoMap(chr_file_1,anno_file_2,data_based_color_map = T,data_type = "numeric",canvas_height = 300,id="newh745994",lg_y = 50,
          plots = "scatter",left_margin = 60,plot_height = 40,
           region = c("chr1:1:550:754","chr2:1:221:450"))
```

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

### ChromoMap with Links Visualization (Multiple Colors)

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Generates a ChromoMap visualization with links displayed, using multiple colors for links and adjusting legend position.

```r
chromoMap(chr_file, anno_file,
          show.links = T,
          loci_links = link_data,
          #plot adjustments
          links.colors =  c("red2","blue2"),
          ch_gap = 50,
          y_chr_scale = 100,
          top_margin = 100,
          id="cnew2",
          canvas_width = 600,
          links.lg_y = 30
          )
```

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

### Exporting chromoMap Plots

Source: https://rdrr.io/cran/chromoMap/f/vignettes/chromoMap.Rmd

Enable in-built export options by setting `export.options` to TRUE to get buttons for exporting plots as PNG or SVG. Note that these buttons may not work in RStudio's viewer pane.

```R
chromoMap("chromosome_file.txt","annotation_file.txt",
          export.options = TRUE)


```

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

### Process Chromosome Data without Centromeres

Source: https://rdrr.io/cran/chromoMap/src/R/chromoMap.R

Processes chromosome data when only chromosome name, start, and end positions are available (data_col == 3). It calculates chromosome averages and window counts. This snippet is used when centromere data is not provided.

```R
if(data_col==3){colnames(chr.data[[g]])=c("ch_name","ch_start","ch_end");cnt=FALSE} else {
      if(data_col==4){colnames(chr.data[[g]])=c("ch_name","ch_start","ch_end","cnt_start");cnt=TRUE} else{
        stop(message("The Input data contains insufficient columns. Please check the vignette for more details."))
      }
    }
    
    cat("Number of Chromosomes in set ",g,":",nrow(chr.data[[g]]),"\n")
    
    if(!fixed.window){
    switch(as.character(data_col),
           
           '4'={chr.data[[g]]=data.frame(
             name=chr.data[[g]]$ch_name,
             start=chr.data[[g]]$ch_start,
             end=chr.data[[g]]$ch_end,
             ch_average= (chr.data[[g]]$ch_end-chr.data[[g]]$ch_start+1)/ch_longest_len,
             n= round((chr.data[[g]]$ch_end-chr.data[[g]]$ch_start+1)/ch_longest_len,2)*(100*n_win.factor),
             cnt_start=chr.data[[g]]$cnt_start,
             cnt_proprtion= round(chr.data[[g]]$cnt_start/(chr.data[[g]]$ch_end-chr.data[[g]]$ch_start+1),2),
             
             p=(round(round(chr.data[[g]]$cnt_start/(chr.data[[g]]$ch_end-chr.data[[g]]$ch_start+1),2)*(round((chr.data[[g]]$ch_end-chr.data[[g]]$ch_start+1)/ch_longest_len,2)*(100*n_win.factor)))),
             q=(round((chr.data[[g]]$ch_end-chr.data[[g]]$ch_start+1)/ch_longest_len,2)*(100*n_win.factor))-(round(round(chr.data[[g]]$cnt_start/(chr.data[[g]]$ch_end-chr.data[[g]]$ch_start+1),2)*(round((chr.data[[g]]$ch_end-chr.data[[g]]$ch_start+1)/ch_longest_len,2)*(100*n_win.factor))))
             ,seq_len=chr.data[[g]]$ch_end-chr.data[[g]]$ch_start+1,stringsAsFactors = F)},
           '3'={chr.data[[g]]=data.frame(
             name=chr.data[[g]]$ch_name,
             start=chr.data[[g]]$ch_start,
             end=chr.data[[g]]$ch_end,
             ch_average= (chr.data[[g]]$ch_end-chr.data[[g]]$ch_start+1)/ch_longest_len,
             n= round((chr.data[[g]]$ch_end-chr.data[[g]]$ch_start+1)/ch_longest_len,2)*(100*n_win.factor),
             seq_len=chr.data[[g]]$ch_end-chr.data[[g]]$ch_start+1,stringsAsFactors = F);}
           
    )}
    else {
      
      switch(as.character(data_col),
             
             '4'={chr.data[[g]]=data.frame(
               name=chr.data[[g]]$ch_name,
               start=chr.data[[g]]$ch_start,
               end=chr.data[[g]]$ch_end,
               ch_average= (chr.data[[g]]$ch_end-chr.data[[g]]$ch_start+1)/ch_longest_len,
               n= ceiling((chr.data[[g]]$ch_end-chr.data[[g]]$ch_start+1)/window.size),
               cnt_start=chr.data[[g]]$cnt_start,
               cnt_proprtion= round(chr.data[[g]]$cnt_start/(chr.data[[g]]$ch_end-chr.data[[g]]$ch_start+1),2),
               
               p=ceiling(chr.data[[g]]$cnt_start/window.size),
               q=ceiling(((chr.data[[g]]$ch_end-chr.data[[g]]$ch_start+1) - chr.data[[g]]$cnt_start)/window.size)
               ,seq_len=chr.data[[g]]$ch_end-chr.data[[g]]$ch_start+1,stringsAsFactors = F)},
             '3'={chr.data[[g]]=data.frame(
               name=chr.data[[g]]$ch_name,
               start=chr.data[[g]]$ch_start,
               end=chr.data[[g]]$ch_end,
               ch_average= (chr.data[[g]]$ch_end-chr.data[[g]]$ch_start+1)/ch_longest_len,
               n= ceiling((chr.data[[g]]$ch_end-chr.data[[g]]$ch_start+1)/window.size),
               seq_len=chr.data[[g]]$ch_end-chr.data[[g]]$ch_start+1,stringsAsFactors = F);}
             
      )
    }
    
    if(data_col==4){
      max.ch.domain=rbind.data.frame(max.ch.domain,cbind.data.frame(chr.data[[g]][chr.data[[g]]$seq_len==max(chr.data[[g]]$seq_len),c(1,2,3,10)],pld=g))
      
    } else {
    if(data_col==3){
    max.ch.domain=rbind.data.frame(max.ch.domain,cbind.data.frame(chr.data[[g]][chr.data[[g]]$seq_len==max(chr.data[[g]]$seq_len),c(1,2,3,6)],pld=g))
 }}
```

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

### Display First Few Rows of Annotation Data

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Displays the first few rows of the 'anno_file_3' data frame to inspect its structure.

```r
head(anno_file_3)
```

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

### ChromoMap with Window Summary Display (n_win.factor = 3)

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Visualizes chromosome data with a window summary, using `n_win.factor = 3`. Adjusts canvas height and width.

```R
chromoMap(chr_file_1,anno_file_1,canvas_height = 200,id="cmapRR2",
          n_win.factor = 3,win.summary.display = T)
```

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

### chromoMap with Aggregation Function

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Demonstrates using chromoMap with a specified aggregation function (e.g., 'sum') for processing the data.

```R
#  chromoMap("chromosome_file.txt","annotation_file.txt",
#            data_based_color_map = T,
#            data_type = "numeric",
#            aggregate_func = "sum")
```

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

### Processing Genomic Ranges and Data

Source: https://rdrr.io/cran/chromoMap/src/R/chromoMap.R

This R code iterates through input data to map genomic ranges and associated data. It assigns data points to specific chromosomal regions and generates a unique identifier for each mapped locus, incorporating chromosome name, region index, and dataset identifier. It also handles cases where the end of a genomic feature falls within a different region than its start.

```r
cat("Number of annotations in data set ",h,":",nrow(inputData[[h]]),"\n")
    temp.input.df=c()
    
    
    for(i in 1:nrow(inputData[[h]])){
      
      temp.list=mega.list.of.ranges[[h]]
      names(temp.list)=ch.name.list[[h]]
      
      temp.df=temp.list[[inputData[[h]]$ch_name[i]]]
      
      
      
      for(j in 1:nrow(temp.df)){
        
        
        if(abs(as.integer(inputData[[h]]$ch_start[i]))>=temp.df[j,1] & abs(as.integer(inputData[[h]]$ch_start[i]))<=temp.df[j,2]){
          
          
          temp.input.df= rbind(temp.input.df,c(as.character(inputData[[h]]$name[i]),inputData[[h]]$data[i],inputData[[h]]$cate[i],paste(id,"-",inputData[[h]]$ch_name[i],"-",j,"-",h,sep = ""),as.numeric(temp.df[j,1]),as.numeric(temp.df[j,2]),inputData[[h]]$hlink[i]))
          
            
            if(abs(as.integer(inputData[[h]]$ch_end[i]))>=temp.df[j,1] & abs(as.integer(inputData[[h]]$ch_end[i]))<=temp.df[j,2]){
              
              #assigned.loci.end[i]=paste(inputData[[h]]$ch_name[i],"-",j,"-",h,sep = "")
              
              
            } else {
              
              for(t in j:nrow(temp.df)){
              
              
              if(abs(as.integer(inputData[[h]]$ch_end[i]))>=temp.df[t,1] & abs(as.integer(inputData[[h]]$ch_end[i]))<=temp.df[t,2]){
                temp.input.df= rbind(temp.input.df,c(as.character(inputData[[h]]$name[i]),inputData[[h]]$data[i],inputData[[h]]$cate[i],paste(id,"-",inputData[[h]]$ch_name[i],"-",t,"-",h,sep = ""),as.numeric(temp.df[t,1]),as.numeric(temp.df[t,2]),inputData[[h]]$hlink[i]))
                break
              } else {
                temp.input.df= rbind(temp.input.df,c(as.character(inputData[[h]]$name[i]),inputData[[h]]$data[i],inputData[[h]]$cate[i],paste(id,"-",inputData[[h]]$ch_name[i],"-",t,"-",h,sep = ""),as.numeric(temp.df[t,1]),as.numeric(temp.df[t,2]),inputData[[h]]$hlink[i])) 
              }
              }
            }
            
          
          
          
        }
        
        
      }
      
      
      
    }
    
    dfff=as.data.frame(temp.input.df,stringsAsFactors = F)
    colnames(dfff)=c("name","data","cate","loci","loci_start","loci_end","hlink")
    if(color_map){
      if(color_scale=="linear"){
    dfff[,2]=as.numeric(dfff[,2])
    } else {if(color_scale=="ordinal"){
dfff[,2]=as.character(dfff[,2])}}
    } else {dfff[,2]=rep(NA,nrow(dfff))}
    dfff[,4]=as.character(dfff[,4])
    dfff[,5]=as.integer(dfff[,5])
    dfff[,6]=as.integer(dfff[,6])
    inputData[[h]]=unique(dfff)
```

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

### ChromoMap with Fixed Window Size

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Generates a chromoMap visualization with a fixed window size for summary display. Sets `fixed.window = T` and `window.size = 5`.

```R
chromoMap(chr_file_1,anno_file_1,canvas_height = 200,id="cmapRRu2",
          fixed.window = T,window.size = 5,win.summary.display = T)
```

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

### ChromoMap Chord Visualization with Custom Plot Adjustments

Source: https://rdrr.io/cran/chromoMap/f/inst/doc/chromoMap.Rmd

Demonstrates chord visualization ('segment_annotation = T') with custom plot adjustments including 'id', 'canvas_width', and 'links.lg_y'.

```r
chromoMap(chr_file, anno_file,
          show.links = T,
          loci_links = link_data,
          segment_annotation = T,
          #plot adjustments
          links.colors =  c("orange3","purple","green2"),
          ch_gap = 50,
          y_chr_scale = 100,
          top_margin = 100,
          id="cnew4",
          canvas_width = 600,
          links.lg_y = 30
          )


```

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

### ChromoMap for Polyploidy

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Creates a chromoMap visualization for polyploid organisms by providing lists of chromosome and annotation files. Sets canvas height and a unique ID.

```R
chromoMap(c(chr_file_1,chr_file_1),c(anno_file_1,anno_file_2),
          ploidy = 2,canvas_height = 300,id="cmap2")
```

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

### High-Resolution Map with Fixed Window Size

Source: https://rdrr.io/cran/chromoMap/f/vignettes/chromoMap.Rmd

Generate a high-resolution map using a fixed window size. Set 'fixed.window' to TRUE and specify the 'window.size'. Window summary is displayed.

```r
library(chromoMap)
chromoMap(chr_file_1,anno_file_1,
          fixed.window = T,
          window.size = 5,
          win.summary.display = T)
```

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

### High-Resolution Map with Default Window Scaling

Source: https://rdrr.io/cran/chromoMap/f/inst/doc/chromoMap.Rmd

Generate a high-resolution map with default window scaling. The number of windows is proportional to the longest chromosome. `win.summary.display=T` outputs window summary to the console.

```R
# default value
library(chromoMap)
chromoMap(chr_file_1,anno_file_1,
          n_win.factor = 1,
          win.summary.display=T)
```

```R
chromoMap(chr_file_1,anno_file_1,canvas_height = 200,
          id="cmapRR1",n_win.factor = 1,
          win.summary.display=T)
```

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

### Initialize Data Structures for Ranges

Source: https://rdrr.io/cran/chromoMap/src/R/chromoMap.R

Initializes lists to store chromosome ranges, names, input data, and cluster information. This prepares the environment for creating the chromosome map visualization.

```R
##################################################
  ###########################################
  #creating the ranges for the chromosomes
  mega.list.of.ranges=list()
  ch.name.list=list()
  tempInput = list()
  clusters_info = list()
```

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

### ChromoMap with Multiple Files and Chromosome Colors

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Uses multiple chromosome and annotation files with a specified ploidy and custom colors for chromosomes.

```r
chromoMap(c("chromosome_file_set_1.txt","chromosome_file_set_2.txt")
            ,c("annotation_file_set_1.txt","annotation_file_set_2.txt"),
            ploidy = 2,
            chr_color = c("pink","blue"))
```

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

### Shiny Bindings for chromoMap

Source: https://rdrr.io/cran/chromoMap/man/chromoMap-shiny.html

Provides output and render functions for integrating chromoMap visualizations into Shiny applications and R Markdown documents. These functions allow users to display chromoMap plots interactively.

```APIDOC
## chromoMapOutput

### Description
Output function for using chromoMap within Shiny applications and interactive Rmd documents.

### Usage
```R
chromoMapOutput(outputId, width = "100%", height = "400px")
```

### Arguments
- **outputId** (string) - Required - The output variable to read from.
- **width, height** (CSS unit or number) - Optional - Must be a valid CSS unit (like '100%', '400px', 'auto') or a number, which will be coerced to a string and have 'px' appended.

## renderChromoMap

### Description
Render function for using chromoMap within Shiny applications and interactive Rmd documents.

### Usage
```R
renderChromoMap(expr, env = parent.frame(), quoted = FALSE)
```

### Arguments
- **expr** (expression) - Required - An expression that generates a chromoMap.
- **env** (environment) - Optional - The environment in which to evaluate `expr`.
- **quoted** (boolean) - Optional - Is `expr` a quoted expression (with `quote()`)? This is useful if you want to save an expression in a variable.
```

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

### Basic ChromoMap Visualization

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Generates a basic chromoMap visualization using a chromosome file and an annotation file. Sets canvas height and a unique ID for the plot.

```R
chromoMap(chr_file_1,anno_file_1,canvas_height = 200,id="cmap1")
```

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

### ChromoMap with Discrete Colormap, Labels, and Font Customization

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Creates a chromoMap visualization with a discrete colormap, labels, and customized font size and angle. Adjusts canvas height.

```R
chromoMap(chr_file_1,"data/discrete_colormap.txt",
          canvas_height = 250,id="cmap1925",labels = T,
          label_font = 10,
          label_angle = -65)
```

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

### Display First Few Rows of Link Data

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Displays the first few rows of the 'link_data' data frame to inspect its structure.

```r
head(link_data)
```

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

### Visualize Polyploid Chromosomes

Source: https://rdrr.io/cran/chromoMap/f/vignettes/chromoMap.Rmd

Use this snippet to visualize multiple sets of chromosomes, where each set is provided as a separate file. Ensure the 'ploidy' argument is set to the number of chromosome sets you are inputting.

```r
library(chromoMap)
chromoMap(c(chr_file_1,chr_file_1),c(anno_file_1,anno_file_2),
          ploidy = 2)
```

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

### Display Head of Annotation File (Positive Data)

Source: https://rdrr.io/cran/chromoMap/f/inst/doc/chromoMap.Rmd

Reads and displays the first few lines of a tab-delimited annotation file containing positive data values.

```R
head(read.table(anno_file_1,sep = "	"))
```

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

### Integrating ChromoMap in Shiny Applications

Source: https://rdrr.io/cran/chromoMap/f/inst/doc/chromoMap.Rmd

Include chromoMaps in Shiny applications using `chromoMapOutput()` in the UI and `renderChromoMap()` in the server logic.

```r
library(shiny)
library(chromoMap)

# Define UI for application that draws chromoMap
ui <- fluidPage(

   # Application title
   titlePanel("An example of chromoMap in Shiny"),

   # you can use GUI controls for your chromoMap
   sidebarLayout(
      sidebarPanel(
         #some code
      ),

      # Show a plot of the generated distribution
      mainPanel(
         chromoMapOutput("myChromoMap")
      )
   )
)

# Define server logic required to draw chromoMap
server <- function(input, output) {

   output$myChromoMap <- renderChromoMap({
     chromoMap("chromosome_file.txt","annotation_file.txt")
   })
}

# Run the application 
shinyApp(ui = ui, server = server)


```

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

### Load Alternative Link Data

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Loads an alternative set of link data from a CSV file with no header.

```r
link_data <- read.csv("data/SLINK_link2.csv",header = F)
```

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

### Display Head of Annotation File (Positive and Negative Data)

Source: https://rdrr.io/cran/chromoMap/f/inst/doc/chromoMap.Rmd

Reads and displays the first few lines of a tab-delimited annotation file containing both positive and negative data values.

```R
head(read.table(anno_file_2,sep = "	"))
```

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

### Data Preparation for chromoMap

Source: https://rdrr.io/cran/chromoMap/src/R/chromoMap.R

This R code snippet prepares input data for chromoMap by standardizing column names, handling optional hyperlink columns, and setting plotting parameters like color mapping and plot type. It ensures the data frame has the correct structure ('name', 'ch_name', 'ch_start', 'ch_end', 'data', 'hlink', 'cate').

```r
inputData[[h]]=cbind.data.frame(inputData[[h]],
                                                  rep(NA,nrow(inputData[[h]])),
                                                  rep(NA,nrow(inputData[[h]])));
                  inputData[[h]]=inputData[[h]][,c(1,2,3,4,6,5,7)]
                  inputData[[h]][,5] = inputData[[h]][,3]
                  colnames(inputData[[h]])=c("name","ch_name","ch_start","ch_end","data","hlink","cate")
                  color_map = T
                  plots[h] = "2d"
                  data_based_color_map = T
                  color_scale = "linear"
                  heat_map[h] = F
                  legend[h] = F
```

```r
inputData[[h]]=cbind.data.frame(inputData[[h]],
                                                rep("http://",nrow(inputData[[h]])));
                inputData[[h]]=inputData[[h]][,c(1,2,3,4,5,7,6)]
                colnames(inputData[[h]])=c("name","ch_name","ch_start","ch_end","data","hlink","cate")
```

```r
inputData[[h]]=cbind.data.frame(inputData[[h]],
                                                rep(NA,nrow(inputData[[h]])));
                colnames(inputData[[h]])=c("name","ch_name","ch_start","ch_end","data","hlink","cate")
```

```r
inputData[[h]]=cbind.data.frame(inputData[[h]],
                                                  inputData[[h]][,5]);
                  inputData[[h]][,5] = inputData[[h]][,3]
                  colnames(inputData[[h]])=c("name","ch_name","ch_start","ch_end","data","hlink","cate")
                  color_map = T
                  plots[h] = "2d"
                  data_based_color_map = T
                  color_scale = "linear"
                  heat_map[h] = F
                  legend[h] = F
```

```r
colnames(inputData[[h]])=c("name","ch_name","ch_start","ch_end","data","hlink","cate")
```

```r
stop(message("The Input data contains insufficient columns. Please check the vignette for more detail."))
```

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

### Basic chromoMap with Numeric Data

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Generates a basic chromoMap visualization with numeric data, enabling data-based color mapping and displaying a legend.

```R
chromoMap(chr_file_1,anno_file_2,data_based_color_map = T,data_type = "numeric",legend = T,canvas_height = 300,id="cmap4",lg_y = 50)
```

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

### Basic ChromoMap Plot with Bar and Scatter Plots

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Generates a chromoMap visualization with both bar and scatter plots. Customizes grid lines and margins.

```R
chromoMap(c(chr_file_1,chr_file_1),c(anno_file_1,anno_file_2),data_based_color_map = T,data_type = "numeric",canvas_height = 400,id="cnew4125",ploidy = 2,
          plots = c("bar","scatter"),
          plot_height = 40,
          plot_color = c("green","red"),
          ref_line = T,
          refl_pos = 20,
          vertical_grid = T,
          grid_array = c(1,54,100,420,621),left_margin = 60
          ,grid_text = c("","","mark 1","region 1"," "))
```

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

### ChromoMap with Multiple Files and Annotation Colors

Source: https://rdrr.io/cran/chromoMap/src/inst/doc/chromoMap.R

Uses multiple chromosome and annotation files with a specified ploidy and custom colors for annotations.

```r
chromoMap(c("chromosome_file_set_1.txt","chromosome_file_set_2.txt")
            ,c("annotation_file_set_1.txt","annotation_file_set_2.txt"),
            ploidy = 2,
            anno_col = c("pink","blue"))
```
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