chromoMap-An R package for Interactive Genomic Visualization of Multi-Omics Data
Lakshay Anand
2021-01-12
Author: Lakshay Anand
chromoMap provides interactive, configurable and elegant graphics visualization of chromosomes or chromosomal regions allowing users to map chromosome features (like genes,SNPs etc.) onto the chromosome and visualize the feature-associated data (like multi-omics data). Each chromosome is composed of loci(representing a specific range determined based on chromosome length) that, on hover, shows details about the annotations in that locus range. The plots can be saved as HTML documents that can be shared easily. In addition, you can include them in R Markdown or in R Shiny applications.
Some of the prominent features of the package are:
- visualizing polyploidy simultaneously on the same plot.
- annotating groups of elements as distinct colors using
chromoMap-DiscreteColorMaps. - visualizing multi-omics data associated with features using a variety of plots like
chromoMap-HeatMaps(for chromsome heat-maps),chromoMap-Bar(bar plots on chromosomes),chromoMap-Scatter(scatter plot on chromosomes), andepi-tags(condition-based tagging of chromosome loci). - adjusting chromosome range or visualizing chromosome regions such as genes
- adding labels to the plot
- adding hyperlinks to each element
Getting Started
This vignette provide you with a description of how you can use the various features of chromoMap to create fantastic annotation plots and visualizing the feature-associated data. If you, however, want to know more about the applications of the plot, please check the publication or contact me. I recommend using the RStudio application since the interactive plots can be viewed beautifully in the application’s viewer pane and it allows you to export the plot either as static image or a stand-alone HTML web page.
Install chromoMap
You can install the package by just typing the following commands:
install.packages("chromoMap")
Prepare Input Data Files
The chromoMap can be used to visualize and annotate chromosomes of any living organism. It is because it renders the chromosome based on the co-ordinate information that you will provide as input. So, if you have the genomic co-ordinates of the organism, you can create chromoMaps for it.
The input data are tab-delimited text files (almost similar to the BED file format). It takes separate files for the chromosomes and the annotations. The input files should not have column headers (however, I have explained each column type below)
Chromosome Files
This file contains the co-ordinates of the chromosomes. The columns of this file(in order) are described below (all columns are mandatory unless specified optional):
- chromosome name: a character representing the chromosome/contig/region name like ‘chr1’ or ‘1’ or ‘ch1’
- chromosome start: a numeric value to specify chromosome (or chromosome region) start position. If you are considering entire chromosome this value is typically 1.
- chromsome end: a numeric value specifying chromosome/contig/region end position. Again, if you are considering entire chromosome, then this value is the length of chromosome.
- centromere start (optional): centromeres will be added automatically if you provide the its start cordinates.
I have developed algorithm that will include both start and end coordinates of chromosomes so that users can also be able to visualize a region of chromosome (not necessarily starting at 1). You can use your imagination to visualize anything that has coordinates( like RNA as well).
Your chromosome file should look like:
Annotation Files
Once you have chromosome co-ordinates in file, the next thing is to have data for annotation. annotation elements/features could be anything that has co-ordinates like genes,SNPS, etc., and associated data, like gene-expression, methylation etc. The annotation-data is also provided in the same format.
- Element Name: a character specifying (uniquely) the elements. This can be identifiers,symbols etc.
- Chromosome Name: a character specifying the chromosome name. [NOTE: the chromosome names should be consistent in chromosome and data files.]
- Element Start: A numeric specifying element start position.
- Element End: A numeric specifying element end position.
- Data(optional): A numeric or character specifying the data value.
- Hyperlinks(optional): a character specifying the URL of the element.
your annotation file should look like:
To prevent you from making some possible errors, here are a few points to care about while preparing files:
- Do not include column headers in files.
- Chromosomes names should be consistent in both files.
- Elements and chromosome names (first column of both files) should be unique.
TIP: You can use MS excel to create your files and then use save as tab-delimited option.
chromoMap files for this vignette
For the plots in this vignette, I will be using synthetic chromosome and annotation files.
# chromosome files
chr_file_1 = "chr_file_without_centromere.txt"
chr_file_2 = "chr_file_with_centromere.txt"
# annotation files
anno_file_1 = "annotation_pos.txt"
anno_file_2 = "annotation_pos_and_neg.txt"NOTE: I have used variables to assign file names. You can directly use the file names strings in the chromoMap( ) function.
Let’s have a look at the data in these files:
chromosome file:
head(read.table(chr_file_1,sep = "\t"))
#> V1 V2 V3
#> 1 chr1 1 1000
#> 2 chr2 1 700Chromosome file with centromere:
head(read.table(chr_file_2,sep = "\t"))
#> V1 V2 V3 V4
#> 1 chr1 1 1000 500
#> 2 chr2 1 700 450Annotation file with positive data.
head(read.table(anno_file_1,sep = "\t"))
#> V1 V2 V3 V4 V5
#> 1 An1 chr1 558 560 10
#> 2 An2 chr2 396 398 34
#> 3 An3 chr2 281 283 89
#> 4 An4 chr1 125 127 56
#> 5 An5 chr2 406 408 100
#> 6 An6 chr2 340 342 91Annotation file with both positive and negative data.
head(read.table(anno_file_2,sep = "\t"))
#> V1 V2 V3 V4 V5
#> 1 An1 chr1 558 560 -64
#> 2 An2 chr2 396 398 29
#> 3 An3 chr2 281 283 -137
#> 4 An4 chr1 125 127 -129
#> 5 An5 chr2 406 408 -18
#> 6 An6 chr2 340 342 149My first chromoMap
Once you have your input files ready, begin creating chromosomes plots like a pro. The simple annotation plot can be created using the following command:
library(chromoMap)
chromoMap(chr_file_1,anno_file_1)
#> ********************************** __ __ ************
#> ** __**|__ * __* __ * __ __ * __ *| | |* __ * __ **
#> **|__**| |*| *|__|*| | |*|__|*| | |*|_ |*|__|**
#> ***********************************************| **
#> *****************************************************
#> OUTPUT:
#> Number of Chromosome sets: 1
#> Number of Chromosomes in set 1 : 2
#> Processing data..
#> Number of annotations in data set 1 : 200This will create a plot with default properties.
that’s it! you have created a simple annotation plot. now hover over the annotated loci to see the magic. you should see a tooltip describing:
- the range of the selected locus in bp
- the count showing total elements mapped at this locus
- the element(s) names mapped at this locus which is clickable
If you have added hyperlinks to the elements, you can click the element labels in tooltip to access the web page.
Tool-tip Toggle: On hover, the tooltip appear on the screen as long as your pointer is over the locus. It will disappear if you move the pointer away. You can click the locus to have a stable tooltip on screen. click again on same or other locus to hide it again.
If you are not satisfied with the default look of the plot(which I’m sure you wouldn’t), you can play around with some of the properties to style your plot described under the section ‘configuring chromoMap’ in this vignette.
Now, let’s create one with centromeres.
library(chromoMap)
chromoMap(chr_file_2,anno_file_1)
#> ********************************** __ __ ************
#> ** __**|__ * __* __ * __ __ * __ *| | |* __ * __ **
#> **|__**| |*| *|__|*| | |*|__|*| | |*|_ |*|__|**
#> ***********************************************| **
#> *****************************************************
#> OUTPUT:
#> Number of Chromosome sets: 1
#> Number of Chromosomes in set 1 : 2
#> Processing data..
#> Number of annotations in data set 1 : 200Polyploidy
Biologically speaking, chromosomes occur in sets. So, just visualizing a set of chromosome(called as haploid) wouldn’t be sufficient in some scenarios. Hence, I added the feature of adding sets of chromosomes as seperate set of files. Don’t forget to set the ploidy argument to the number of sets you are passing.
library(chromoMap)
chromoMap(c(chr_file_1,chr_file_1),c(anno_file_1,anno_file_2),
ploidy = 2)
#> ********************************** __ __ ************
#> ** __**|__ * __* __ * __ __ * __ *| | |* __ * __ **
#> **|__**| |*| *|__|*| | |*|__|*| | |*|_ |*|__|**
#> ***********************************************| **
#> *****************************************************
#> OUTPUT:
#> Number of Chromosome sets: 2
#> Number of Chromosomes in set 1 : 2
#> Number of Chromosomes in set 2 : 2
#> Processing data..
#> Number of annotations in data set 1 : 200
#> Number of annotations in data set 2 : 200polyploidy turned out to be a powerful feature that can actually be used in multiple ways. The sets of chromsomes are rendered independent of each other and, hence, can differ in number and size. Using this feature you can visualize polyploid sets, haploid sets of different species on same plot, or even different samples of same species for comparison. Be creative to use this feature to your own requirement. Some interesting examples I have included in my paper.
Point and Segment-annotation plots
I have provided two types of annotation algorithms that will visualize the annotations differently. Point annotation will annotate an element on a single locus, ignoring its size. While, the segment-annotation algorithm consider the size and visualize the annotation as a segment.
The default is point-annotation. To use segment annotation set the argument segment_annotation to TRUE. Segment annotations will be advantageous in cases like displaying gene structure.
chromoMap("chromosome_file.txt","annotation_file.txt",segment_annotation = T)
here’s a hypothetical example(exon regions of the genes g1,g2 and g3).
Data-based annotation plots
Huge volume of biological data is being produced in today’s world. I thought it would be nice to visualize the data associated with the chromosome regions or elements/features. You can do this by creating data-based color annotation plots in chromoMap. Before going forward let’s know about the data types chromoMap can handle. You can use either numeric data or character/categorical data for annotations. For the type of data type you are using, you need to set the argument data_type to either numeric or categorical. Also, to use this category of plot, you need to set data_based_color_map to TRUE.Now let’s explore the various types of plots you can create using chromoMap.
chromoMap-DiscreteColorMaps
This type of plot can be used if your annotations are categorized into groups. This plot will assign distinct colors to each group. Your annotations file’s data column should have groups assigned to each element as character value.
IMPORTANT: the data_colors argument will specify the color for each group and must be passed as a list() of vectors. If the ploidy is 2, two vectors will be passed in list. Hence, you must pass a vector for each ploidy in a list.
chromoMap("chromosome_file.txt","annotation_file.txt",
data_based_color_map = T,
data_type = "categorical",
data_colors = list(c("orange","yellow")))
#> ********************************** __ __ ************
#> ** __**|__ * __* __ * __ __ * __ *| | |* __ * __ **
#> **|__**| |*| *|__|*| | |*|__|*| | |*|_ |*|__|**
#> ***********************************************| **
#> *****************************************************
#> OUTPUT:
#> Number of Chromosome sets: 1
#> Number of Chromosomes in set 1 : 2
#> Processing data..
#> Number of annotations in data set 1 : 20The best thing is, it can also create a legend for each group with labels used by you as group names. [see more under ‘legends’ section]
chromoMap-HeatMaps
chromoMap-HeatMaps are chromosome heatmaps that allow you to visualize feature associated numeric data as heat colors. In your annotations file, add numeric data in data column.
chromoMap(chr_file_1,anno_file_2,
data_based_color_map = T,
data_type = "numeric")#> ********************************** __ __ ************
#> ** __**|__ * __* __ * __ __ * __ *| | |* __ * __ **
#> **|__**| |*| *|__|*| | |*|__|*| | |*|_ |*|__|**
#> ***********************************************| **
#> *****************************************************
#> OUTPUT:
#> Number of Chromosome sets: 1
#> Number of Chromosomes in set 1 : 2
#> Processing data..
#> Number of annotations in data set 1 : 200data_colors can be used to set the heat colors also. It should be passed as a list of vector(s) with a vector for each ploidy.
Data Aggregation
Well, remember that chromosomal locus in the plot is a range, and more than one elements can be annotated in that range. So, for the data assignment of the loci where multiple elements are mapped, there is an aggregation method that allows you to control how data is aggregated/summarized for a given locus. The data for each locus will be determined by aggregate_func argument which can take avg for average (default) ,sum for summation of data values of all elements mapped on that locus, min , max, and count.
So, if you want to use the sum function:
chromoMap("chromosome_file.txt","annotation_file.txt",
data_based_color_map = T,
data_type = "numeric",
aggregate_func = "sum")You can use the different agregate functions for each ploidy by passing the argument as vector. Hence, for polyploidy, if only one value is passed, this value will be used for all sets.Otherwise, you can specify for each set as:
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"))Note: If only one element is annotated per loci, than the loci will take the element’s data value.
chromoMap-Bar Plots
In addition to visualizing numeric data as heat colors on the chromosomes, chromoMap allows creating data charts over the annotated loci. chromoMap-Bar plot can be used to visualize annotated data as barplots. You will need to use the plots argument to specify the type of data charts you wish to visualize.
chromoMap(chr_file_1,anno_file_1,
data_based_color_map = T,
data_type = "numeric",
plots = "bar")#> ********************************** __ __ ************
#> ** __**|__ * __* __ * __ __ * __ *| | |* __ * __ **
#> **|__**| |*| *|__|*| | |*|__|*| | |*|_ |*|__|**
#> ***********************************************| **
#> *****************************************************
#> OUTPUT:
#> Number of Chromosome sets: 1
#> Number of Chromosomes in set 1 : 2
#> Processing data..
#> Number of annotations in data set 1 : 200The bars represents the aggregate_function values for the loci. You can customize the chromoMap-Bar plots using various properties described under customizing Data-Plots section.
You can also turn off the heatmap feature to selectively visualize the chromoMap-Bar plots ( applicable for all of the plots discussed in the subsequent section). For that, set the heat_map argument to FALSE.
chromoMap(chr_file_1,anno_file_1,
data_based_color_map = T,
data_type = "numeric",
plots = "bar",
heat_map = F)#> ********************************** __ __ ************
#> ** __**|__ * __* __ * __ __ * __ *| | |* __ * __ **
#> **|__**| |*| *|__|*| | |*|__|*| | |*|_ |*|__|**
#> ***********************************************| **
#> *****************************************************
#> OUTPUT:
#> Number of Chromosome sets: 1
#> Number of Chromosomes in set 1 : 2
#> Processing data..
#> Number of annotations in data set 1 : 200chromoMap-Scatter Plots
chromoMap-Scatter plots allows to visualize each value for a given annotated locus as scatter plot.
chromoMap(chr_file_1,anno_file_2,
data_based_color_map = T,
data_type = "numeric",
plots = "scatter")#> ********************************** __ __ ************
#> ** __**|__ * __* __ * __ __ * __ *| | |* __ * __ **
#> **|__**| |*| *|__|*| | |*|__|*| | |*|_ |*|__|**
#> ***********************************************| **
#> *****************************************************
#> OUTPUT:
#> Number of Chromosome sets: 1
#> Number of Chromosomes in set 1 : 2
#> Processing data..
#> Number of annotations in data set 1 : 200chromoMap-Scatter plots are also interactive as the tooltips for a scatter shows the annotated element name and value.
chromoMap-Filters
This feature allows to filter numeric data based on the various mathematical conditions.
tag-filters
Tag-filters allows you to selectively tag a locus based on mathemetical condition on the data. If the condition is satisfied, the locus will be tagged. tag-filters are passed using the tag_filter argument. The argument is a list of vectors where you can pass a vector for each ploidy. the vector specifies two values necessary to pass for a filter. The first value specifies the operation-type and the second value specifies the operands. Following table describes all possible operation-types along with usage example.
In general, filters are specified as list(c("operation-type-label","operand1","operand2"))
| operation type label | description | usage example |
|---|---|---|
| eq | equal | list(c(“eq”,5)) |
| lt | less than | list(c(“lt”,-5)) |
| gt | greater than | list(c(“gt”,6)) |
| lte | less than equal | list(c(“lte”,6)) |
| gte | greater than equal | list(c(“gte”,6)) |
| gtalt | greater than AND less than | list(c(“gtalt”,5,-5)) |
| gtolt | greater than OR less than | list(c(“gtolt”,5,-5)) |
| gtealte | greater than equal AND less than equal | list(c(“gtealte”,5,-5)) |
| gteolte | greater than equal OR less than equal | list(c(“gteolte”,5,-5)) |
For instance, to tag the loci with negative number values:
chromoMap(chr_file_1,anno_file_2,
data_based_color_map = T,
data_type = "numeric",
plots = "tags",
tag_filter = list(c("lt",0)))#> ********************************** __ __ ************
#> ** __**|__ * __* __ * __ __ * __ *| | |* __ * __ **
#> **|__**| |*| *|__|*| | |*|__|*| | |*|_ |*|__|**
#> ***********************************************| **
#> *****************************************************
#> OUTPUT:
#> Number of Chromosome sets: 1
#> Number of Chromosomes in set 1 : 2
#> Processing data..
#> Number of annotations in data set 1 : 200plot-filters
plot-filters are similar to tag-filters as they allow filtering of numeric data based on mathematical conditions, but they are visualized as condition-based coloring of chromoMap-Bar and chromoMap-scatter plots. The plot_filter argument allows you to pass filter conditions. It is also a list of vector(s) similar to tag_filter above, except that you have to pass an additional necessary option of color to it. If the condition is satisfied, this color value is assigned.
So, plot-filters are specified as list(c("operation-type-label","operand1","operand2","color-value"))
Here is an example of using plot-filter on chromoMap-Bar plots. bars with value greater than and equal to are colored ‘green’.
chromoMap(chr_file_1,anno_file_1,
data_based_color_map = T,
data_type = "numeric",
plots = "bar",
plot_filter = list(c("gte",50,"green")))#> ********************************** __ __ ************
#> ** __**|__ * __* __ * __ __ * __ *| | |* __ * __ **
#> **|__**| |*| *|__|*| | |*|__|*| | |*|_ |*|__|**
#> ***********************************************| **
#> *****************************************************
#> OUTPUT:
#> Number of Chromosome sets: 1
#> Number of Chromosomes in set 1 : 2
#> Processing data..
#> Number of annotations in data set 1 : 200Similarly, it can be applied for chromoMap-Scatter plots. The following example visualizes negative values as red on the scatter plot.
chromoMap(chr_file_1,anno_file_2,
data_based_color_map = T,
data_type = "numeric",
plots = "scatter",
plot_filter = list(c("lt",0,"red")))#> ********************************** __ __ ************
#> ** __**|__ * __* __ * __ __ * __ *| | |* __ * __ **
#> **|__**| |*| *|__|*| | |*|__|*| | |*|_ |*|__|**
#> ***********************************************| **
#> *****************************************************
#> OUTPUT:
#> Number of Chromosome sets: 1
#> Number of Chromosomes in set 1 : 2
#> Processing data..
#> Number of annotations in data set 1 : 200Customizing chromoMaps
chromoMap allows various options to customize and fine-tune your plots as desirable. This section covers all the customization options available.
width and height
You can configure the dimensions(width and height) of the plot by using the parameter canvas_width and canvas_height.
chromoMap("chromosome_file.txt","annotation_file.txt",
canvas_width = 600,
canvas_height = 700)When you use multiple ploidy, the plot might go off the margins. You can adjust the width and height to fit the plot to your need.
Title
You can add a title to your plot by using title argument.
chromoMap("chromosome_file.txt","annotation_file.txt",
title = "my first chromoMap plot")You can also adjust the font-size of the title using the title_font_size argument:
chromoMap("chromosome_file.txt","annotation_file.txt",
title = "my first chromoMap plot",
title_font_size = 12)Margins
You can adjust the left and top margins through top_margin and left_margin.
chromoMap("chromosome_file.txt","annotation_file.txt",
top_margin = 25,
left_margin = 15)Chromosome colors
You can change the color of each set of chromosome by using chr_color property.
chromoMap("chromosome_file.txt","annotation_file.txt",
chr_color = c("orange"))For polyploidy, if only one color is passed it will be taken for all sets of chromsomes. Otherwise, you can assign color to each set:
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"))USEFUL TIPS :
- Use hexadecimal color codes to assign beautiful color shades to embellish your plot.
- setting the chromosome color to white will make them appear invisible hence only colored annotations will be visible(might be helpful in some case).
Annotation colors
For simple annotation plot, you can change the annotation color by using anno_col argument.
chromoMap("chromosome_file.txt","annotation_file.txt",
anno_col = c("orange"))For polyploidy, if you have passed one color value it will be taken for all the sets. Otherwise, you can pass distinct color values for each set:
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"))NOTE: For data-based annotation plots(chromoMap-DiscreteColorMaps or chromoMap-HeatMaps), colors are controlled by data_colors argument which is passed as a list of vector(s).
Chromosome width, length, and spacing
Do you think chromosomes appear too thin or too short for your annotations? well, you can adjust these parameters by using chr_width and chr_length arguments.
chromoMap("chromosome_file.txt","annotation_file.txt",
chr_width = 4,
chr_length = 5)the spacing between chromosomes can be adjusted with ch_gap argument.
chromoMap("chromosome_file.txt","annotation_file.txt",
ch_gap = 6)Chromosome curves
The curves appearing at the telo-meres (chromosome end loci) and in centromere locus can also be adjusted using the chr_curve argument.
chromoMap("chromosome_file.txt","annotation_file.txt",
chr_curve = 5)TIP: setting this property to 0 will remove the curves that render the chromosomes as rectangles.
Chromosome text
Well, the chromosome text will be taken from file you have provided. The only thing I thought might be useful is to enable or disable text individually for each ploidy. This is done by using chr_text parameter.
chromoMap("chromosome_file.txt","annotation_file.txt",
chr_text = F) For multiple ploidy pass a vector:
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_text = c(T,F))you can adjust the text font size using text_font_size parameter.
plot ID
The id argument allows to uniquely identify the plot. This feature is important when including multiple chromoMap plots in a single HTML document (like RMarkdown) as it prevent from any conflicts.
chromoMap("chromosome_file.txt","annotation_file.txt",
id="my_plot_1") Customizing Data Plots
chromoMap-Bar, chromoMap-Scatter , and epi-tag plots can be customized as desired.
Reference Lines
You can add a horizontal reference line to the data-plots. Set the ref_line argument to TRUE. The reference line is attached to the axis-scale. By default, the line is attached at the beginning of the axis(position:0). You can adjust the refl_pos argument to bring the line to desirable position.
TIP:For the line to be at the middle of axis, set refl_pos argument to exactly half of plot_height.
chromoMap(chr_file_1,anno_file_1,
data_based_color_map = T,
data_type = "numeric",
plots = "bar",
ref_line = T,
refl_pos = 15)#> ********************************** __ __ ************
#> ** __**|__ * __* __ * __ __ * __ *| | |* __ * __ **
#> **|__**| |*| *|__|*| | |*|__|*| | |*|_ |*|__|**
#> ***********************************************| **
#> *****************************************************
#> OUTPUT:
#> Number of Chromosome sets: 1
#> Number of Chromosomes in set 1 : 2
#> Processing data..
#> Number of annotations in data set 1 : 200Reference line in scatter plot:
chromoMap(chr_file_1,anno_file_2,
data_based_color_map = T,
data_type = "numeric",
plots = "scatter",
ref_line = T,
refl_pos = 20)#> ********************************** __ __ ************
#> ** __**|__ * __* __ * __ __ * __ *| | |* __ * __ **
#> **|__**| |*| *|__|*| | |*|__|*| | |*|_ |*|__|**
#> ***********************************************| **
#> *****************************************************
#> OUTPUT:
#> Number of Chromosome sets: 1
#> Number of Chromosomes in set 1 : 2
#> Processing data..
#> Number of annotations in data set 1 : 200You can change the color and stroke-size of the reference line using refl_color and refl_stroke_w arguments respectively.
Plot height
Change the plot height using plot_height parameter.
chromoMap(chr_file_1,anno_file_2,
data_based_color_map = T,
data_type = "numeric",
plots = "scatter",
plot_height = 50)Plot Colors
Change the plot colors (bar and scatter colors) using the plot_color parameter.
chromoMap(chr_file_1,anno_file_2,
data_based_color_map = T,
data_type = "numeric",
plots = "scatter",
plot_color = "orange")For the epi-tag plots, you need to use tagColor argument that will change the tag-head colors.
chromoMap(chr_file_1,anno_file_2,
data_based_color_map = T,
data_type = "numeric",
plots = "tags",
tagColor = "orange")Plot y-axis range and ticks
You can change the range of y-axis of the plots using plot_y_domain argument. It is passed as list of vector(s), a vector for each ploidy. You can also change the number of ticks of the scale using the plot_ticks argument.
chromoMap(chr_file_1,anno_file_2,
data_based_color_map = T,
data_type = "numeric",
plots = "scatter",
plot_ticks = 3,
plot_y_domain = list(c(-5,5)))Vertical Grid Lines
vertical grid lines can be used to highlight specific regions of the chromosomes. You can add any number of grid-lines to the plot. Set the vertical_grid argument to TRUE. You can add multiple grid lines by specifying a vector of positions of the lines. The following example demonstrate the use of grid lines where 5 grid lines are added at positions 0,50,100,250, and 400. The plot also demonstrate the use of other customization options.
chromoMap(c(chr_file_1,chr_file_1),c(anno_file_1,anno_file_2),
ploidy = 2
data_based_color_map = T,
data_type = "numeric",
plots = c("bar","scatter"),
plot_height = 40,
plot_color = c("green","red"),
ref_line = T,
refl_pos = 20,
vertical_grid = T,
grid_array = c(0,50,100,250,400))#> ********************************** __ __ ************
#> ** __**|__ * __* __ * __ __ * __ *| | |* __ * __ **
#> **|__**| |*| *|__|*| | |*|__|*| | |*|_ |*|__|**
#> ***********************************************| **
#> *****************************************************
#> OUTPUT:
#> Number of Chromosome sets: 2
#> Number of Chromosomes in set 1 : 2
#> Number of Chromosomes in set 2 : 2
#> Processing data..
#> Number of annotations in data set 1 : 200
#> Number of annotations in data set 2 : 200You can also change the grid lines color using the grid_color argument.
Legends
legends are provided for data-based annotation plots (chromoMap-DiscreteColorMaps and chromoMap-HeatMaps). It is hidden by default. Use legend option to enable it.
chromoMap("chromosome_file.txt","annotation_file.txt",
data_based_color_map = T,
data_type = "categorical",
legend = T) For polyploidy, you can enable or disable the legend independently for each set.
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",
legend = c(F,T))positioning legends
I know, the legends in your plot are present weirdly?. I have made the postion of legends independent of the plot and hence you can position it anywhere in the plot you want using the y and x direction length. Consider the orgin to be the bottom right corner of the plot now tweak the lg_x and/or lg_y arguments to adjust the positioning of the legend.
chromoMap("chromosome_file.txt","annotation_file.txt",
data_based_color_map = T,
data_type = "categorical",
legend = T, lg_x = 100,
lg_y = 250)Labellings
This feature I added at the end thought might be useful in some scenarios. This will show the labels (element names) on top of locus. It is disabled by default, to enable it use label argument.
chromoMap("chromosome_file.txt","annotation_file.txt",
labels=T)#> ********************************** __ __ ************
#> ** __**|__ * __* __ * __ __ * __ *| | |* __ * __ **
#> **|__**| |*| *|__|*| | |*|__|*| | |*|_ |*|__|**
#> ***********************************************| **
#> *****************************************************
#> OUTPUT:
#> Number of Chromosome sets: 1
#> Number of Chromosomes in set 1 : 2
#> Processing data..
#> Number of annotations in data set 1 : 20You can change the labels angle and font-size using label_angle and label_font arguments respectively.
chromoMap("chromosome_file.txt","annotation_file.txt",
labels=T,
label_font = 12,
label_angle = -65)#> ********************************** __ __ ************
#> ** __**|__ * __* __ * __ __ * __ *| | |* __ * __ **
#> **|__**| |*| *|__|*| | |*|__|*| | |*|_ |*|__|**
#> ***********************************************| **
#> *****************************************************
#> OUTPUT:
#> Number of Chromosome sets: 1
#> Number of Chromosomes in set 1 : 2
#> Processing data..
#> Number of annotations in data set 1 : 20TIP: To make the labels non-overlapping, tweak the chromosome length and width properties like shown in the following example.
chromoMap("chromosome_file.txt","annotation_file.txt",
labels=T,
label_angle = -65,
chr_length = 6,
chr_width = 25,
canvas_width = 800)#> ********************************** __ __ ************
#> ** __**|__ * __* __ * __ __ * __ *| | |* __ * __ **
#> **|__**| |*| *|__|*| | |*|__|*| | |*|_ |*|__|**
#> ***********************************************| **
#> *****************************************************
#> OUTPUT:
#> Number of Chromosome sets: 1
#> Number of Chromosomes in set 1 : 2
#> Processing data..
#> Number of annotations in data set 1 : 20Hyperlinks
This feature will allow you to add hyperlinks with elements. Add hyperlinks in annotations file and don’t forget to use hlinks argument to enable it.
chromoMap("chromosome_file.txt","annotation_file.txt",
hlinks=T)Exporting chromoMaps
The RStudio allows the option to export the graphics, shown in its viewer’s pane ,as either a static image or a web page. Use this feature to either save chromoMaps as static images and include them into your documents or papers, or export interactive plots as standalone-html to include them as supplementary materials in publications.
Including chromoMaps in Shiny Applications
You can include chromoMaps in Shiny application by using the function chromoMapOutput() in the UI part of the code and renderChromoMap() in the server part of the code.
Shiny Application example
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)
license
The chromoMap package is distributed under GPL-3 (GNU GENERAL PUBLIC LICENSE version 3).
Contact
You are welcome to send feedback or bug reports through e-mail lakshayanan15@gmail.com
Session Info
sessionInfo()
#> R version 4.0.3 (2020-10-10)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 19042)
#>
#> Matrix products: default
#>
#> locale:
#> [1] LC_COLLATE=C
#> [2] LC_CTYPE=English_United States.1252
#> [3] LC_MONETARY=English_United States.1252
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.1252
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] knitr_1.30 chromoMap_0.3
#>
#> loaded via a namespace (and not attached):
#> [1] htmlwidgets_1.5.3 compiler_4.0.3 magrittr_1.5 htmltools_0.5.0
#> [5] tools_4.0.3 yaml_2.2.1 stringi_1.5.3 rmarkdown_2.6
#> [9] highr_0.8 jsonlite_1.7.1 stringr_1.4.0 digest_0.6.27
#> [13] xfun_0.20 rlang_0.4.8 evaluate_0.14