pyranges-plot 0.0.17

'Gene visualization package for dataframe objects generated with PyRanges.'

pyranges_plot

Gene visualization package for dataframe objects generated with PyRanges.

Overview

The goal is getting a plot displaying a series of genes contained in a dataframe from a PyRanges object. It displays the genes' intron-exon structure in its corresponding chromosome.

There are some features to be defined by the user, one is the plot's engine since it can be based on Matplotlib or Plotly, the other is the name of the gene ID column in the data. The rest of features can either be left as default or be customized. In example, the plot shows the first 25 genes of the dataframe by default, but this can be modified. It is worth noting that the order of the genes will be conserved.

In the case of coloring, Pyranges Plot offers a wide versatility. The data feature (column) according to which the genes will be colored is by default the gene ID, but this "color column" can be selected manually. Color specifications can be left as the default colormap (plotly.colors.qualitative.Alphabet) or be provided as dictionaries, lists or color objects from either Matplotlib or Plotly regardless of the chosen engine. When a colormap or list of colors is specified, the colors assigned to the genes will iterate over the provided ones following the color column pattern. In the case of concrete color instructions such as dictionary, the genes will be colored according to it while the non-specified ones will be colored in black.

Installation

PyRanges-Plot can be installed using pip:

pip install pyranges-plot

Examples

Next we will test pyranges_plot visualization options using the plot function. For that we will be using a PyRanges object generated from a dictionary.

import pyranges as pr
import pyranges_plot as prp

p = pr.from_dict({"Chromosome": [1, 1, 2, 2, 2, 2, 2, 3],
                  "Strand": ["+", "+", "-", "-", "+", "+", "+", "+"],
                  "Start": [1, 40, 10, 70, 85, 110, 150, 140],
                  "End": [11, 60, 25, 80, 100, 115, 180, 152], 
                  "transcript_id":["t1", "t1", "t2", "t2", "t3", "t3", "t3", "t4"], 
                  "feature1": ["a", "a", "b", "b", "c", "c", "c", "d"], 
                  "feature2": ["A", "A", "B", "B", "C", "C", "C", "D"]})
print(p)

+--------------+--------------+-----------+-----------+-----------------+------------+------------+
|   Chromosome | Strand       |     Start |       End | transcript_id   | feature1   | feature2   |
|   (category) | (category)   |   (int64) |   (int64) | (object)        | (object)   | (object)   |
|--------------+--------------+-----------+-----------+-----------------+------------+------------|
|            1 | +            |         1 |        11 | t1              | a          | A          |
|            1 | +            |        40 |        60 | t1              | a          | A          |
|            2 | +            |        85 |       100 | t3              | c          | C          |
|            2 | +            |       110 |       115 | t3              | c          | C          |
|            2 | +            |       150 |       180 | t3              | c          | C          |
|            2 | -            |        10 |        25 | t2              | b          | B          |
|            2 | -            |        70 |        80 | t2              | b          | B          |
|            3 | +            |       140 |       152 | t4              | d          | D          |
+--------------+--------------+-----------+-----------+-----------------+------------+------------+
Stranded PyRanges object has 8 rows and 7 columns from 3 chromosomes.
For printing, the PyRanges was sorted on Chromosome and Strand.

The generated data is a stranded PyRanges object containing 4 genes in 3 chromosomes as shown above. Having this example data stored in the variable p, we are able to start exploring Pyranges Plot options. We can get a plot in a single line:

prp.plot(p, engine="plt", id_col="transcript_id")

The output is an interactive Matplotlib plot. To obtain it we just need to provide the data, the engine and the name of the id column. However, the engine and the id column can be set previously so there is no need to specify them anymore while plotting:

# For engine use 'plotly' or 'ply' for Plotly plots and 'matplotlib' or 'plt' for Matplotlib plots
prp.set_engine('plotly')
prp.set_idcol('transcript_id')

Since the data has only 4 genes all of them are plotted, but the function has a default limit of 25, so in a case where the data contains more genes it will only show the top 25, unless the max_ngenes parameter is specified. For example, we can set the maximum number of genes as 2. Note that in the case of plotting more than 25 a warning about the plot's integrity will appear.

prp.plot(p, max_ngenes=2)

Now the plot is based on Plotly because we set it as the engine, though it looks the same as the Matplotlib one. Also, both libraries offer interactive zoom options. For Matplotlib…

and for Plotly.

Another pyranges_plot functionality is allowing to define the plots' coordinate limits through the limits parameter. The default limits show some space between the first and last plotted exons of each chromosome, but these can be customized. The user can decide to change all or some of the coordinate limits leaving the rest as default if desired. The limits can be provided as a dictionary, tuple or PyRanges object:

• Dictionary where the keys should be the data's chromosome names in string format and the values can be either None or a tuple indicating the limits. When a chromosome is not specified in the dictionary or it is assigned None the coordinates will appear as default.
• Tuple option sets the limits of all plotted chromosomes as specified.
• PyRanges object can also be used to define limits, allowing the visualization of one object's genes in another object's range window.

prp.plot(p, limits={"1": (None, 100), "2": (60, 200), "3": None})
prp.plot(p, limits=(0,300))

We can try to color the genes according to the strand column instead of the ID (default). For that the color_col parameter should be used.

prp.plot(p, color_col="Strand")

This way we see the "+" strand genes in one color and the "-" in another color. Additionally, these colors can be customized through the colormap parameter. For this case we can specify it as a dictionary in the following way:

prp.plot(
    p,
    color_col="Strand",
    colormap={"+": "green", "-": "red"}
)

The parameter colormap is very versatile because it accepts dictionaries for specific coloring, but also Matplotlib and Plotly color objects such as colormaps (or even just the string name of these objects) as well as lists of colors. For example, we can use the Dark2 Matplotlib colormap, even if the plot is based on Plotly:

prp.plot(p, colormap="Dark2")

The disposition of the genes is by default a packed disposition, so the genes are preferentially placed one beside the other preferentially. But this disposition can be displayed as 'full' if the user wants to display each gene under the other by setting the packed parameter as False.

prp.plot(p, packed=False)

In interactive plots there is the option of showing information about the gene when the mouse is placed over its structure. This information always shows the gene's strand if it exists, the start and end coordinates and the ID. To add information contained in other dataframe columns to the tooltip, a string should be given to the showinfo parameter. This string must contain the desired column names within curly brackets as shown in the example. Similarly, the title of the chromosome plots can be customized giving the desired string to the chr_string parameter, where the correspondent chromosome value of the data is referred to as {chrom}. An example could be the following:

prp.plot(
    p, 
    showinfo="first feature: {feature1}\nsecond feature: {feature2}",
    chr_string = 'Chr: {chrom}'
)

Pyranges Plot also offers the possibility to add a legend by setting the legend parameter as True.

Another interesting feature is showing the transcript structure, so the exons appear as wider rectangles than UTR regions. For that the proper information should be stored in the "Feature" column of the data. A usage example is:

pp = pr.from_dict({
	"Chromosome": [1, 1, 2, 2, 2, 2, 2, 3, 4, 4, 4, 4, 4, 4],
	"Strand": ["+", "+", "-", "-", "+", "+", "+", "+", "-", "-", "-", "-", "+", "+"],
	"Start": [1, 40, 10, 70, 85, 110, 150, 140, 30100, 30150, 30500, 30647, 29850, 29970],
	"End": [11, 60, 25, 80, 100, 115, 180, 152, 30300, 30300, 30700, 30700, 29900, 30000],
	"transcript_id":["t1", "t1", "t2", "t2", "t3", "t3", "t3", "t4", "t5", "t5", "t5", "t5", "t6", "t6"],
	"feature1": ["1", "1", "1", "1", "1", "2", "2", "2", "2", "2", "2", "2", "2", "2"],
	"feature2": ["A", "A", "B", "B", "C", "C", "C", "D", "E", "E", "E", "E", "F", "F"],
	"Feature": ["exon", "exon", "CDS", "CDS", "CDS", "CDS", "CDS", "exon", "exon", "CDS", "CDS", "exon", None, None]
    
})

prp.plot(pp, transcript_str = True)

Lastly, some features of the plot appearance can also be customized. The background, plot border or title default colors can be checked in the following way:

# Check the default values
prp.print_default()

+-----------------+-----------+----------+------------------------------------------------------------------------+
|     Feature     |   Value   | Modified |                              Description                               |
+-----------------+-----------+----------+------------------------------------------------------------------------+
| tag_background  |   grey    |          | Background color of the tooltip annotation for the gene in Matplotlib. |
| plot_background |   white   |          | Background color for the chromosomes plots.                            |
|   plot_border   |   black   |          | Color of the line defining the chromosome plots.                       |
|   title_size    |    18     |          | Size of the plots' titles.                                             |
|   title_color   | goldenrod |          | Color of the plots' titles.                                            |
|   exon_width    |    0.4    |          | Height of the exon rectangle in the plot.                              |
+-----------------+-----------+----------+------------------------------------------------------------------------+

The way to change the default features is using the set_default function. An example is shown below.

# Change the default values
prp.set_default('plot_background', 'rgb(173, 216, 230)')
prp.set_default('plot_border', '#808080')
prp.set_default('title_color', 'magenta')

# Make the customized plot
prp.plot(p)

Now the modified values will be marked when checking the default values:

prp.print_default()

+-----------------+--------------------+----------+------------------------------------------------------------------------+
|     Feature     |       Value        | Modified |                              Description                               |
+-----------------+--------------------+----------+------------------------------------------------------------------------+
| tag_background  |        grey        |          | Background color of the tooltip annotation for the gene in Matplotlib. |
| plot_background | rgb(173, 216, 230) |    *     | Background color for the chromosomes plots.                            |
|   plot_border   |      #808080       |    *     | Color of the line defining the chromosome plots.                       |
|   title_size    |         18         |          | Size of the plots' titles.                                             |
|   title_color   |      magenta       |    *     | Color of the plots' titles.                                            |
|   exon_width    |        0.4         |          | Height of the exon rectangle in the plot.                              |
+-----------------+--------------------+----------+------------------------------------------------------------------------+

To return to the original appearance of the plot, the reset_default function can restore all or some parameters. By default, it will reset all the features, but it also accepts a string for resetting a single feature or a list of strings to reset a few.

prp.reset_default()  # reset all
prp.reset_default('plot_background')  # reset one feature
prp.reset_default(['plot_border', 'title_color'])  # reset a few features

Once we are able to get the plot we want, it can be exported to pdf or png format using the to\_file parameter. This parameter takes a string with the name or path of the file including its extension. Additionally, the size can be customized through the file_size parameter by providing a tuple containing the height and width values.

# Build the plot and save it in pdf or png
prp.plot(p, to_file='my_plot.pdf', file_size=(1300, 600))

# An example of some pyranges adjustments and save
p_subset = p[p.transcript_id.isin(['t3', 't4'])]
prp.plot(p_subset, colormap='Set3', to_file='t3_t4_plot.png')

Coming soon

• Option to turn off introns.
• Accept different PyRanges objects or DataFrames as input for the same plot.
• Bases will be displayed along coordinates.
• Colorblind friendly.