MGPLOT is a tool for visualizing genomic feature data. The script generates average profiles and heatmaps of selected genomic features based on a provided bedgraph file.
- Python (tested on 3.8)
- NumPy (tested on 1.22.3)
- SciPy (tested on 1.8.0)
- Matplotlib (tested on 3.5.1)
- Seaborn (tested on 0.11.2)
| Argument | Description | Default | Values |
|---|---|---|---|
-r --region_file |
File containing genomic regions | None | file name or path |
-i --signal_file |
Bedgraph file containing the signal | None | file name or path |
-c --config_file |
Configuration file | None | file name or path |
--format |
Format of the file containing regions | bed | bed , score_tsv |
--replot |
Numpy binary file containing a matrix to be replotted | None | file name or path |
Console arguments override configuration file arguments
For the configuration file to be read correctly it should have only two whitespace separated columns. In each row the first column is the argument, the second is the value.
When an argument can take multiple values such as in --omit_chr and --omit_reg the values should be in the second column separated by commas.
Example of a correctly formatted configuration file:
roi body
plot_type both
n_ticks 2
sort true
only_chr chr1,chr2,chrX
min_score 1000,
max_score 10000
--replot allows you to plot previously saved data (using --save_data) without reading other input files again, with different plot settings. When using replot the roi, body and flank arguments must remain unchanged.
| Argument | Description | Default | Values |
|---|---|---|---|
--plot_type |
Type of plot to be generated | avg_prof | avg_prof , heatmap , both |
--n_ticks |
Number of ticks on the x-axis per plot segment (left flank, right flank and body) in addition to start and end ticks | 1 | a non negative integer |
--cmap |
Colormap to be used when generating a heatmap | Reds | matplotlib colormap name |
--sort |
Sort the matrix used for generating a heatmap | False | no value in console, 'true' or 'false' in config file |
--smooth |
Smoothing factor for average profile smoothing. Set to True to automatically determine based on std | False | boolean or a non negative float |
--norm |
Type of normalization to use (linear, logarithmic or symmetric logarithmic) for the heatmap colorscale | lin | lin , log , sym_log |
--hm_title |
Title of the heatmap | None | string |
--avg_title |
Title of the average profile | None | string |
--colorbar |
Show a colorbar next to the heatmap | False | no value in console, 'true' or 'false' in config file |
When --smooth is set to true the curve is smoothed with a spline with a smoothing parameter equal to std(mean(input_array))/2.
Example average profile with --smooth set to 0.08:
The same average profile with --smooth set to 0:
Matplotlib colormap names can be found here.
--norm allows you to use a logarithmic or symmetric logarithmic normalization when linear normalization generates a poorly visible heatmap.
Example heatmap with --norm set to 'lin':
The same heatmap with --norm set to 'sym_log':
| Argument | Description | Default | Values |
|---|---|---|---|
--roi |
Region of interest to be plotted | start | start , end , body |
--flank |
Length of the flanking regions | 1000 | non-negative integer |
--body |
Length to which genomic regions will be normalized | None | positive integer or none |
The script will generate plots of the selected location with surrounding flanking segments.
When --roi is set to body the regions will be normalized with a cubic spline to have length equal to the value of --body.
If --body is set to None max(2*flank, 1000) will be used
| Argument | Description | Default | Values |
|---|---|---|---|
--out_prefix |
Prefix to be used for output files | mgplot | str |
--out_dir |
Path of the output directory | current working dir | path |
--out_format |
Format of plot output files | png | png, pdf, svg |
--save_data |
Save data for replotting (in a npy file) | False | no value in console, 'true' or 'false' in config file |
The --save_data argument allows you to save a matrix for replotting data without repeating data extraction. The matrix is saved in a numpy binary file.
| Argument | Description | Default | Values |
|---|---|---|---|
--omit_reg |
Genomic regions to be omitted | None | region names |
--omit_chr |
Chromosomes to be omitted | None | chromosome names |
--only_chr |
Chromosomes to be considered exclusively | None | chromosome names |
--min_score |
Lower limit (inclusive) of score values of which features will be considered | None | float |
--max_score |
Upper limit (inclusive) of score values of which features will be considered | None | float |
--only_first |
Use only the first occurrence of regions with the same name | False | no value in console, 'true' or 'false' in config file |
--n_best |
Number of regions with the best scores to be considered | None | positive integer or none |
The --n_best argument selects genes after chromosomes and genes are excluded.
The configuration file (example1.cfg):
region_file RNAseq_counts.tsv
signal_file H3K4me3.bedgraph
format score_tsv
flank 2000
region start
n_ticks 2
plot_type both
smooth true
save_data true
norm log
sort false
n_best 2000
Running the script will generate an average profile and heatmap of H3K4me3 enrichment around the transcription start site on the first 2000 most expressed genes in the tsv file. The matrix will be saved for replotting as mgplot_data.npy.
python make_plots.py -c example1.cfg
The result:
Replotting the heatmap with --sort set to True:
python make_plots.py -c example1.cfg --replot mgplot_data.npy --sort
