Correlation heatmap¶
usage: plot_corr.py [-h] -f INPUT [-s SEP] [--skiprows SKIPROWS] [--cmap CMAP]
[-o OUTPUT] [--size SIZE] [--smart_label]
optional arguments:
-h, --help show this help message and exit
-f INPUT, --input INPUT
correlation matrix with index and header (default:
None)
-s SEP, --sep SEP this program can infer separator automatically, but it
may fail. Use auto if the input tables contain
different separators. (default: auto)
--skiprows SKIPROWS Pandas read_csv parameter to skip first N rows
(default: 0)
--cmap CMAP Pandas read_csv parameter to skip first N rows
(default: Reds)
-o OUTPUT, --output OUTPUT
output file name (default: yli11_2019-10-21)
--size SIZE Figure size, default=Ncol/4 (default: auto)
--smart_label try to infer a meaning unique group name, string will
be splited by . - |, items that occur only once or
occur above 95% will be removed (default: False)
Summary¶
Plot correlation heatmap given correlation matrix.
One usage: When using bw corr, the result figure can look bad because of large number of files (>50). In this case, you want to plot your own figures using their output.
Input¶
A correlation matrix with index name and column names.
Output¶
Usage¶
Go to your data directory and type the following.
Step 0: Load python version 2.7.13.
hpcf_interactive
module load python/2.7.13
plot_corr.py -f plotCorrelation.tab --smart_label --skiprows 1 -s "\t"
plotCorrelation.tab is an output from bw corr, the first line is notes, so we skip the first row when read the file using --skiprows 1.