Homologous Recombination Profiler (HRProfiler) is a classification tool that predicts HRD status for both whole-genome and exome-sequenced breast and ovarian samples using HRD-specific mutation and copy number features.
# clone the development branch of the *HRProfiler* tool
git clone -b main git@github.com:AlexandrovLab/HRProfiler.git
# install the dependencies
cd HRProfiler
python3 setup.py installFrom within a python session, you can run HRProfiler as follows:
$ python3
>> from HRProfiler.scripts import HRProfiler as HR
>> HR.HRProfiler(data_matrix,genome,exome,INDELS_DIR,SNV_DIR,CNV_DIR,RESULT_DIR,cnv_file_type,bootstrap,nreplicates,normalize,hrd_prop_thresh,plot_predictions,organ)The layout of the parameters are as follows:
| Parameters | Info |
|---|---|
| data_matrix | An optional pandas dataframe with the following required feature columns (in the same order):
|
| genome | Genome build of snv and indel input files. Options include: GRCh37 and GRCh38 (default: GRCh38) |
| exome | Is the input data exome or not (default: True) |
| SNV_DIR | Directory path to snv vcf/tab-delimited files (default: None) |
| INDELS_DIR | Directory path to indel vcf/tab-delimited files. If SNV_DIR contains mutation files with both indels and SNVs, then set INDEL_DIR=None (default: None) |
| CNV_DIR | Directory path to allele-specific segmentation files (default: None) |
| RESULT_DIR | Path to the directory where HRProfiler will save the output and the log files (default: None) |
| cnv_file_type | File type for CNV files provided. Options include: 'ASCAT' , 'ASCAT-NGS', 'ABSOLUTE','BATTENBERG', 'SEQUENZA' (default: 'ASCAT') |
| bootstrap | Simulate features per sample based on the sample-weighted probability (default: False) |
| nreplicates | Number of replicates to simulate per sample (default: 20) |
| normalize | Normalize each feature column by pre-defined mean and standard deviation (default: True) |
| hrd_prop_thresh | HRD Probability threshold to classify a sample as HRD (default: 0.5) |
| plot_predictions | plot a histogram with the HRD probability values for all samples (default: True) |
| organ | Organ type for prediction. Options include 'BREAST' and 'OVARIAN' (default: 'BREAST') |
To determine HRD status for samples with snvs and indel vcf files and allele-specific copy number calls, we can run the following example code where input files have been provided for 5 breast WGS samples.
# Note: cd to parent directory that contains the HRProfiler folder before executing the command
from HRProfiler.scripts import HRProfiler as HR
HR.HRProfiler(data_matrix=None,
genome='GRCh37',
exome=False,
INDELS_DIR='./HRProfiler/example/input/indels',
SNV_DIR='./HRProfiler/example/input/mutations/',
CNV_DIR='./HRProfiler/example/input/copynumber/',
RESULT_DIR='./HRProfiler/example/output_example1/',
cnv_file_type = 'ASCAT',
bootstrap=False,
nreplicates=20,
normalize=True,
hrd_prob_thresh=0.5,
plot_predictions=True,
organ='BREAST')To determine the HRD status for samples with pre-defined HRD features: ['NCTG', 'NCGT', 'DEL_5_MH', 'LOH.1.40Mb', '3-9:HET.10.40Mb','2-4:HET.40Mb'], run the following command:
# Note: cd to parent directory that contains the HRProfiler folder before executing the command.
from HRProfiler.scripts import HRProfiler as HR
import pandas as pd
data_matrix = pd.read_csv('./HRProfiler/example/input/example_data_matrix.txt', sep="\t")
HR.HRProfiler(data_matrix=data_matrix,
genome='GRCh37',
exome=False,
INDELS_DIR=None,
SNV_DIR=None,
CNV_DIR=None,
RESULT_DIR='./HRProfiler/example/output_for_example2/',
cnv_file_type='ASCAT',
bootstrap=False,
nreplicates=20,
normalize=True,
hrd_prob_thresh=0.5,
plot_predictions=True,
organ='BREAST')HRProfiler generates a histogram with the HRD probabilities per sample and a tab-delimited table with the following columns:
| Columns | Info |
|---|---|
| samples | sample names |
| NCTG | Proportion of C:G>T:A single base substitutions at 5’-NpCpG-3’ context |
| NCGT | Proportion of C:G>G:C single base substitutions at 5’-NpCpT-3’ context |
| DEL_5_MH | Proportion or total counts of deletions spanning at least 5bp at microhomologies |
| LOH.1.40Mb | Proportion of genomic segments with loss of heterozygosity (LOH) with sizes between 1 and 40 megabases |
| 3-9:HET.10.40Mb | Proportion of heterozygous genomic segments with TCN between 2 and 4 and sizes above 40 megabases |
| 2-4:HET.40Mb | Proportion of heterozygous genomic segments with TCN between 2 and 4 and sizes above 40 megabases |
| hrd.prob | HRD Probability |
| prediction | HRD status |
Additional columns provided if bootstrap=True:
| Columns | Info |
|---|---|
| mean.hrd.prob | Average HRD probability across all replicates |
| LCI.95.hrd.prob | Lower confidence interval (2.5%) HRD Probability |
| UCI.95.hrd.prob | Upper confidence interval (97.5%) HRD Probability |
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