For any question about this repo, please contact Joe Glessner ([email protected]).

We propose a deep learning approach to remove the false positive CNV calls from SNP array and sequencing CNV detection programs. This repo constains the model code and an executable script with five sample inputs. Since the pre-trained model file exceeds the upload size of Github, it can be accessed by this external link. The dataset of this project is not for public. blended_learning.py is the training script. You can feed your own dataset to train the model using blended_learning.py.

perl visualize_cnv.pl -format plot -signal 200477520001_R06C01.baflrr 200477520001_R06C01.rawcnv;
Typically the baflrr signal file has header: Name Chr Position sample.B Allele Freq sample.Log R Ratio;
--snpposfile NameChrPosition.txt can be added if only "Name sample.B Allele Freq sample.Log R Ratio" columns provided in baflrr signal files;
PennCNV-Seq can be run on sequencing BAM/CRAM to generate baflrr files;
Rawcnv input file is: chr:start-stop numsnp=1 length=1 state2,cn=1 200477520001_R06C01.baflrr startsnp=a endsnp=b;
chr:start-stop and 200477520001_R06C01.baflrr are the only critical fields to be specified, making it easily adaptable to most CNV call output formats;

The 3 .py files in the main directory and script directory are the first version script implementations (except for visualize_cnv.pl which remains constant).
Array (Initial Implementation): ./blended_learning.py and ./script/run.py
Array (Published in Briefings in Bioinformatics Implementation): ./DeepCNVv2/blend.py and ./DeepCNVv2/blend_pred.py
Sequencing (Initial Implementation): ./predict.py
Sequencing (Published in Briefings in Bioinformatics Implementation): ./DeepCNV_Seqv2/train.py and ./DeepCNV_Seqv2/predict.py

The command line arguments differ. Array has blended learning using both JPGs with manual PASS/FAIL labeling and CNV Calling Quality Control Metric Values metadata from PennCNV detect_cnv.pl log file summary lines. Sequencing does not have blended learning, just training based on only JPGs with manual PASS/FAIL labeling and prediction of additional JPGs. See the column Command (Argument Descriptions) below.

This Array with metadata (./DeepCNVv2/blend.py) and Sequencing without metadata (./DeepCNV_Seqv2/train.py ) difference is why the CNN part of the model described in the article has two convolution blocks with 64, and then two more blocks with 128 feature layers (Array with metadata) vs. the model in ./DeepCNV_Seqv2/train.py only has one convolution block of these (Sequencing without metadata).
Optionally, if PennCNV-Seq convert_map2signal.pl is used on the BAMs/CRAMs of samples you have to make BAF/LRR signal files for each sample, then PennCNV detect_cnv.pl can be run on the sequencing data to produce CNV Calling Quality Control Metric Values metadata from PennCNV detect_cnv.pl log file summary lines. Then DeepCNV blended learning could be applied to the sequencing data in a more highly similar way to array data.

The JPG output from PennCNV visualize_cnv.pl is Size 900x900. We initially scaled that down to Size 300x300 to limit computational requirements and runtime.
In Code predict.py, DeepCNV_Seqv2/train.py, and DeepCNV_Seqv2/predict.py:
target_size = (300, 300)
In Subsequent code optimization and hdf5 model file size minimization, the image dimension downsizing was no longer needed as shown in Code blended_learning.py, DeepCNVv2/blend.py, DeepCNVv2/blend_pred.py:
dim=(900,900)

1. Download the pre-trained model file from this link;
2. Download script folder;
3. Copy model file into script folder;
4. Enter script folder from Terminal;
5. Check the package requirments. Different package may generate different results;
6. Create output folder by mkdir output;
7. Run python run.py ./data/JPG ./data/samples.csv ./output ./DeepCNV.hdf5;
8. Check the results from output folder.

python 2.7.12
pandas 0.17.1
numpy 1.11.0
tensorflow 1.12.0
keras 2.2.4
cv2 2.4.9.1

wget https://repo.anaconda.com/miniconda/Miniconda2-latest-Linux-x86_64.sh 

bash Miniconda2-latest-Linux-x86_64.sh -b -p $HOME/miniconda2 

pip install pandas numpy tensorflow==1.12.0 keras==2.2.4 opencv-python

pip install pandas==0.17.1 numpy==1.11.0 tensorflow==1.12.0 keras==2.2.4 cv2==2.4.9.1