CD-Loop: A chromatin loop detection method based on the diffusion model
git clone https://github.com/wangyang199897/CD-Loop.git
pytorch
numpy
PIL
sklearn
pandas
juicer_tools
juicer_tools dump observed KR [.hic file] [chromosome] [chromosome] BP 5000 output [output file path]
juicer_tools dump norm KR [.hic file] [chromosome] [chromosome] BP 5000 [output file path]
python CD_Loop.py -inf [Interaction frequency file path] -nof [norm factor file path] -chr [chromosome name(only one)] -lcps [lower center point scope def 30000] -hcps [high center point scope def 3000000] -outpath [output directory]
[Interaction frequency file path] The Interaction frequency file generate with juicer_tools
[norm factor file path] The norm factor file generate with juicer_tools
[chromosome name(only one)] The chromosome which you want to predict
[lower center point scope def 30000] Predict the left endpoint of the range of interaction points, default 30000
[high center point scope def 3000000] Predict the right endpoint of the range of interaction points, default 3000000
[output directory] Output file saving path
There are seven subfolders in the output folder
/bigmatrix Convert interaction frequencies to a large matrix
/interactionPointInformation Interaction point information within a limited range
/deleteInteractionPointInformation Cleaned interaction point information
/subMatrix Input sub-matrix required by the model
/deleteSubMatrix Cleaned submatrix
/beforeClustering Files to be clustered
/result final output result