1.2 Usage for SVs: diffdomains_sv.py

README

diffdomains_sv.py is the diffdomain adjusted for svs.

The main focus of this work is to address the issue of extensive matrix gaps caused by the presence of Structural Variations (SVs), which hinders the recognition of reorganized Topologically Associating Domains (TADs) by the DiffDomain method. This approach involves the interpolation of large missing blocks within the matrix due to SVs, followed by validation of the interpolated matrix.

Usage

• python diffdomains_sv.py dvsd one <chrn> <start> <end> <hic0> <hic1> [options]

• python difdddomains_sv.py dvsd multiple <hic0> <hic1> <tadlist_of_hic0.bed> [options]

options:

• --ofile filepath for output file [default: stdout]
• --oprefix prefix for output files
• --oprefixFig prefix for output figures
• --sep deliminator for hicfile [default: \t]
• --hicnorm hic matrix normalization method [default: KR]
• --chrn chromosome number [default: ALL]
• --reso resolution for hicfile [default: 100000]
• --ncore number of parallel process [default: 10]
• --min_nbin effective number of bin [default: 10]
• --f parameters for filtering the null values of the matrix[0~1) [default: 0.5]
• --filter As long as the pvalue of TADs is less than 0.05 after adjustment if argument is true [default: false]
• --svsfile is the choice to judge whether you have a file of sv,only two choice "svsfile" and "nosvsfile" [default: nosvsfile]
• --fsvs is the bed file which has four columns as :chromosome,breakpoint1,breakpoint2,type [default: None]

Note

• if you have a bed file of SVs and want to use diffdomains_sv.py to recognize reorganized TADs, you can :

  python diffdomains_sv.py dvsd one <chr> <start> <end> <hic0> <hic1> --svsfile svsfile --fsvs <file_svs>

• if you do not have a file of SVs but you know there is an SV or SVs in the region, then you can:

  python diffdomains_sv.py dvsd one <chr> <start> <end> <hic0> <hic1>

Examples

• This is an example of extensive matrix gaps caused by deletion-type SVs. After interpolation, "diffdomains_sv" is able to identify reorganized TADs resulting from the large missing gaps.

python diffdomains_sv.py dvsd one 10 103580000 103875000 \
data/GSE63525_GM12878_insitu_primary_replicate_combined.hic \ 
data/GSE63525_K562_combined.hic \
--reso 10000 \
--min_nbin 5  --f 0.1 \
--svsfile svsfile  --fsvs K562_SVs.bed

• This is an example where SVs lead to matrix gaps exceeding 80% of the matrix size. When the number of missing values in the matrix exceeds 80% of the matrix size, this method considers the corresponding TADs to have been reorganized, and directly outputs the p-value.

python diffdomains_sv.py dvsd one 12 11210000 11490000 \
data/GSM4969660_NHA-un-hic.hic \
data/GSM4969657_DIPGXIII-un-hic.hic \
--reso 10000 \
--min_nbin 5  --f 0.1
--svsfile svsfile  -fsvs DIPGXIII_SVs.bed