SnapHiC-D 0.1.0

A computational pipeline to identify differential chromatin contacts from single cell Hi-C data

SnapHiC-D

Identifying differential chromatin interactions from single cell Hi-C data

Find the preprint here.

SnapHiC-D is an extension of SnapHiC and requires SnapHiC's RWR step output for its input. For a faster version, use SnapHiC2, which is enabled by selecting "method="sliding_window".

Install SnapHiC-D

Install SnapHiC-D through pip:

conda create --name SnapHiC_D_env python==3.6.8
conda activate SnapHiC_D_env
pip install SnapHiC-D

Requirements

SnapHiC-D was built using following Python packages.

1. Python 3.6.8
2. numpy 1.19.0
3. pandas 1.1.5
4. qnorm 0.8.1 (https://github.com/Maarten-vd-Sande/qnorm)
5. scipy 1.5.4
6. statsmodels 0.12.2
7. futures 3.0.5
8. click 7.1.2

Running SnapHiC-D

Activate the python environment with SnapHiC-D installed and enter the following in the terminal:

SnapHiC-D diff-loops -i group_A_dir -j group_B_dir -o out_dir -c chr -n num_CPUs\
                     -b genome_region_path -g genome_transcript_path\
                     --binsize bin_size --fdr_threshold fdr_threshold\
                     --mini_gap min_gap --maxi_gap max_gap

The required inputs variables are:

1. group_A_dir : The directory of files for group A
2. group_B_dir : The directory of files for group B
3. out_dir : The output directory
4. chr : chromosome number (i.e. chr3)
5. num_CPUs : The number of CPUs one would like to use. One can check how many CPUs are available by "lscpu". If num_CPUs = 1, the program will run as a single processor. When using a HPC with job scheduler, make sure to ask for 1 node.
6. genome_region_path: the path of mm10_filter_regions.txt or hg19_filter_regions.txt, depending on the reference genome. These files are provided in the ext folder.
7. genome_transcript_path: the path of mm10.refGene.transcript.TSS.061421.txt or hg19.refGene.transcript.TSS.061421.txt, depending on the reference genome. These files are provided in the ext folder.
8. bin_size : The resolution of bin size
9. fdr_threshold : FDR threshhold; the default value is 0.1
10. min_gap : The minimum distance gap; the default value is 2 (2kb)
11. max_gap : The maximum distance gap; the default value is 101 (1MB)

We have provided input example data of 94 mouse embryonic stem cells (mESC) and 188 mouse neuron progenitor cells (NPCs) in zipped folders to test SnapHiC-D. These are the trimmed RWR results from SnapHiC around the 200Kb region of Sox2 locus - chr3:34,601,000–34,806,000 (ref: mm10). To run SnapHiC-D, type

SnapHiC-D diff-loops -i group_A_dir -j group_B_dir -o output -c chr3 -n 2\
                     -b "ext/mm10_filter_regions.txt"\
                     -g "ext/mm10.refGene.transcript.TSS.061421.txt"

A directory named output will be created with the following files inside:

1. output/combined_results_chr3.txt: T-test results of bin pairs.
2. output/DI_FDR0.1_T2_Test_chr3.txt: filtered results based on FDR and the T statistic.

Contact Us

For any questions regarding this software, contact Ming Hu ([e-mail]), Lindsay Lee ([e-mail]), or Hongyu Yu ([e-mail]).