ddbois/snapfish

SnapFISH: a computational pipeline to identify chromatin loops from multiplexed DNA FISH data

Find the preprint of SnapFISH here.

Installation

Recommended Way

SnapFISH is a published package on PyPI and can be downloaded by

pip install SnapFISH

It is recommended to create a new conda or Python virtual environment before installing SnapFISH. This can be done by

conda create --name SnapFISH_env python==3.6.8
conda activate SnapFISH_env
pip install SnapFISH

Alternative Way

Alternatively, you can download the source code from this GitHub page and run it using the Shellscript provided:

git clone https://github.com/lindsayhrlee/SnapFISH SnapFISH && cd SnapFISH
pip install -r requirements.txt

Program Description

The SnapFISH algorithm consists of four steps:

• Step 1a: Take 3D coordinates of each locus in each cell as input, calculate Eucildean disance between any two loci pairs, and also calculate average Euclidean disance for each loci pair across all available cells to create distance matrix
• Step 1b: Make heatmap of average Euclidean distance and contact frequencies
• Step 2: Perform two sample T-test or Wilcoxon rank test for each loci pair based on local background model
• Step 3a: Identify loop candidates
• Step 3b: Make heatmap of loop candidates
• Step 4a: Group nearby loop candidates into clusters. remove singletons, only keep summits
• Step 4b: Make heatmap of loop summits

Required Input

SnapFISH requires two text files described below as an input. The example of input files can be found in the ext folder.

1. 3D_COOR.txt : the txt file containing 3D coordinates information as the following columns (separated by tab)

• Col #1 chr: chromosome number
• Col #2 cell_id: unique ID for each cell
• Col #3 pos: locus ID
• Col #4~#6 x, y, z: 3D coordinates (X,Y,Z) for each bin, unit: nm.

3. ANN.txt : the 1D genomic annotation file containing the genomic location for each bin

• Col #1 chr: chromosome number
• Col #2 start: the starting 1D genomic location for the corresponding bin specified by Col #4
• Col #3 end: the ending 1D genomic location for the corresponding bin specified by Col #4
• Col #4 pos: the unique ID for each bin

Demo of SnapFISH

Dataset Used for GitHub Demo

The dataset for the demo is a chromatin tracing data from Huang et al, NG, 2021 (PMID: 34002095) of the 5Kb bin resolution where 41 loci cover a 205Kb region in mESC Sox2 locus - chr3:34,601,078–34,806,078 (ref: mm10). This dataset contains two alleles: CAST and 129. The CAST allele contains a 7.5Kb 4CBS insertion (data not used in this GitHub demo), while the 129 allele does not contain this insertion (data used in this GitHub demo). On average, each allele contains 28 5Kb bins.

SnapFISH As a Command Line Tool

You can use SnapFISH as a command line tool after activating the conda environment and installing SnapFISH by

pip install SnapFISH

Identification of Enhancer-Promoter Loops

To call enhancer-promoter loops, type the following in your terminal:

SnapFISH call-loops -i PATH/TO/3DCOOR.txt -o OUT/DIR -a PATH/TO/ANN.txt -p 1 -d SUFFIX

where

1. -i PATH/TO/3DCOOR.txt : The txt file containing the following columns (separated by tab) i

2. -o OUT/DIR : The directory for output

3. -a PATH/TO/ANN.txt : The 1D genomic annotation file containing the genomic location for each bin

4. -p 1 : 0 or 1; 1 to save the heatmaps produced in steps 1b, 3b, and 4b, and 0 to not.

5. -d SUFFIX : The name of data for suffix of the output files

For Windows, the file path needs to be separated by \\ instead of /.

For example, running

SnapFISH call-loops -i ext/129_3d_coor.txt -o tmp -a ext/input_ann.txt -p 1 -d 129

will print

129chr3 cutoff: 349.4659
Pearson's r (average.dist and contact.freq): -0.9845

The cutoff is the average Euclidean distance in nm between two bin pairs 25Kb apart. The Pearson's r value is the correlation between the average distance matrix and the contact frequency matrix. The following files will also be produced:

1. tmp/output_3D_dist_129chr3.txt:

   • The pairwise distance between each bin in each cell. Missing values are skipped.

2. tmp/output_3D_dist_129chr3_avg.txt:

   • The average pairwise distance between each bin across all cells.

3. tmp/heatmap_av_Euc_dist_129chr3.pdf, tmp/heatmap_Contact_freq_129chr3.pdf:

   • The heatmaps of average pairwise distance and contact frequency.

4. tmp/output_test_129.txt:

   • T-test and Wilcoxon rank test results from the local background model.

5. tmp/output_loop_candidate_129.txt, tmp/heatmap_loop_candidates_129chr3.pdf:

   • The loop candidates are selected, and the heatmap showing their positions.

6. tmp/output_loop_summit_129.txt, tmp/heatmap_loop_summits_129chr3.pdf:

   • The loop summits are identified, and the heatmap showing their positions.

Pairwise Distances

In some cases, users might only be interested in getting the pairwise distances between each bin without calling loops. This can be done by

SnapFISH step1 -i PATH/TO/3DCOOR.txt -o OUT/DIR -a PATH/TO/ANN.txt -p 1 -d SUFFIX

The only output will be pairwise distances and the two associated heatmaps.

Wilcoxon Rank Test

By default, SnapFISH identifies loop candidates by the T-test. However, if the underlying distribution deviates from the normal distribution significantly, users can also filter loops by the Wilcoxon rank test. After running the call-loops command, the user can adjust the filtering criteria by

SnapFISH test -i OUT/DIR/output_test_SUF.txt -o OUT/DIR -a PATH/TO/ANN.txt -p 1 -d SUFFIX -m w -h 0.1

where

1. -i OUT/DIR/output_test_SUF.txt: The output from step 2.

2. -m w: If w, will use the FDR (False Discovery Rate) from the Wilcoxon rank tests; If t, will use the FDR from the T-tests. Default setting is t.

3. -h 0.1: The filtering threshold of the FDR. Only bin pairs with the FDR less than the threshold will be kept. Default setting is 0.1.

For example,

SnapFISH -i tmp/output_test_129.txt -o tmp -a ext/input_ann.txt -p 1 -d 129 -m w -h 0.05

will only keep the bin pairs with the FDR less than 0.05 from the Wilcoxon rank test.

Run SnapFISH with Shellscript

Although, it is recommended to install SnapFISH via Pip, it is possible to run SnapFISH with the Shellscript. Simply download the dependencies, the files in this GitHub page, and run the Shellscript run_SnapFISH.sh.

Dependencies

SnapFISH was built using following Python packages.

1. Python 3.6.8
2. numpy 1.19.5
3. pandas 1.1.5
4. scipy 1.5.4
5. statsmodels 0.12.2
6. matplotlib 3.3.4
7. click 7.1.2

Input

The required input variables of the Shellscript are:

1. SnapFISH_dir : The directory of SnapFISH
2. input_path : The path of the file containing 3D coordinates
3. output_dir : The directory for output
4. ann_file : The path of the file containing genomic location for each bin
5. save_pic : 1 for outputting heatmaps, and 0 for not
6. data_name : Name of data for the output file suffix

To run SnapFISH,

./run_SnapFISH.sh

If you are using Windows, please change / to \\.

Contact Us

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