ENT3C 2.2.2

Compute similarity between genomic contact matrices with "Entropy 3C"

ENT3C is a method for qunatifying the similarity of micro-C/Hi-C derived chromosomal contact matrices. It is based on the von Neumann entropy¹ and recent work on entropy quantification of Pearson correlation matrices². For a contact matrix, ENT3C records the change in local pattern complexity of smaller Pearson-transformed submatrices along a matrix diagonal to generate a characteristic signal. Similarity is defined as the Pearson correlation between the respective entropy signals of two contact matrices.

https://github.com/X3N1A/ENT3C

Installation

1. generate and activate python environment

   python3.11 -m venv .ent3c_venv
   
   source .ent3c_venv/bin/activate
   

2. install ENT3C:

   pip install ENT3C
   

Usage

• CLI (python) usage:

   Usage:
   	ENT3C <command> --config=<path/to/config.json> [options]
  
   	Commands:
           get_entropy        Generates entropy output file <entropy_out_FN> .
           get_similarity           Generates similarity output file <similarity_out_FN> from <entropy_out_FN>.
           run_all            Generates <entropy_out_FN> and <similarity_out_FN>.
           compare_groups     Compare signal groups (requires --group1 and --group2 options)
  
   	Global Options:
           --config=<path>    Path to config JSON file (required for all commands)
  
   	<compare_groups> Options:
       	--group1=<GROUP>        First group name, must correspond to what comes before _BR* in config file.
       	--group2=<GROUP>        Second group name, must correspond to what comes before _BR* in config file.
  
   	Examples:
           ENT3C run_all --config=configs/myconfig.json
           ENT3C get_entropy --config=configs/myconfig.json
           ENT3C get_similarity --config=configs/myconfig.json
           ENT3C compare_groups --config=configs/myconfig.json --group1=H1-hESC --group2=K562
  

• alternatively run ENT3C in python as:

   import ENT3C
  
   ENT3C_OUT = ENT3C.run_get_entropy("config/myconfig.json")
  
   Similarity = ENT3C.run_get_similarity("config/myconfig.json")
  
   ENT3C_OUT, Similarity = ENT3C.run_all("config/myconfig.json")
  
   EUCLIDEAN = ENT3C.run_compare_groups("config/myconfig.json",group1,group2)
  
  

• all ENT3C parameters are defined in .json files config/config.json. Examples can be found in config directory.

• Paremeters defined in <config_file>:

  1. The main ENT3C parameter affecting the final entropy signal $S$ is the dimension of the submatrices SUB_M_SIZE_FIX.

     • "SUB_M_SIZE_FIX": <integer> $\dots$ fixed submatrix dimension.

       • SUB_M_SIZE_FIX can be either be fixed by or alternatively, one can specify CHRSPLIT; in this case SUB_M_SIZE_FIX will be computed internally to fit the number of desired times the contact matrix is to be paritioned into.

       PHI=1+floor((N-SUB_M_SIZE)./phi)

       where N is the size of the input contact matrix, phi is the window shift, PHI is the number of evaluated submatrices (consequently the number of data points in $S$).

     • "CHRSPLIT": <integer> $\dots$ number of submatrices into which the contact matrix is partitioned into. If specified, then "SUB_M_SIZE_FIX": null otherwise "CHRSPLIT": null.

  2. "DATA_PATH": </path/to/data> $\dots$ input data path.

  3. input files in format: [<COOL_FILENAME>, <SHORT_NAME>]

     "FILES": [
     	"ENCSR079VIJ.BioRep1.40kb.cool",
     	"G401_BR1",
     	"ENCSR079VIJ.BioRep2.40kb.cool",
     	"G401_BR2"]
     

     • Any biological replicates must be indicated in <SHORT_NAME> using the suffix "_BR%d".

     • Note: ENT3C also takes mcool files as input.

  4. "`OUT_DIR": "<desired_output_directory_name>" $\dots$ output directory. OUT_DIR will be concatenated with OUTPUT/JULIA/ or OUTPUT/MATLAB/.

  5. "OUT_PREFIX": "<desired_output_prefix_>" $\dots$ prefix for output files.

  6. "Resolution": "<integer,integer,...>" e.g. "40e3,100e3" $\dots$ resolutions to be evaluated.

  7. "ChrNr": "<integer,integer,...>" "15,16,17,18,19,20,21,22,X" $\dots$ chromosome numbers to be evaluated.

  8. "NormM": <0|1> $\dots$ input contact matrices can be balanced. If NormM: 1, balancing weights in cooler are applied. If set to 1, ENT3C expects weights to be in dataset /resolutions/<resolution>/bins/<WEIGHTS_NAME>.

  9. "WEIGHTS_NAME": "<name_of_weights>" $\dots$ name of dataset in cooler containing normalization weights.

  10. "phi": <integer> $\dots$ number of bins to the next matrix.

  11. "PHI_MAX": <integer> $\dots$ number of submatrices; i.e. number of data points in entropy signal $S$. If set, $\varphi$ is increased until $\Phi \approx \Phi_{\max}$.

Output files:

1. <OUT_DIR>/<OUTPUT_PREFIX>_ENT3C_similarity.csv $\dots$ will contain all combinations of comparisons. The second two columns contain the short names specified in FILES and the third column Q the corresponding similarity score.

   Resolution	ChrNr	Sample1	Sample2	Q
   40000	2	HFFc6_BR3	A549_BR2	0.6132789056404898
   40000	2	HFFc6_BR3	LNCap_BR2	0.3126805134567409
   40000	2	HFFc6_BR3	LNCap_BR1	0.4221187669214683
   40000	2	HFFc6_BR3	HFFc6_BR2	0.9632461160758761
   .		.	.		.	.	.		.		.		.		.
   .		.	.		.	.	.		.		.		.		.
   .		.	.		.	.	.		.		.		.		.
   

2. <OUT_DIR>/<OUTPUT_PREFIX>_ENT3C_OUT.csv $\dots$ ENT3C output table.

   Name	ChrNr	Resolution	n	PHI	phi	binNrStart	binNrEND	START	END	S
   G401_BR1	2	40000	500	918	6	0	499	0	20000000	3.7896426915562462
   G401_BR1	2	40000	500	918	6	6	505	240000	20240000	3.789044181663418
   G401_BR1	2	40000	500	918	6	12	511	480000	20480000	3.7918253959272032
   .		.	.		.	.	.		.		.		.		.
   .		.	.		.	.	.		.		.		.		.
   .		.	.		.	.	.		.		.		.		.
   

   Each row corresponds to an evaluated submatrix with fields Name (the short name specified in FILES), ChrNr, Resolution, the sub-matrix dimension sub_m_dim, PHI=1+floor((N-SUB_M_SIZE)./phi), binNrStart and binNrEnd correspond to the start and end bin of the submatrix, START and END are the corresponding genomic coordinates and S is the computed von Neumann entropy.

   • Example of output generated for ENT3C get_entropy --config=config/myconfig.json:
     • EvenChromosomes_NoWeights_40kb_ENT3C_signals.pdf
     • unbalanced 40kb contact matrices for even chromosomes across 5 cell lines. SUB_MATRIX_SIZE was 500:

3. <OUT_DIR>/<OUTPUT_PREFIX>_Eucl_<group1>vs<group2>.csv $\dots$ Euclidean distance between average z-scores of S over <group1> and <group2>: (here group1=HFFc6, group2=G401)

   Resolution	ChrNr	START	END	meanS_Euclidean
   40000	6	62360000	82360000	3.3625023926723685
   40000	6	62120000	82120000	3.3546076641065095
   40000	6	61880000	81880000	3.3441925121710026
   

   • Example of first page of output generated for ENT3C compare_groups --config=config/myconfig.json --group1 = HFFc6 group2 = "G401"
     • EvenChromosomes_NoWeights_Eucl_40kb_HFFc6vsG401.pdf