MINTsC learns multi-way chromatin interactions from single cell high throughput chromatin conformation data.
Paper
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The authors' code
R · 188 lines · 5.3 KB · no license
- options(scipen = 100, digits = 4)
- pacman::p_load(purrr, furrr, parallel, data.table, dplyr,stringr,gtools,igraph)
- find_cliques=function(binsize=500000,
- data_dir='/storage10/kwangmoon/MINTsC/data/Ramani2017/',
- output_dir='/storage10/kwangmoon/MINTsC/results/Ramani2017/',
- corenum_celltype=1,
- corenum_cells=20,
- chrnum=23,
- sizefile='hg19.chrom.sizes',
- ncellsthreshold_c0=1,ncellsthreshold_c1=0,Smax=6,
- chrlist=NULL){
- if(is.null(chrlist)){chrlist=c(paste0("chr",c(1:(chrnum-1),"X")))}
- setwd(data_dir)
- cell_type=qs::qread("cell_type.qs")
- ctlists=unique(cell_type[,2])
- future::plan(multicore, workers = corenum_celltype)
- future_map(ctlists,function(ct){
- hic_df_ct=lapply(1:chrnum,function(x)qs::qread(paste0(chrlist[x],'/hic_df_',chrlist[x],'_',ct,'.qs'))[,c('cell','chr','binA','binB')] )
- hic_df_ct=do.call('rbind',hic_df_ct)
- hic_df_ct=split(hic_df_ct,by='cell')
- future::plan(multicore, workers = corenum_cells)
- system(paste0('mkdir ',output_dir,"/","'",ct,"'"))
- future_map(hic_df_ct,function(tmp){
- if(!paste0("cell_clique_",tmp$cell[1],'.qs')%in%list.files(paste0(output_dir,"/",ct))){
- nodes<-c(paste0(tmp$chr,"_",tmp$binA),paste0(tmp$chr,"_",tmp$binB)) %>% unique
- vertices=data.frame(nodes)
- edges <-tmp[,.('from'=paste0(chr,"_",binA),'to'=paste0(chr,"_",binB))]
- g <- igraph::graph_from_data_frame(edges, directed=FALSE, vertices=nodes)
- three_clique<-igraph::cliques(g,min=3,max=Smax)
- tmpres=unlist(lapply(three_clique,function(x)paste(mixedsort(names(x)),collapse="-")))
- qs::qsave(tmpres, paste0(output_dir,"/",ct,"/cell_clique_",tmp$cell[1],'.qs'))
- }
- })
- #If you do setwd within lapply, it will globally setwd as well. be careful.
- cliques_loop=lapply(list.files(paste0(output_dir,"/",ct)),function(x){qs::qread(paste0(output_dir,"/",ct,"/",x))})
- rm(hic_df_ct)
- gc()
- qs::qsave(cliques_loop,paste0(output_dir,"/cliques_loop_3tomax_",ct,".qs"))
- print(paste0(ct," done"))
- }
- )
- cat('Clique finding for each cell coimplete...')
- cat("\n")
- cliques_loop_ct=list()
- for(ct in ctlists){
- cliques_loop_ct[[ct]]=qs::qread(paste0(output_dir,"/cliques_loop_3tomax_",ct,".qs"))
- }
- qs::qsave(cliques_loop_ct,paste0(output_dir,"/cliques_loop_3tomax.qs"))
- cat('Saving cliques')
- cat("\n")
- Q=unlist(cliques_loop_ct)
- qs::qsave(Q,paste0(output_dir,"/Q_notunique.qs"))
- tableQ=Q %>% table
- print('Sorting cluques and generating pairwise interactions within each clique...')
- cat("\n")
- Q_unique=names(tableQ)
- cliquesize<-sapply(strsplit(Q_unique, "-"), length)
- chrlabel=word(Q_unique,1,sep="_")
- Smax=max(cliquesize)
- options(future.globals.maxSize= Inf)
- future::plan(multicore, workers = length(3:Smax))
- Qdat=data.table(Q_unique,chrlabel,cliquesize,ncell=as.numeric(tableQ ))
- rm(Q_unique)
- rm(chrlabel)
- rm(tableQ)
- rm(cliquesize)
- gc()
- qs::qsave(Qdat,
- paste0(output_dir,'/Q_summary.qs'))
- qs::qsave(lapply(chrlist,function(x){Qdat[cliquesize==3&chrlabel==x&ncell>ncellsthreshold_c0]$Q_unique}),
- paste0(output_dir,'/Q3_filtered_list.qs'))
- future_map(4:Smax,function(S){
- qs::qsave(lapply(chrlist,function(x){Qdat[cliquesize==S&chrlabel==x&ncell>ncellsthreshold_c1]$Q_unique}),
- paste0(output_dir,'/Q',S,'_filtered_list.qs'))
- }
- )
- # Q3=unique(Q[Q%in%Q_unique[cliquesize==3&tableQ>ncellsthreshold]])
- # chrlabel_3=word(Q3,1,sep="_")
- # qs::qsave(lapply(chrlist,function(x){Q3[chrlabel_3==x]}),
- # paste0(output_dir,'/Q3_filtered_list.qs'))
- rm(Qdat)
- gc()
- # for(S in 3:Smax){
- future_map(3:Smax,function(S){
- tmp_list=list()
- for(chr in 1:chrnum){
- tmp=lapply(qs::qread(paste0(output_dir,'/Q',S,'_filtered_list.qs'))[[chr]] ,function(x)strsplit(x, "-"))
- tmp_list[[chr]]=lapply(tmp,function(x)apply(combn(x[[1]],2),2,function(x)paste(x,collapse = "-")))
- }
- qs::qsave(tmp_list,paste0(output_dir,'/pairwise_',S,'_filtered_list.qs'))
- }
- # }
- )
- }
find_cliques.R at commit e121729, no license · at the source
Overview
- Department of Statistics, University of Wisconsin - Madison,Madison, WI USA
- Department of Biomedical Engineering and Informatics, Indiana University Indianapolis,Indianapolis, IN USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison,Madison, WI USA
Abstract
The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.
Repositories
Its files are read in the Code ↔ Paper reader above.
keleslab/MINTsC
e121729fd15493fcb637e0373cfd318248bd3e1d, 15 November 2025Availability: 1 check, the latest on 27 September 2026: the link answers
- 27 September 2026: the link answers
5 files
- code/
functions/ , R, 188 linesfind_cliques.R - code/
functions/ , R, 406 linesmodel_fit_porder.R - code/
functions/ , R, 45 linessignificant_clique_calle r.R - code/
scripts/ , Jupyter, 249 linesTutorial.ipynb - README.md, Text, 42 lines
Zenodo 19502488
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
- 27 September 2026: the link answers (HTTP 200)
Zenodo 19502489
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
- 27 September 2026: the link answers (HTTP 200)
Code availability statement
The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:
- it points to the authors' code: keleslab/
MINTsC , Zenodo 19502488
Read it in the paper: doi.org/10.1038/s41467-026-73773-y.
Tracing map
Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.
What the map holds:
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- no match between paragraphs and code yet;
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.
Data
Datasets cited
- doi:10.7303/
syn21241740 , at the source; found in “Data availability” - geo:GSE119171, at NCBI GEO; found in “Data availability”
- synapse.org/
synapse:syn22264775 , at Synapse; found in “Data availability” - synapse.org/
synapse:syn31141704 , at Synapse; found in “Data availability” - zenodo:19562677, at Zenodo; found in “Data availability”
- zenodo:19965486, at Zenodo; found in “Data availability”
Data availability statement
The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:
- it points to 6 datasets: DOI 10.7303/
syn21241740 , NCBI GEO GSE119171, synapse.org/synapse:syn22264775 , synapse.org/synapse:syn31141704 , Zenodo 19562677, Zenodo 19965486 - it says that the data are available on request
Read it in the paper: doi.org/10.1038/s41467-026-73773-y.
Versions
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 4 keywords, 5 MeSH terms, 2 funders, 45 references.
Cite
This paper
Park, K., Gao, T., Yan, J., & Keleş, S. (2026). MINTsC learns multi-way chromatin interactions from single cell high throughput chromatin conformation data. Nature communications, 17(1), 7077. https://
BibTeX
@article{park2026mintsc,
author = {Park, Kwangmoon and Gao, Tianchuan and Yan, Jingwen and Keleş, Sündüz},
title = {{MINTsC learns multi-way chromatin interactions from single cell high throughput chromatin conformation data}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7077},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/
url = {https://
pmid = {42230572},
pmcid = {PMC13392375}
}
RIS
TY - JOUR
AU - Park, Kwangmoon
AU - Gao, Tianchuan
AU - Yan, Jingwen
AU - Keleş, Sündüz
TI - MINTsC learns multi-way chromatin interactions from single cell high throughput chromatin conformation data
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/
VL - 17
IS - 1
SP - 7077
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/
UR - https://
LA - en
ER -
CSL-JSON
{
"id": "10.1038/
"type": "article-journal",
"title": "MINTsC learns multi-way chromatin interactions from single cell high throughput chromatin conformation data",
"container-title": "Nature communications",
"author": [
{
"family": "Park",
"given": "Kwangmoon"
},
{
"family": "Gao",
"given": "Tianchuan"
},
{
"family": "Yan",
"given": "Jingwen"
},
{
"family": "Keleş",
"given": "Sündüz"
}
],
"container-title-short":
"volume": "17",
"issue": "1",
"page": "7077",
"DOI": "10.1038/
"PMID": "42230572",
"PMCID": "PMC13392375",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
2
]
]
}
}
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