OSCR

CellLoop: Identifying single-cell 3D genome chromatin loops.

Code ↔ Paper

7 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 7 matches
  1. [1] § Results › Performance evaluation of CellLoop via loop frequency map (LFmap) ↔ CellLoop.py, lines 180–257 · score 0.68 · frequency map, LFmap, single cell loop, loop calling, aggregating, contact
  2. [2] § Results › Overview of CellLoop algorithm ↔ CellLoop.py, lines 180–257 · score 0.58 · LFmap, Frequency Map, contact maps, Chromatin Loop Frequency, aggregated, scloops
  3. [3] § Methods › UMAP visualization and clustering of single cells ↔ plot/plot_units.py, lines 350–405 · score 0.58 · Leiden clustering, genes expressed, UMAP, PCA, log
  4. [4] § Methods › UMAP visualization and clustering of single cells ↔ src/GAGE-seq_preprocess.py, lines 96–152 · score 0.55 · RNA seq, Scanpy, SVD, UMAP, Leiden, filtered
  5. [5] § Methods › The enrichment analysis of ChIP-seq peak on chromatin loops ↔ src/plot_paper.py, lines 83–134 · score 0.54 · plot_surface, seq peaks
  6. [6] § Methods › UMAP visualization and clustering of single cells ↔ plot/plot_units.py, lines 350–405 · score 0.50 · Leiden clustering, Scanpy, UMAP, filtered
  7. [7] § Methods › Identification of single-cell chromatin loops ↔ src/sc_interactions.py, lines 963–1081 · score 0.50 · Node2Vec, walk, network, graph, embedding, neighboring

Paper

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The authors' code

Python · 380 lines · 18 KB · no license · 2 matches

  1. # -*- coding: utf-8 -*-
  2. """
  3. Created on Wed Jul 6 14:32:26 2022
  5. @author: user
  6. """
  7. #!/usr/bin/env python3
  8. # -*- coding: utf-8 -*-
  9. import os
  10. #########################Initial parameters##############################################
  11. ###please write the directory where CellLoop package is located#####
  12. #main_dir='/home/dell/Desktop/CellLoop_test/CellLoop'
  13. main_dir='/mnt/nas/Projects/Project18/Codes/CellLoop_Install/CellLoop'
  15. os.chdir(main_dir)
  16. ######select the data type and bin resolution you want to analyze######
  17. DATASET='Dip-C'
  18. #DATASET='HiRES'
  19. #DATASET='GAGE-seq'
  20. BINSIZE=100e3
  21. if BINSIZE<=10e3:
  22. MAXDIST=5000000
  23. #knn_cell_num=50
  24. else:
  25. MAXDIST=10000000
  26. #knn_cell_num=50
  27. #default parameter to cut-off for removing short-range reads
  28. KNN_CELL_NUM=50
  29. LOW_CUTOFF=1e3
  30. if DATASET=='Dip-C':
  31. GENOME='mm10'
  32. #dataset dir
  33. INDIR="/mnt/nas/Datasets/2020_Dip-C/GSE162511"##
  34. FILE_SUFFIX=".contacts.pairs.txt.gz"
  35. MINCONCNUM=250000
  36. CHR_COLUMNS=[1,3]
  37. POS_COLUMNS=[2,4]
  38. FEATURE='higashi_embed'
  39. OUTDIR=INDIR+'_'+str(int(BINSIZE//1000))+'kb'+'_knn='+str(KNN_CELL_NUM)
  41. elif DATASET=='HiRES':
  42. GENOME='mm10'
  43. INDIR="/mnt/nas/Datasets/2023_Science/GSE223917"
  44. FILE_SUFFIX=".pairs.gz"
  45. MINCONCNUM=250000
  46. CHR_COLUMNS=[1,3]
  47. POS_COLUMNS=[2,4]
  48. FEATURE='RNA_embed'
  49. OUTDIR=INDIR+'_'+str(int(BINSIZE//1000))+'kb'+'_knn='+str(KNN_CELL_NUM)
  51. elif DATASET=='GAGE-seq':
  52. GENOME='mm10'
  53. INDIR="/mnt/data/Datasets/GAGE-seq/SingleCells"
  54. FILE_SUFFIX=".pairs"
  55. MINCONCNUM=200000
  56. CHR_COLUMNS=[0,2]
  57. POS_COLUMNS=[1,3]
  58. FEATURE='RNA_embed'
  59. OUTDIR=INDIR+'_'+str(int(BINSIZE//1000))+'kb'+'_knn='+str(KNN_CELL_NUM)
  61. if GENOME=='hg19':
  62. CHR_LEN=main_dir+'/ext/hg19.chrom.sizes'
  63. FILTER_FILE=main_dir+'/ext/hg19_filter_regions.txt'
  64. elif GENOME=='mm10':
  65. CHR_LEN=main_dir+'/ext/mm10.chrom.sizes'
  66. FILTER_FILE=main_dir+'/ext/mm10_filter_regions.txt'
  67. elif GENOME=='hg38':
  68. CHR_LEN='main_dir+/ext/hg38.chrom.sizes.txt'
  69. FILTER_FILE=main_dir+'/ext/hg38_filter_regions.txt'
  70. ###################################################################################
  71. import argparse
  72. import src.logger
  73. #import time
  74. from src.bin_reads import bin_sets
  75. import pandas as pd
  76. #from src.RepresentLearning_95 import get_rl_for_all_95
  77. #from src.sc_compartments import read_sc_compartment
  78. #from src.sc_tadboundaries import sctad_boundary
  79. from src.sc_interactions import scloops
  80. #from visual_show import visual_show
  81. #from src.commonorspecific_boundaries import comorspebound
  82. #from src.commonorspecific_loops import comorspeloops
  83. from src.sample_aggr_cells import get_aggr_cells
  84. #from src.Computing_contactprob import con_prob_dis
  85. from src.Aggrloops import aggreloops
  86. #from src.clustercells_loops import clustercells
  87. #from src.aggremaps_visualanalyzing import aggremaps,aggreloops_cluster,aggr_bulkmaps,aggr_bulkloops
  88. #from src.statistic_analysis_loops import statistic_analysis_loops
  89. import multiprocessing
  90. import scanpy as sc
  92. #import pandas as pd
  93. #########################################################################################
  94. def main():
  95. #__spec__ = "ModuleSpec(name='builtins', loader=<class '_frozen_importlib.BuiltinImporter'>)"
  96. parser = create_parser()
  97. args = parser.parse_args()
  98. chrom_dict = parse_chrom_lengths(args.chrom, args.chr_lens, args.genome, args.max_chrom_number)
  99. parallel_mode, rank, n_proc, parallel_properties = determine_parallelization_options(args.parallel, args.threaded, args.num_proc)
  101. if rank == 0 and not os.path.exists(args.outdir):
  102. os.makedirs(args.outdir)
  103. if parallel_mode == "parallel":
  104. parallel_properties['comm'].Barrier()
  105. threaded = True if parallel_mode == "threaded" else False
  106. logger = src.logger.Logger(f'{args.outdir}/scloops.log', rank = rank, verbose_threshold = args.verbose, threaded = threaded)
  108. ###############################################################################################
  109. #step 1; binning
  110. bin_dir = os.path.join(args.outdir, "binned")
  111. if 'bin' in args.steps:
  112. logger.write('starting the binning step')
  113. logger.flush()
  114. if parallel_mode == 'nonparallel':
  115. #indir=args.indir; suffix=args.suffix; binsize = args.binsize; outdir = bin_dir;
  116. #chr_columns = args.chr_columns; pos_columns = args.pos_columns;
  117. #low_cutoff = args.low_cutoff; mincontactnum=args.mincontactnum;dataset=args.dataset;n_proc = n_proc;rank = rank;logger = logger
  118. bin_sets(args.indir, args.suffix, binsize = args.binsize, outdir = bin_dir, \
  119. chr_columns = args.chr_columns, pos_columns = args.pos_columns, \
  120. low_cutoff = args.low_cutoff, mincontactnum=args.mincontactnum,dataset=args.dataset,n_proc = n_proc, rank = rank, logger = logger)
  121. elif parallel_mode == 'threaded':
  122. params = [(args.indir, args.suffix, args.binsize, bin_dir, args.chr_columns, args.pos_columns,\
  123. args.low_cutoff,args.mincontactnum,args.dataset,n_proc,i,logger) for i in range(n_proc)]
  124. with multiprocessing.Pool(n_proc) as pool:
  125. pool.starmap(bin_sets, params)
  126. logger.write("binning completed")
  127. logger.flush()
  129. ################### you can get using filelist ##########################
  130. filenames=os.listdir(bin_dir)
  131. filenames.sort()
  132. #for filelist
  133. f=open(args.indir+'/filelist.txt','w')
  134. for line in filenames:
  135. print(line)
  136. f.write(line.rstrip('.bedpe')+'\n')
  137. f.close()
  140. ####################################################################################################
  141. #step 1.1 #choose feature compute knn graph of single cells
  142. ####################################################################################################
  143. #indir=args.indir
  144. feature=args.feature
  145. if feature=='higashi_embed':
  146. feature_dir=main_dir+'/Dataset/'+DATASET+'/CellFeatures/'+feature
  147. filenames=os.listdir(bin_dir)
  148. filenames.sort()
  149. #for filelist
  150. f=open(feature_dir+'/filelist.txt','w')
  151. for line in filenames:
  152. print(line)
  153. f.write(line.rstrip('.bedpe')+'\n')
  154. f.close()
  156. elif feature=='RNA_embed':
  157. feature_dir=main_dir+'/Dataset/'+DATASET+'/CellFeatures/'+feature
  158. adata_rna=sc.read(feature_dir+'/adata_rna.h5ad')
  159. filelist_dir=args.indir+'/filelist.txt'
  160. filenames=pd.read_csv(filelist_dir, index_col=None,header=None,sep='\t')
  161. # filenames=os.listdir(bin_dir)
  162. # filenames.sort()
  163. adata_rna.obs_names=adata_rna.obs['cell']
  164. #adata_rna=adata_rna[[cellname in filenames for cellname in adata_rna.obs_names],:]
  165. filenames=filenames[[cellname in adata_rna.obs_names for cellname in filenames[0].values]]
  166. adata_rna=adata_rna[filenames[0].values.tolist(),:]
  167. # sc.pp.neighbors(adata_rna, n_neighbors=15, n_pcs=20)
  168. # sc.tl.umap(adata_rna)
  169. # sc.tl.leiden(adata_rna)
  170. # sc.pl.umap(adata_rna,color=['n_genes','leiden'], palette='tab20',size=10,wspace=1,hspace=0.1)
  171. adata_rna.write(feature_dir+'/adata_rna.h5ad')
  172. #for filelist
  173. f=open(feature_dir+'/filelist.txt','w')
  174. for line in filenames[0]:
  175. print(line)
  176. f.write(line.rstrip('.bedpe')+'\n')
  177. f.close()
  180. #############################################################################################
  181. #### step 2. Generating the enhanced contact map of the single cells#########################
  182. # sampling and getting aggregated cells
  183. #choose feature compute knn graph of single cells
  184. SampleAggrCell_dir=os.path.join(args.outdir,'sampleaggrcell_dir')
  185. knn_cell_num=args.knn_cell_num
  186. #k=20
  187. if 'sampleaggrcell' in args.steps:
  188. logger.write('starting the sampling aggragated cells step')
  189. logger.flush()
  190. #if parallel_mode == 'nonparallel':
  191. #bin_dir=bin_dir;outdir=SampleAggrCell_dir;feature_dir=feature_dir;feature=feature;chrom_lens=chrom_dict;
  192. #n_proc=n_proc;rank=rank;binsize=args.binsize;k=knn_cell_num;
  193. get_aggr_cells(bin_dir=bin_dir,outdir=SampleAggrCell_dir,feature_dir=feature_dir,feature=feature,chrom_lens=chrom_dict,\
  194. n_proc=n_proc, rank=rank,binsize=args.binsize,k=knn_cell_num)
  195. # bin_sets(args.indir, args.suffix, binsize = args.binsize, outdir = bin_dir, \
  196. # chr_columns = args.chr_columns, pos_columns = args.pos_columns, \
  197. # low_cutoff = args.low_cutoff, n_proc = n_proc, rank = rank, logger = logger)
  198. # elif parallel_mode == 'threaded':
  199. # params = [(bin_dir,SampleAggrCell_dir,feature_dir,feature,chrom_dict,n_proc,i,\
  200. # args.binsize,knn_cell_num) for i in range(n_proc)]
  201. # with multiprocessing.Pool(n_proc) as pool:
  202. # pool.starmap(get_aggr_cells, params)
  203. logger.write("binning completed")
  204. logger.flush()
  207. ### Step 3 Calling chromatin loops of single cells################################################################
  208. scloops_dir=os.path.join(args.outdir,'scloops_new')
  209. # temp_keys={'chr2','chr13'}
  210. # chrom_dict={key:chrom_dict[key] for key in chrom_dict.keys() & temp_keys }
  211. alpha=1
  212. scloops_outdir=os.path.join(scloops_dir+'_'+feature,'alpha='+str(alpha))
  213. # scloops_dir+'_'+feature+'_alpha='+str(alpha)
  214. if 'scloops' in args.steps:
  215. indir=SampleAggrCell_dir+'_'+feature
  216. logger.write('starting the chromatin loops calling')
  217. logger.flush()
  218. if parallel_mode=="nonparallel":
  219. #chrom_lens = chrom_dict;outdir = scloops_outdir;indir = indir
  220. #binsize = args.binsize;dist = args.dist
  221. #neighborhood_limit_lower = args.local_lower_limit; neighborhood_limit_upper = args.local_upper_limit
  222. #rank = rank; n_proc = n_proc; max_mem = args.max_memory; logger = logger
  223. scloops(indir = indir, outdir = scloops_outdir, chrom_lens = chrom_dict, \
  224. binsize = args.binsize, dist = args.dist, \
  225. neighborhood_limit_lower = args.local_lower_limit, \
  226. neighborhood_limit_upper = args.local_upper_limit,alpha=alpha,\
  227. rank = rank, n_proc = n_proc, max_mem = args.max_memory, logger = logger)
  228. elif parallel_mode == 'threaded':
  229. params = [(indir, scloops_outdir, chrom_dict, \
  230. args.binsize,args.dist,args.local_lower_limit,args.local_upper_limit,alpha,\
  231. i,n_proc, args.max_memory,logger) for i in range(n_proc)]
  232. with multiprocessing.Pool(n_proc) as pool:
  233. pool.starmap(scloops, params)
  234. logger.write("calling completed")
  235. logger.flush()
  237. #############################################################################################
  238. ##### Step 4. Generating chromatin loop frequency map(LFmap)##############################################################
  239. aggreloops_dir=os.path.join(args.outdir,'aggreloops','alpha='+str(alpha))
  240. if 'aggreloops' in args.steps:
  241. logger.write('starting aggregated loops')
  242. logger.flush()
  243. if parallel_mode=="nonparallel":
  244. #indir = scloops_outdir; outdir = aggreloops_dir;chrom_lens = chrom_dict;
  245. #binsize = args.binsize;dist = args.dist
  246. #rank = rank; n_proc = n_proc; max_mem = args.max_memory; logger = logger
  247. aggreloops(indir = scloops_outdir,outdir = aggreloops_dir,\
  248. chrom_lens = chrom_dict, binsize = args.binsize,
  249. rank = rank, n_proc = n_proc, logger = logger)
  250. elif parallel_mode == 'threaded':
  251. params = [(scloops_outdir, aggreloops_dir, chrom_dict, \
  252. args.binsize,\
  253. i,n_proc,logger) for i in range(n_proc)]
  254. with multiprocessing.Pool(n_proc) as pool:
  255. pool.starmap(aggreloops, params)
  256. logger.write("aggregating completed")
  257. logger.flush()
  261. def parse_chrom_lengths(chrom, chrom_lens_filename, genome, max_chrom_number):
  262. if not max_chrom_number or max_chrom_number == -1:
  263. if not chrom or chrom == "None":
  264. chrom_count = 22 if genome.startswith('hg') else 19 if genome.startswith("mm") else None
  265. if not chrom_count:
  266. raise("Genome name is not recognized. Use --max-chrom-number")
  267. chrom = ['chr' + str(i) for i in range(1, chrom_count + 1)]
  268. else:
  269. chrom = [c.strip() for c in chrom.split()]
  270. else:
  271. chrom = ['chr' + str(i) for i in range(1, max_chrom_number + 1)]
  272. with open(chrom_lens_filename) as infile:
  273. lines = infile.readlines()
  274. chrom_lens = {line.split()[0]: int(line.split()[1]) for line in lines if line.split()[0] in chrom}
  275. return chrom_lens
  277. def determine_parallelization_options(parallel, threaded, n_proc):
  278. if parallel and threaded:
  279. raise "Only one of 'parallel' or 'threaded' flags can be set. \
  280. If using a job scheduling system on a cluster with multiple machines, use parallel.\
  281. If using a single machine with multiple CPUs, use threaded"
  282. else:
  283. if parallel:
  284. #print('it;s parallel')
  285. from mpi4py import MPI
  286. comm = MPI.COMM_WORLD
  287. n_proc = comm.Get_size()
  288. rank = comm.Get_rank()
  289. #print('n proc is', n_proc)
  290. #print('rank is', rank)
  291. mode = 'parallel'
  292. properties = {'comm': comm}
  293. elif threaded:
  294. import multiprocessing
  295. if n_proc < 1:
  296. raise Exception('if threaded flag is set, n should be a positive integer')
  297. n_proc = n_proc
  298. mode = 'threaded'
  299. rank = 0
  300. properties = {}
  301. else:
  302. mode = 'nonparallel'
  303. n_proc = 1
  304. rank = 0
  305. properties = {}
  306. return mode, rank, n_proc, properties
  308. def create_parser():
  309. parser = argparse.ArgumentParser()
  311. ################################# Required parameters
  312. parser.add_argument('-i', '--indir', action = 'store', required =False, \
  313. help = 'input directory',default=INDIR)
  314. parser.add_argument('-s', '--suffix', required = False, \
  315. help = 'suffix of the input files', default=FILE_SUFFIX)
  316. parser.add_argument('-o', '--outdir', action = 'store', \
  317. required = False, help = 'output directory', default=OUTDIR)
  318. parser.add_argument('-c', '--chr-columns', action = 'store', nargs = 2, \
  319. type = int, help = 'two integer column numbers for chromosomes', required = False, default = CHR_COLUMNS)
  320. parser.add_argument('-p', '--pos-columns', action = 'store', nargs = 2, \
  321. type = int, help = 'two integer column numbers for read positions', required = False, default = POS_COLUMNS)
  322. parser.add_argument('-l', '--chr-lens', action = 'store', \
  323. help = 'path to the chromosome lengths file', required = False,\
  324. default=CHR_LEN)
  325. parser.add_argument('-g', '--genome', action = 'store', help = 'genome name; hgxx or mmxx', \
  326. required = False, default = "hg19")
  327. parser.add_argument('--dist', type = int, help = 'distance from diagonal to consider', \
  328. default =MAXDIST, required = False)
  329. parser.add_argument('--mincontactnum', type = int, help = 'minimum contact number for single cells', \
  330. default =MINCONCNUM, required = False)
  331. parser.add_argument('--dataset', action = 'store', help = 'choose dataset', \
  332. default = DATASET, required = False)
  333. parser.add_argument('--knn-cell-num', action = 'store', help = 'maximum neighboring cell number', \
  334. default =KNN_CELL_NUM, required = False)
  336. parser.add_argument('--feature', action = 'store', help = 'choose feature', \
  337. default = FEATURE, required = False)
  339. parser.add_argument('--binsize', type = int, help = 'bin size used for binning the reads', \
  340. required = False, default = BINSIZE)
  341. parser.add_argument('--low-cutoff', type = int, help = 'cut-off for removing short-range reads', \
  342. default = LOW_CUTOFF, required = False)
  344. ################################## optional parameters
  345. parser.add_argument('--parallel', action = 'store_true', default = False, \
  346. help = 'if set, will attempt to run in parallel mode', required = False)
  347. parser.add_argument('--threaded', action = 'store_true', default =True, \
  348. help = 'if set, will attempt to use multiprocessing on single machine', required = False)
  349. parser.add_argument('-n', '--num-proc', help = 'number of processes used in threaded mode',
  350. required = False, default = 10, type = int)
  351. parser.add_argument('--local-lower-limit', default = 2, type = int, required = False, \
  352. help = 'number of bins around center (in each direction) to exlude from neighborhood')
  353. parser.add_argument('--local-upper-limit', default = 5, type = int, required = False, \
  354. help = 'number of bins around center (in each direction) forming the neighborhood')
  355. parser.add_argument('--filter-file', default=FILTER_FILE, required = False, \
  356. help = "bed file of regions to be filtered.")
  357. parser.add_argument('--max-memory', default = 8, type = float, required = False, \
  358. help = 'memory available in GB, that will be used in constructing dense matrices')
  359. parser.add_argument('--verbose', type = int, required = False, default = 0,
  360. help = 'integer between 0 and 3 (inclusive), 0 for the least amount to print to log file')
  361. parser.add_argument('--steps', nargs = "*", default = ['bin','sampleaggrcell','scloops','aggreloops'] , \
  362. required = False, help = 'steps to run. Default is all steps.')
  363. parser.add_argument('--chrom', action = 'store', help = 'chromosome to process', \
  364. required = False, default = None)
  365. parser.add_argument('--max-chrom-number', action = "store", required = False, type = int, \
  366. help = "biggest chromosome number to consider in genome, for example 22 for hg", default = -1)
  367. return parser
  369. if __name__ == "__main__":
  370. main()
  371. #passa

CellLoop.py at commit 87e49ce, no license · at the source

Overview

Authors: Yusen Ye1, Yiheng Wang1, Shiji Liu1, Yuxuan Hu1, Liang Yu1, Weibing Wang1, Shihua Zhang2,3,4, Hebing Chen5, Lin Gao1
ORCID iDs: Yusen Ye, Shihua Zhang, Hebing Chen, Lin Gao
  1. School of Computer Science and Technology, Xidian University,Xi’an, Shaanxi China
  2. NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences,Beijing, China
  3. School of Mathematical Sciences, University of Chinese Academy of Sciences,Beijing, China
  4. Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences,Hangzhou, China
  5. Academy of Military Medical Sciences,Beijing, China
Institutions: Xidian University (China); Chinese Academy of Sciences (China); University of Chinese Academy of Sciences (China); Academy of Military Medical Sciences (China)
Journal: Nature communications, volume 17, issue 1, article 4904
Dates: received 30 June 2025; accepted 19 March 2026; published online 6 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71406-y · PMID 41942438 · PMCID PMC13230548 · OpenAlex W4409537027
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.04.08.647893
Status: code verified
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity, Machine learning
Keywords: Data mining, Genomics, Genome informatics
MeSH: Chromatin*, Genome*, Single-Cell Analysis*, Animals, Brain, Mice (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (62573335, 62002277, 62422318, 62132015, 62350087, 62550005)
Citations: cited by 1 paper (Europe PMC); 46 references in the paper

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, with 7 matches between paragraphs and lines of code.

YusenYe/CellLoop

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 87e49cefe2bbbab98e5ace6fad552dc7b4a1b5ee, 30 December 2025
Languages: Python (11)
Size: 40 files, 11 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: pandas (9 files), NumPy (6 files), Scanpy (6 files), SciPy (5 files), Matplotlib (3 files), seaborn (2 files), NetworkX (1 file), scikit-image (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
12 files
At the source: github.com/YusenYe/CellLoop

Zenodo 18784293

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (9 files), NumPy (6 files), Scanpy (6 files), SciPy (5 files), Matplotlib (3 files), seaborn (2 files), NetworkX (1 file), scikit-image (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
12 files
At the source:

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:

Read it in the paper: doi.org/10.1038/s41467-026-71406-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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 22 scripts, each with its path and the digest of its content;
  • 7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

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:

Read it in the paper: doi.org/10.1038/s41467-026-71406-y.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 3 keywords, 6 MeSH terms, 1 funder, 45 references.

Cite

This paper

Ye, Y., Wang, Y., Liu, S., Hu, Y., Yu, L., Wang, W., Zhang, S., Chen, H., & Gao, L. (2026). CellLoop: Identifying single-cell 3D genome chromatin loops. Nature communications, 17(1), 4904. https://doi.org/10.1038/s41467-026-71406-y

BibTeX

@article{ye2026cellloop,
author = {Ye, Yusen and Wang, Yiheng and Liu, Shiji and Hu, Yuxuan and Yu, Liang and Wang, Weibing and Zhang, Shihua and Chen, Hebing and Gao, Lin},
title = {{CellLoop: Identifying single-cell 3D genome chromatin loops}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {4904},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71406-y},
url = {https://doi.org/10.1038/s41467-026-71406-y},
pmid = {41942438},
pmcid = {PMC13230548}
}

RIS

TY - JOUR
AU - Ye, Yusen
AU - Wang, Yiheng
AU - Liu, Shiji
AU - Hu, Yuxuan
AU - Yu, Liang
AU - Wang, Weibing
AU - Zhang, Shihua
AU - Chen, Hebing
AU - Gao, Lin
TI - CellLoop: Identifying single-cell 3D genome chromatin loops
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/06
VL - 17
IS - 1
SP - 4904
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71406-y
UR - https://doi.org/10.1038/s41467-026-71406-y
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-71406-y",
"type": "article-journal",
"title": "CellLoop: Identifying single-cell 3D genome chromatin loops",
"container-title": "Nature communications",
"author": [
{
"family": "Ye",
"given": "Yusen"
},
{
"family": "Wang",
"given": "Yiheng"
},
{
"family": "Liu",
"given": "Shiji"
},
{
"family": "Hu",
"given": "Yuxuan"
},
{
"family": "Yu",
"given": "Liang"
},
{
"family": "Wang",
"given": "Weibing"
},
{
"family": "Zhang",
"given": "Shihua"
},
{
"family": "Chen",
"given": "Hebing"
},
{
"family": "Gao",
"given": "Lin"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "4904",
"DOI": "10.1038/s41467-026-71406-y",
"PMID": "41942438",
"PMCID": "PMC13230548",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-71406-y",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
6
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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