biostudies-literaturede Boer CGHoward Hughes Medical InstituteNHGRI NIH HHSNational Human Genome Research InstituteCanadian Institutes of Health ResearchCIHR253https://www.ebi.ac.uk/biostudies/studies/S-EPMC6029352biostudies-literatureUnknown19(1)<h4>Background</h4>Variation in chromatin organization across single cells can help shed important light on the mechanisms controlling gene expression, but scale, noise, and sparsity pose significant challenges for interpretation of single cell chromatin data. Here, we develop BROCKMAN (Brockman Representation Of Chromatin by K-mers in Mark-Associated Nucleotides), an approach to infer variation in transcription factor (TF) activity across samples through unsupervised analysis of the variation in DNA sequences associated with an epigenomic mark.<h4>Results</h4>BROCKMAN represents each sample as a vector of epigenomic-mark-associated DNA word frequencies, and decomposes the resulting matrix to find hidden structure in the data, followed by unsupervised grouping of samples and identificationBMC bioinformaticsBROCKMAN: deciphering variance in epigenomic regulators by k-mer factorization.PMC6029352CEGSFellowshipRM1 HG006193Investigatorde Boer CGRegev AfalseBROCKMAN: deciphering variance in epigenomic regulators by k-mer factorization.<h4>Background</h4>Variation in chromatin organization across single cells can help shed important light on the mechanisms controlling gene expression, but scale, noise, and sparsity pose significant challenges for interpretation of single cell chromatin data. Here, we develop BROCKMAN (Brockman Representation Of Chromatin by K-mers in Mark-Associated Nucleotides), an approach to infer variation in transcription factor (TF) activity across samples through unsupervised analysis of the variation in DNA sequences associated with an epigenomic mark.<h4>Results</h4>BROCKMAN represents each sample as a vector of epigenomic-mark-associated DNA word frequencies, and decomposes the resulting matrix to find hidden structure in the data, followed by unsupervised grouping of samples and identification2018-01-01T00:00:00Z2018 Jul2026-05-01T03:21:45.157Z2026-04-07T16:34:32.748ZS-EPMC60293522997000410.1186/s12859-018-2255-6