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ReFACTor specifications

Information


Unique identifier OMICS_13676
Name ReFACTor
Alternative name Reference-Free Adjustment for Cell-Type composition
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data A path to a site by sample matrix file of tab-delimited beta-normalized methylation levels and the number of assumed cell types
Output data A matrix with the first numcomp ReFACTor components for each individual and a ranked list of the methylation sites
Output format txt
Operating system Unix/Linux
Programming languages Python
Computer skills Advanced
Version 1.0
Stability Stable
Requirements
R
Maintained Yes

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Maintainer


  • person_outline Eran Halperin

Publication for Reference-Free Adjustment for Cell-Type composition

ReFACTor citations

 (9)
library_books

Epigenome wide association study of total serum immunoglobulin E in children: a life course approach

2018
Clin Epigenetics
PMCID: 5905182
PMID: 29692868
DOI: 10.1186/s13148-018-0488-x

[…] nsity of methylated cytosines over the signal intensity of methylated and un-methylated cytosines at the 5C position [β value = M/(M + U)]. In order to control for cell-type heterogeneity, we applied ReFACTor—a reference-free method based on principle component analysis (PCA) with low rank approximation []. We chose this method because ReFACTor components correlate well with measured eosinophil an […]

library_books

MethCNA: a database for integrating genomic and epigenomic data in human cancer

2018
BMC Genomics
PMCID: 5810021
PMID: 29433427
DOI: 10.1186/s12864-018-4525-0

[…] the tumor cell content between samples. There are several tools developed for the correction of cell type heterogeneity and control for false positives in large-scale epigenome data analysis, such as ReFACTor [] and MeDeCom []. In the next release of our database, we will utilize these tools to remove confounding variation and to provide a better framework for data interpretation. […]

library_books

Comparative genomic analysis of the ‘pseudofungus’ Hyphochytrium catenoides

2018
Open Biol
PMCID: 5795050
PMID: 29321239
DOI: 10.1098/rsob.170184

[…] Putatively secreted proteins were predicted using a custom pipeline (https://github.com/fmaguire/predict_secretome/tree/refactor) which identifies sequences predicted to have a signal peptide (via SignalP 4.1 []), no TM domains in their mature peptide (via TMHMM 2.0c [,]), a signal peptide that targets for secretion (v […]

library_books

Grandmaternal stress during pregnancy and DNA methylation of the third generation: an epigenome wide association study

2017
PMCID: 5611722
PMID: 28809857
DOI: 10.1038/tp.2017.153

[…] CDV+ and CVD− groups, we applied a combination of methods available within GLINT 1.0.3 (http://glint-epigenetics.readthedocs.io/en/latest/). EPISTRUCTURE was used for genetic ancestry analysis, while ReFACTor was used for reference free, and the Houseman method was used for reference based cell-type analysis. Since, saliva contains many white-blood cells we used the Houseman blood reference availa […]

library_books

Comparison of different cell type correction methods for genome scale epigenetics studies

2017
BMC Bioinformatics
PMCID: 5391562
PMID: 28410574
DOI: 10.1186/s12859-017-1611-2

[…] corded the number of CpGs showing statistically significant associations with prenatal urinary arsenic exposure after adjusting for multiple testing by controlling false discovery rate (FDR) at 0.05. ReFACTor identified the largest number of CpGs (~60,000) and no CpGs were detected by FaST-LMM-EWASher (Table ). RefFreeCellMix also identified a large number of CpGs (~3000). SVA and RefFreeEWAS dete […]

library_books

Rheumatoid Arthritis Naive T Cells Share Hypermethylation Sites With Synoviocytes

2017
PMCID: 5328845
PMID: 27723282
DOI: 10.1002/art.39952

[…] In order to determine whether the proportions of cell subtypes in the sorted cells were confounding the results, we performed Reference‐Free Adjustment for Cell‐Type Composition (ReFACTor) analysis , in which a PCA is performed on a subset of sites that are informative with respect to the cell composition in the data. The Re […]

Citations

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ReFACTor institution(s)
Blavatnik School of Computer Science, Tel-Aviv University, Tel Aviv, Israel; Department of Medicine, University of California, San Francisco, CA, USA; Department of Bioengineering and Therapeutic Science, University of California, San Francisco, CA, USA; Department of Computer Science, University of California, Los Angeles, CA, USA; Department of Human Genetics, University of California, Los Angeles, CA, USA; Microsoft Research New England, Cambridge, MA, USA; International Computer Science Institute, Berkeley, CA, USA; The Department of Molecular Microbiology and Biotechnology, Tel-Aviv University, Tel Aviv, Israel
ReFACTor funding source(s)
This work was supported by the Edmond J. Safra Center for Bioinformatics at Tel-Aviv University; the Israel Science Foundation (grant n°1425/13); the United States-Israel Binational Science Foundation (grant n°2012304); the German-Israeli Foundation (grant n°1094-33.2/2010); the National Science Foundation (grant n°III-1217615); the Len Blavatnik and the Blavatnik Family Foundation; the National Science Foundation (grants n°1065276, 1302448, 1320589 and 1331176); the National Institutes of Health (grants n°R01-GM083198, R01-ES021801, R01-MH101782, R01-ES022282 and U54EB020403); the Sandler Foundation, the American Asthma Foundation, and the National Institutes of Health (grants n°R01 ES015794, R01 HL088133, M01 RR000083, R01 HL078885, R01 HL104608, P60 MD006902, U19 AI077439 and M01 RR00188).

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