| Cross-tissue immune cell analysis reveals tissue-specific features in humans C Domínguez Conde, C Xu, LB Jarvis, DB Rainbow, SB Wells, T Gomes, ... Science 376 (6594), eabl5197, 2022 | 325 | 2022 |
| Accurate estimation of cell composition in bulk expression through robust integration of single-cell information B Jew, M Alvarez, E Rahmani, Z Miao, A Ko, KM Garske, JH Sul, ... Nature communications 11 (1), 1971, 2020 | 270 | 2020 |
| Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies E Rahmani, N Zaitlen, Y Baran, C Eng, D Hu, J Galanter, S Oh, ... Nature methods 13 (5), 443-445, 2016 | 244 | 2016 |
| Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology E Rahmani, R Schweiger, B Rhead, LA Criswell, LF Barcellos, E Eskin, ... Nature communications 10 (1), 3417, 2019 | 109 | 2019 |
| Genome-wide methylation data mirror ancestry information E Rahmani, L Shenhav, R Schweiger, P Yousefi, K Huen, B Eskenazi, ... Epigenetics & chromatin 10, 1-12, 2017 | 95 | 2017 |
| A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity D Goodman-Meza, A Rudas, JN Chiang, PC Adamson, J Ebinger, N Sun, ... Plos one 15 (9), e0239474, 2020 | 72 | 2020 |
| Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM M Alvarez, E Rahmani, B Jew, KM Garske, Z Miao, JN Benhammou, ... Scientific reports 10 (1), 11019, 2020 | 69 | 2020 |
| GLINT: a user-friendly toolset for the analysis of high-throughput DNA-methylation array data E Rahmani, R Yedidim, L Shenhav, R Schweiger, O Weissbrod, N Zaitlen, ... Bioinformatics 33 (12), 1870-1872, 2017 | 55 | 2017 |
| BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference E Rahmani, R Schweiger, L Shenhav, T Wingert, I Hofer, E Gabel, E Eskin, ... Genome biology 19, 1-18, 2018 | 52 | 2018 |
| Rheumatoid arthritis naive T cells share hypermethylation sites with synoviocytes B Rhead, C Holingue, M Cole, X Shao, HL Quach, D Quach, K Shah, ... Arthritis & rheumatology 69 (3), 550-559, 2017 | 52 | 2017 |
| The causal effect of obesity on prediabetes and insulin resistance reveals the important role of adipose tissue in insulin resistance Z Miao, M Alvarez, A Ko, Y Bhagat, E Rahmani, B Jew, S Heinonen, ... PLoS genetics 16 (9), e1009018, 2020 | 51 | 2020 |
| Correcting for cell-type heterogeneity in DNA methylation: a comprehensive evaluation E Rahmani, N Zaitlen, Y Baran, C Eng, D Hu, J Galanter, S Oh, ... Nature methods 14 (3), 218-219, 2017 | 34 | 2017 |
| RL-SKAT: an exact and efficient score test for heritability and set tests R Schweiger, O Weissbrod, E Rahmani, M Müller-Nurasyid, S Kunze, ... Genetics 207 (4), 1275-1283, 2017 | 20 | 2017 |
| EPIQ—efficient detection of SNP–SNP epistatic interactions for quantitative traits Y Arkin, E Rahmani, ME Kleber, R Laaksonen, W März, E Halperin Bioinformatics 30 (12), i19-i25, 2014 | 20 | 2014 |
| Using stochastic approximation techniques to efficiently construct confidence intervals for heritability R Schweiger, E Fisher, E Rahmani, L Shenhav, S Rosset, E Halperin Journal of Computational Biology 25 (7), 794-808, 2018 | 16 | 2018 |
| Association testing of bisulfite-sequencing methylation data via a Laplace approximation O Weissbrod, E Rahmani, R Schweiger, S Rosset, E Halperin Bioinformatics 33 (14), i325-i332, 2017 | 10 | 2017 |
| Association of a variant in VWA3A with response to anti-vascular endothelial growth factor treatment in neovascular AMD M Grunin, G Beykin, E Rahmani, R Schweiger, G Barel, S Hagbi-Levi, ... Investigative Ophthalmology & Visual Science 61 (2), 48-48, 2020 | 7 | 2020 |
| CONFINED: distinguishing biological from technical sources of variation by leveraging multiple methylation datasets M Thompson, ZJ Chen, E Rahmani, E Halperin Genome biology 20, 1-15, 2019 | 7 | 2019 |
| Detecting heritable phenotypes without a model using fast permutation testing for heritability and set-tests R Schweiger, E Fisher, O Weissbrod, E Rahmani, M Müller-Nurasyid, ... Nature communications 9 (1), 4919, 2018 | 6 | 2018 |
| A Bayesian framework for estimating cell type composition from DNA methylation without the need for methylation reference E Rahmani, R Schweiger, L Shenhav, E Eskin, E Halperin Research in Computational Molecular Biology: 21st Annual International …, 2017 | 5 | 2017 |
