Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors L Haghverdi, ATL Lun, MD Morgan, JC Marioni Nature Biotechnology, 2018 | 1843 | 2018 |
A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. ATL Lun, DJ McCarthy, JC Marioni F1000 Research Ltd, 2016 | 1479 | 2016 |
Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R DJ McCarthy, KR Campbell, ATL Lun, QF Wills Bioinformatics 33 (8), 1179-1186, 2017 | 1459 | 2017 |
Pooling across cells to normalize single-cell RNA sequencing data with many zero counts ATL Lun, K Bach, JC Marioni Genome biology 17 (1), 75, 2016 | 1080 | 2016 |
From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline Y Chen, ATL Lun, GK Smyth F1000Research 5, 2016 | 1050 | 2016 |
EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data ATL Lun, S Riesenfeld, T Andrews, T Gomes, JC Marioni Genome Biology 20 (1), 63, 2019 | 653 | 2019 |
Orchestrating single-cell analysis with Bioconductor RA Amezquita, ATL Lun, E Becht, VJ Carey, LN Carpp, L Geistlinger, ... Nature Methods, 1-9, 2019 | 650 | 2019 |
csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows ATL Lun, GK Smyth Nucleic acids research 44 (5), e45, 2016 | 398 | 2016 |
It’s DE-licious: a recipe for differential expression analyses of RNA-seq experiments using quasi-likelihood methods in edgeR ATL Lun, Y Chen, GK Smyth Methods in Molecular Biology 1418, 391-416, 2016 | 396 | 2016 |
Doublet identification in single-cell sequencing data using scDblFinder PL Germain, A Lun, CG Meixide, W Macnair, MD Robinson F1000Research 10, 2021 | 359 | 2021 |
Three-dimensional disorganization of the cancer genome occurs coincident with long-range genetic and epigenetic alterations PC Taberlay, J Achinger-Kawecka, ATL Lun, FA Buske, K Sabir, ... Genome research 26 (6), 719-731, 2016 | 348 | 2016 |
Detection and removal of barcode swapping in single-cell RNA-Seq data JA Griffiths, AC Richard, K Bach, ATL Lun, JC Marioni Nature Communications 9 (1), 2667, 2018 | 233 | 2018 |
Differential Expression Analysis of Complex RNA-seq Experiments Using edgeR Y Chen, ATL Lun, GK Smyth Statistical Analysis of Next Generation Sequencing Data, 51-74, 2014 | 206 | 2014 |
diffHic: a Bioconductor package to detect differential genomic interactions in Hi-C data ATL Lun, GK Smyth BMC Bioinformatics 16 (1), 258, 2015 | 203 | 2015 |
COMRADES determines in vivo RNA structures and interactions O Ziv, MM Gabryelska, ATL Lun, LFR Gebert, J Sheu-Gruttadauria, ... Nature methods 15 (10), 785-788, 2018 | 162 | 2018 |
Transcriptional Heterogeneity in Naive and Primed Human Pluripotent Stem Cells at Single-Cell Resolution T Messmer, F von Meyenn, A Savino, F Santos, H Mohammed, ATL Lun, ... Cell reports 26 (4), 815-824. e4, 2019 | 142 | 2019 |
Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data ATL Lun, JC Marioni Biostatistics 18 (3), 451-464, 2017 | 138 | 2017 |
Testing for differential abundance in mass cytometry data ATL Lun, AC Richard, JC Marioni Nature methods 14 (7), 707-709, 2017 | 131 | 2017 |
De novo detection of differentially bound regions for ChIP-seq data using peaks and windows: controlling error rates correctly ATL Lun, GK Smyth Nucleic acids research 42 (11), e95-e95, 2014 | 128 | 2014 |
Specificity of RNAi, LNA and CRISPRi as loss-of-function methods in transcriptional analysis L Stojic, ATL Lun, J Mangei, P Mascalchi, V Quarantotti, AR Barr, C Bakal, ... Nucleic acids research, 2018 | 114 | 2018 |