Detection of correlated hidden factors from single cell transcriptomes using Iteratively Adjusted-SVA (IA-SVA)
暂无分享,去创建一个
[1] Carlo Colantuoni,et al. Decomposing cell identity for transfer learning across cellular measurements, platforms, tissues, and species , 2018, bioRxiv.
[2] S. Dudoit,et al. A general and flexible method for signal extraction from single-cell RNA-seq data , 2018, Nature Communications.
[3] M. Newton,et al. SCnorm: robust normalization of single-cell RNA-seq data , 2017, Nature Methods.
[4] P. Robson,et al. CellView: Interactive exploration of high dimensional single cell RNA-seq data , 2017, bioRxiv.
[5] Duygu Ucar,et al. Genomics of Islet (Dys)function and Type 2 Diabetes. , 2017, Trends in genetics : TIG.
[6] N. Hacohen,et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors , 2017, Science.
[7] J. George,et al. Single-cell transcriptomes identify human islet cell signatures and reveal cell-type–specific expression changes in type 2 diabetes , 2017, Genome research.
[8] S. Weissman,et al. Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization , 2017, PeerJ.
[9] David A. Knowles,et al. Batch effects and the effective design of single-cell gene expression studies , 2016, Scientific Reports.
[10] A. Murphy,et al. RNA Sequencing of Single Human Islet Cells Reveals Type 2 Diabetes Genes. , 2016, Cell metabolism.
[11] Davis J. McCarthy,et al. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor , 2016, F1000Research.
[12] J. Schug,et al. Single-Cell Transcriptomics of the Human Endocrine Pancreas , 2016, Diabetes.
[13] Greg Finak,et al. The contribution of cell cycle to heterogeneity in single-cell RNA-seq data , 2016, Nature Biotechnology.
[14] Hsin C. Lin,et al. Use of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells , 2016, Proceedings of the National Academy of Sciences.
[15] Aleksandra A. Kolodziejczyk,et al. Classification of low quality cells from single-cell RNA-seq data , 2016, Genome Biology.
[16] Valentina Proserpio,et al. Single-cell technologies are revolutionizing the approach to rare cells , 2015, Immunology and cell biology.
[17] Minoru Kanehisa,et al. KEGG as a reference resource for gene and protein annotation , 2015, Nucleic Acids Res..
[18] Monika S. Kowalczyk,et al. Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells , 2015, Genome research.
[19] Mingxiang Teng,et al. On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data , 2015 .
[20] A. Gelman,et al. Beyond subjective and objective in statistics , 2015, 1508.05453.
[21] S. Quake,et al. A survey of human brain transcriptome diversity at the single cell level , 2015, Proceedings of the National Academy of Sciences.
[22] Evan Z. Macosko,et al. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets , 2015, Cell.
[23] S. Teichmann,et al. Computational and analytical challenges in single-cell transcriptomics , 2015, Nature Reviews Genetics.
[24] Fabian J Theis,et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells , 2015, Nature Biotechnology.
[25] Juancarlos Chan,et al. Gene Ontology Consortium: going forward , 2014, Nucleic Acids Res..
[26] Alyssa C. Frazee,et al. Polyester: Simulating RNA-Seq Datasets With Differential Transcript Expression , 2014, bioRxiv.
[27] S. Dudoit,et al. Normalization of RNA-seq data using factor analysis of control genes or samples , 2014, Nature Biotechnology.
[28] J. Leek. svaseq: removing batch effects and other unwanted noise from sequencing data , 2014, bioRxiv.
[29] Shawn M. Gillespie,et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma , 2014, Science.
[30] Laurens van der Maaten,et al. Accelerating t-SNE using tree-based algorithms , 2014, J. Mach. Learn. Res..
[31] Johann A. Gagnon-Bartsch,et al. Using control genes to correct for unwanted variation in microarray data. , 2012, Biostatistics.
[32] Andrew E. Teschendorff,et al. Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling studies , 2011, Bioinform..
[33] Jeffrey T Leek,et al. A general framework for multiple testing dependence , 2008, Proceedings of the National Academy of Sciences.
[34] John D. Storey,et al. Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis , 2007, PLoS genetics.
[35] James Maurer,et al. Going Forward , 2007, ACM Queue.
[36] A. Buja,et al. Remarks on Parallel Analysis. , 1992, Multivariate behavioral research.