title: FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data.
abstract: BACKGROUND
  A key challenge in the emerging field of single-cell RNA-Seq is to characterize phenotypic diversity between cells and visualize this information in an informative manner. A common technique when dealing with high-dimensional data is to project the data to 2 or 3 dimensions for visualization. However, there are a variety of methods to achieve this result and once projected, it can be difficult to ascribe biological significance to the observed features. Additionally, when analyzing single-cell data, the relationship between cells can be obscured by technical confounders such as variable gene capture rates.
  
  
  RESULTS
  To aid in the analysis and interpretation of single-cell RNA-Seq data, we have developed FastProject, a software tool which analyzes a gene expression matrix and produces a dynamic output report in which two-dimensional projections of the data can be explored. Annotated gene sets (referred to as gene 'signatures') are incorporated so that features in the projections can be understood in relation to the biological processes they might represent. FastProject provides a novel method of scoring each cell against a gene signature so as to minimize the effect of missed transcripts as well as a method to rank signature-projection pairings so that meaningful associations can be quickly identified. Additionally, FastProject is written with a modular architecture and designed to serve as a platform for incorporating and comparing new projection methods and gene selection algorithms.
  
  
  CONCLUSIONS
  Here we present FastProject, a software package for two-dimensional visualization of single cell data, which utilizes a plethora of projection methods and provides a way to systematically investigate the biological relevance of these low dimensional representations by incorporating domain knowledge.
authors.affiliation: Department of Electrical Engineering and Computer Science and Center for Computational Biology, University of California, Berkeley, 97420, CA, USA.
authors.forename: David
authors.initials: D
authors.lastname: DeTomaso
authors.affiliation: Department of Electrical Engineering and Computer Science and Center for Computational Biology, University of California, Berkeley, 97420, CA, USA. niryosef@berkeley.edu.|Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard University, Boston, 02139, Massachusetts, USA. niryosef@berkeley.edu.
authors.forename: Nir
authors.initials: N
authors.lastname: Yosef
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods; D005909:Glioblastoma / Q000235:genetics*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D017423:Sequence Analysis, RNA / Q000379:methods*; D059010:Single-Cell Analysis / Q000379:methods*; D012984:Software
chemical_list: D014408:Biomarkers, Tumor
keywords: Dimensionality reduction; RNA-Seq; Single-Cell