CONFESS: Fluorescence-based single-cell ordering in R

Abstract

Modern high-throughput single-cell technologies facilitate the efficient processing of hundreds of individual cells to comprehensively study their morphological and genomic heterogeneity. Fluidigms C1 Auto Prep system isolates fluorescence-stained cells into specially designed capture sites, generates high-resolution image data and prepares the associated cDNA libraries for mRNA sequencing. Current statistical methods focus on the analysis of the gene expression profiles and ignore the important information carried by the images. Here we propose a new direction for single-cell data analysis and develop CONFESS, a customized cell detection and fluorescence signal estimation model for images coming from the Fluidigm C1 system. Applied to a set of HeLa cells expressing fluorescence cell cycle reporters, the method predicted the progression state of hundreds of samples and enabled us to study the spatio-temporal dynamics of the HeLa cell cycle. The output can be easily integrated with the associated single-cell RNA-seq expression profiles for deeper understanding of a given biological system. CONFESS R package is available at Bioconductor (http://bioconductor.org/packages/release/bioc/html/CONFESS.html).