BACKGROUND: Single cell gene expression assays have become a powerful tool with which to dissect heterogeneous populations. While methods and software exist to interrogate such data, what has been lacking is a unified solution combining analysis and visualisation which is also accessible and intuitive for use by non-bioinformaticians, as well as bioinformaticians. RESULTS: We present the Single cell expression visualiser (SCExV), a webtool developed to expedite the analysis of single cell qRT-PCR data. SCExV is able to take any data matrix of Ct values as an input, but can handle files exported by the Fluidigm Biomark platform directly. In addition, SCExV also accepts and automatically integrates cell surface marker intensity values which are measured during index sorting. This allows the user to directly visualise relationships between a single cell gene expression profile and the immunophenotype of the interrogated cell. CONCLUSIONS: SCExV is a freely available webtool created to import, filter, analyse, and visualise single cell gene expression data whilst being able to simultaneously consider cellular immunophenotype. SCExV is designed to be intuitive to use whilst maintaining advanced functionality and flexibility in how analyses are performed.

MOTIVATION: Microarray experiments are inherently noisy. Replication is the key to estimating realistic fold-changes despite such noise. In the analysis of the various sources of noise the dependency structure of the replication needs to be taken into account. RESULTS: We analyzed replicate data sets from a Mycobacterium tuberculosis trcS mutant in order to identify differentially expressed genes and suggest new methods for filtering and normalizing raw array data and for imputing missing values. Mixed ANOVA models are applied to quantify the various sources of error. Such analysis also allows us to determine the optimal number of samples and arrays. Significance values for differential expression are obtained by a hierarchical bootstrapping scheme on scaled residuals. Four highly upregulated genes, including bfrB, were analyzed further. We observed an artefact, where transcriptional readthrough from these genes led to apparent upregulation of adjacent genes. AVAILABILITY: All methods and data discussed are available in the package YASMAhttp://www.cryst.bbk.ac.uk/wernisch/yasma.html for the statistical data analysis system R (http://www.R-project.org).