BACKGROUND: The rapidly increasing dimensionality and throughput of flow and mass cytometry data necessitate new bioinformatics tools for analysis and interpretation, and the recently emerging single-cell-based algorithms provide a powerful strategy to meet this challenge. RESULTS: Here, we present CytoTree, an R/Bioconductor package designed to analyze and interpret multidimensional flow and mass cytometry data. CytoTree provides multiple computational functionalities that integrate most of the commonly used techniques in unsupervised clustering and dimensionality reduction and, more importantly, support the construction of a tree-shaped trajectory based on the minimum spanning tree algorithm. A graph-based algorithm is also implemented to estimate the pseudotime and infer intermediate-state cells. We apply CytoTree to several examples of mass cytometry and time-course flow cytometry data on heterogeneity-based cytology and differentiation/reprogramming experiments to illustrate the practical utility achieved in a fast and convenient manner. CONCLUSIONS: CytoTree represents a versatile tool for analyzing multidimensional flow and mass cytometry data and to producing heuristic results for trajectory construction and pseudotime estimation in an integrated workflow.

SUMMARY: RNA sequencing data are becoming a major method of choice to study transcriptomes, including the mapping of gene expression quantitative trait loci (eQTLs). RNA sample contamination or swapping is a serious problem for downstream analysis and may result in false discovery and lose power to detect the true biological relationships. When genetic data are available, for example, in eQTL studies or samples have been previously genotyped or DNA sequenced, it is possible to combine genetic data and RNA-seq data to detect sample contamination and resolve sample swapping problems. In this article, we introduce a tool (IDCheck) that allows easy assessment of concordance between genotype (from SNP arrays or DNA sequencing) and gene expression (RNA-seq) samples. IDCheck compares the identity of RNA-seq reads and SNP genotypes using a likelihood-based method. Based on maximum likelihood estimates of relevant parameters, we can detect sample contamination and identify correct sample pairs when swapping occurs. Our tool provides an efficient and convenient way to evaluate and resolve these problems. AVAILABILITY: A complete description of the software is included on the application home page. The software is freely available in the public domain at http://eqtl.rc.fas.harvard.edu/idcheck/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: We present a pipeline for the pre-processing, quality assessment, read distribution and methylation estimation for methylated DNA immunoprecipitation (MeDIP)-sequence datasets. This is the first MeDIP-seq-specific analytic pipeline that starts at the output of the sequencers. This pipeline will reduce the data analysis load on staff and allows the easy and straightforward analysis of sequencing data for DNA methylation. The pipeline integrates customized scripting and several existing tools, which can deal with both paired and single end data. AVAILABILITY: The package and extensive documentation, and comparison to public data is available at http://life.tongji.edu.cn/meqa/.