Large-scale single-cell RNA sequencing (scRNA-seq) data sets that are produced in different laboratories and at different times contain batch effects that may compromise the integration and interpretation of the data. Existing scRNA-seq analysis methods incorrectly assume that the composition of cell populations is either known or identical across batches. We present a strategy for batch correction based on the detection of mutual nearest neighbors (MNNs) in the high-dimensional expression space. Our approach does not rely on predefined or equal population compositions across batches; instead, it requires only that a subset of the population be shared between batches. We demonstrate the superiority of our approach compared with existing methods by using both simulated and real scRNA-seq data sets. Using multiple droplet-based scRNA-seq data sets, we demonstrate that our MNN batch-effect-correction method can be scaled to large numbers of cells.

The temporal order of differentiating cells is intrinsically encoded in their single-cell expression profiles. We describe an efficient way to robustly estimate this order according to diffusion pseudotime (DPT), which measures transitions between cells using diffusion-like random walks. Our DPT software implementations make it possible to reconstruct the developmental progression of cells and identify transient or metastable states, branching decisions and differentiation endpoints.

Supplementary data are available at Bioinformatics online.

Here we report the use of diffusion maps and network synthesis from state transition graphs to better understand developmental pathways from single cell gene expression profiling. We map the progression of mesoderm towards blood in the mouse by single-cell expression analysis of 3,934 cells, capturing cells with blood-forming potential at four sequential developmental stages. By adapting the diffusion plot methodology for dimensionality reduction to single-cell data, we reconstruct the developmental journey to blood at single-cell resolution. Using transitions between individual cellular states as input, we develop a single-cell network synthesis toolkit to generate a computationally executable transcriptional regulatory network model that recapitulates blood development. Model predictions were validated by showing that Sox7 inhibits primitive erythropoiesis, and that Sox and Hox factors control early expression of Erg. We therefore demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that control organogenesis.

Supplementary data are available at Bioinformatics online.

Single-cell gene expression profiles of differentiating cells encode their intrinsic latent temporal order. We describe an efficient way to robustly estimate this order according to a diffusion pseudotime, which measures transitions on all length scales between cells using diffusion-like random walks. This allows us to identify cells that undergo branching decisions or are in metastable states, and thereby genes differentially regulated at these states.

Summary: Diffusion maps are a spectral method for non-linear dimension reduction and have recently been adapted for the visualization of single cell expression data. Here we present destiny , an efficient R implementation of the diffusion map algorithm. Our package includes a single-cell specific noise model allowing for missing and censored values. In contrast to previous implementations, we further present an efficient nearest-neighbour approximation that allows for the processing of hundreds of thousands of cells and a functionality for projecting new data on existing diffusion maps. We exemplarily apply destiny to a recent time-resolved mass cytometry dataset of cellular reprogramming. Availability and implementation:destiny is an open-source R/Bioconductor package http://bioconductor.org/packages/ destiny also available at https://www.helmholtz-muenchen. de/icb/destiny. A detailed vignette describing functions and workflows is provided with the package.

Motivation: High-dimensional single-cell snapshot data are becoming widespread in the systems biology community, as a mean to understand biological processes at the cellular level. However, as temporal information is lost with such data, mathematical models have been limited to capture only static features of the underlying cellular mechanisms.Results: Here, we present a modular framework which allows to recover the temporal behaviour from single-cell snapshot data and reverse engineer the dynamics of gene expression. The framework combines a dimensionality reduction method with a cell time-ordering algorithm to generate pseudo time-series observations. These are in turn used to learn transcriptional ODE models and do model selection on structural network features. We apply it on synthetic data and then on real hematopoietic stem cells data, to reconstruct gene expression dynamics during differentiation pathways and infer the structure of a key gene regulatory network.Availability and implementation: C++ and Matlab code available at https://www.helmholtz-muenchen.de/fileadmin/ICB/software/inferenceSnapshot.zip.Contact: fabian.theis@helmholtz-muenchen.deSupplementary information: Supplementary data are available at Bioinformatics online.