Christoph Hafemeister

TL;DR: Two approaches are presented, ranked second in this challenge, that do not rely on sequence-based orthology between rat and human genes to translate pathway perturbation state but instead identify transcriptional response orthologs across a set of training conditions.

TL;DR: Using Drop- seq to profile the single-cell transcriptomes of over 36,000 ventral telencephalic cells isolated from three distinct germinal zones (the ganglionic eminences), it is found that regardless of their origin and ultimate fate, all lineages transit through one of three cardinal transcriptional programs that define the earliest branch points in cell fate determination.

TL;DR: The DREAM Single-Cell Transcriptomics challenge was organized to evaluate methods for reconstructing the spatial arrangement of single cells from single-cell RNA sequencing data, and a novel scoring and cross-validation scheme was devised to evaluate the robustness of the best performing algorithms.

TL;DR: A network inference approach designed to identify sRNA-mediated regulation of transcript levels using existing transcriptional datasets and prior knowledge to infer sRNA regulons using the Inferelator, which produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs.

TL;DR: The authors applied massively parallel single-cell RNA-seq to profile a developmental time course of interneuron development, measuring the transcriptomes of over 60,000 progenitors during their maturation in the ganglionic eminences and embryonic migration into the cortex.