A comprehensive evaluation of module detection methods for gene expression data

A critical step in the analysis of large genome-wide gene expression datasets is the use of module detection methods to group genes into co-expression modules. Because of limitations of classical clustering methods, numerous alternative module detection methods have been proposed, which improve upon clustering by handling co-expression in only a subset of samples, modelling the regulatory network, and/or allowing overlap between modules. In this study we use known regulatory networks to do a comprehensive and robust evaluation of these different methods. Overall, decomposition methods outperform all other strategies, while we do not find a clear advantage of biclustering and network inference-based approaches on large gene expression datasets. Using our evaluation workflow, we also investigate several practical aspects of module detection, such as parameter estimation and the use of alternative similarity measures, and conclude with recommendations for the further development of these methods

TinGa : fast and flexible trajectory inference with Growing Neural Gas

Motivation: During the last decade, trajectory inference (TI) methods have emerged as a novel framework to model cell developmental dynamics, most notably in the area of single-cell transcriptomics. At present, more than 70 TI methods have been published, and recent benchmarks showed that even state-of-the-art methods only perform well for certain trajectory types but not others. Results: In this work, we present TinGa, a new TI model that is fast and flexible, and that is based on Growing Neural Graphs. We performed an extensive comparison of TinGa to five state-of-the-art methods for TI on a set of 250 datasets, including both synthetic as well as real datasets. Overall, TinGa improves the state-of-the-art by producing accurate models (comparable to or an improvement on the state-of-the-art) on the whole spectrum of data complexity, from the simplest linear datasets to the most complex disconnected graphs. In addition, TinGa obtained the fastest execution times, showing that our method is thus one of the most versatile methods up to date

Trajectory-based differential expression analysis for single-cell sequencing data

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data. Downstream of trajectory inference for cell lineages based on scRNA-seq data, differential expression analysis yields insight into biological processes. Here, Van den Berge et al. develop tradeSeq, a framework for the inference of within and between-lineage differential expression, based on negative binomial generalized additive models

Essential guidelines for computational method benchmarking

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology.Comment: Minor update

Essential guidelines for computational method benchmarking

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology

The transcription factor Zeb2 regulates development of conventional and plasmacytoid DCs by repressing Id2

Plasmacytoid dendritic cells (DCs [pDCs]) develop from pre-pDCs, whereas two lineages of conventional DCs (cDCs; cDC1s and cDC2s) develop from lineage-committed pre-cDCs. Several transcription factors (TFs) have been implicated in regulating the development of pDCs (E2-2 and Id2) and cDC1s (Irf8, Id2, and Batf3); however, those required for the early commitment of pre-cDCs toward the cDC2 lineage are unknown. Here, we identify the TF zinc finger E box-binding homeobox 2 (Zeb2) to play a crucial role in regulating DC development. Zeb2 was expressed from the pre-pDC and pre-cDC stage onward and highly expressed in mature pDCs and cDC2s. Mice conditionally lacking Zeb2 in CD11c(+) cells had a cell-intrinsic reduction in pDCs and cDC2s, coupled with an increase in cDC1s. Conversely, mice in which CD11c(+) cells overexpressed Zeb2 displayed a reduction in cDC1s. This was accompanied by altered expression of Id2, which was up-regulated in cDC2s and pDCs from conditional knock-out mice. Zeb2 chromatin immunoprecipitation analysis revealed Id2 to be a direct target of Zeb2. Thus, we conclude that Zeb2 regulates commitment to both the cDC2 and pDC lineages through repression of Id2