We compared eight imputation methods, evaluated their power in recovering original real data, and performed broad analyses to explore their effects on clustering cell types, detecting differentially expressed genes, and reconstructing lineage trajectories in the context of both simulated and real data. Simulated datasets and case studies highlight that there are no one method performs the best in all the situations.

Comparison of methods to detect differentially expressed genes between single-cell populations

comparison of five statistical methods to detect differentially expressed genes between two distinct single-cell populations.

comparison of 36 statistical methods to detect differentially expressed genes between two annotated populations from the [conquer](http://imlspenticton.uzh.ch:3838/conquer/) database of consistently processed scRNA-seq datasets.

an assessment of main bulk and single-cell differential analysis methods used to analyze scRNA-seq data.

Unsure which of the more than 70 **trajectory inference** methods to use for your single-cell dataset? We evaluated 45 methods based on four criteria: the accuracy of the trajectory, how scalable the method is, how stable its outputs are, and the usability of the tool. These are summarised in a *"funky heatmap"* (Figures 2 & 3). Check out [dynverse.org](https://dynverse.org) for more information.

Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data

In this study, we benchmarked four methods (CIBERSORT, GSEA, GSVA, and ORA) for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used scRNA-seq datasets from liver, peripheral blood mononuclear cells and retinal neurons for which reference cell type gene expression signatures were available. Our results show that, in general, all four methods show a high performance in the task as evaluated by Receiver Operating Characteristic curve analysis (average AUC = 0.94, sd = 0.036), whereas Precision-Recall curve analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). CIBERSORT and GSVA were the top two performers.

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

a comparison of gene regulatory network inference methods using simulated and real single-cell RNA-seq datasets

In this article, nine tools have been systematically compared. The article provides a guideline for researchers to select and apply suitable single cell and cluster classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.

Accuracy, Robustness and Scalability of Dimensionality Reduction Methods for Single Cell RNAseq Analysis

Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data

On the discovery of population-specific state transitions from multi-sample multi-condition single-cell RNA sequencing data

A quantitative framework for evaluating single-cell data structure preservation by dimensionality reduction techniques

Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data

Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data

Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data

Our analyses suggest that bulk-based functional TF and pathways analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools (e.g. SCENIC, metaVIPER).

Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data

pipeComp, a general framework for the evaluation of computational pipelines, reveals performant single-cell RNA-seq preprocessing tools