This repository contains the real and synthetic datasets used in the paper "Benchmarking the Autoencoder Design for Imputing Single-Cell RNA Sequencing Data". The zip file includes three folders: 1. overall imputation accuracy: the 12 real scRNA-seq datasets used in the evaluation of overall imputation accuracy. 2. cell clustering: the 20 real scRNA-seq datasets with cell type labels used in the evaluation of cell clustering. 3. DE gene: the 20 scRNA-seq syntehtic datasets with ground-truth DE genes used in the evaluation of DE gene analysis. These datasets are simulated by simulator scDesign and 20 real datasets.

This repository contains the real and synthetic datasets used in the paper "Benchmarking the Autoencoder Design for Imputing Single-Cell RNA Sequencing Data". The zip file includes three folders: 1. overall imputation accuracy: the 12 real scRNA-seq datasets used in the evaluation of overall imputation accuracy. 2. cell clustering: the 20 real scRNA-seq datasets with cell type labels used in the evaluation of cell clustering. 3. DE gene: the 20 scRNA-seq syntehtic datasets with ground-truth DE genes used in the evaluation of DE gene analysis. These datasets are simulated by simulator scDesign and 20 real datasets.

This repository contains the real and synthetic datasets used in the paper "Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data" and "Protocol for Benchmarking Computational Doublet-Detection Methods in Single-Cell RNA Sequencing Data Analysis". Please check the full text published on Cell Systems and STAR Protocols. 1. real_datasets.zip: 16 real scRNA-seq datasets with experimentally annotated doublets. This collection covers a variety of cell types, droplet and gene numbers, doublet rates, and sequencing depths. It represents varying levels of difficulty in detecting doublets from scRNA-seq data. The data collection and preprocessing details are described in our Cell System paper. The name of each file corresponds to the names in the paper. 2. synthetic_datasets.zip: synthetic datasets used in the paper, including datasets with varying doublet rates (i.e., percentages of doublets among all droplets), sequencing depths, cell types, and between-cell-type heterogeneity levels. The synthetic datasets contain ground-truth doublets, cell types, differentially expressed (DE) genes, and cell trajectories. The simulation details are described in our Cell System paper. 3. A detailed description on how to use these datasets is available at our STAR Protocols paper

This repository contains the real and synthetic datasets used in the paper "Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data" and "Protocol for Benchmarking Computational Doublet-Detection Methods in Single-Cell RNA Sequencing Data Analysis". Please check the full text published on Cell Systems and STAR Protocols. 1. real_datasets.zip: 16 real scRNA-seq datasets with experimentally annotated doublets. This collection covers a variety of cell types, droplet and gene numbers, doublet rates, and sequencing depths. It represents varying levels of difficulty in detecting doublets from scRNA-seq data. The data collection and preprocessing details are described in our Cell System paper. The name of each file corresponds to the names in the paper. 2. synthetic_datasets.zip: synthetic datasets used in the paper, including datasets with varying doublet rates (i.e., percentages of doublets among all droplets), sequencing depths, cell types, and between-cell-type heterogeneity levels. The synthetic datasets contain ground-truth doublets, cell types, differentially expressed (DE) genes, and cell trajectories. The simulation details are described in our Cell System paper. 3. A detailed description on how to use these datasets is available at our STAR Protocols paper

This repository contains the real and simulation datasets used in the paper "Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data". Please check the full text published on Cell Systems or our preprint. 1. real_datasets.zip: 16 real scRNA-seq datasets with experimentally annotated doublets. The name of each file corresponds to the names in the benchmark paper. 2. simulation_datasets.zip: simulation datasets used in the benchmark, including different experimental conditions, scalability, stability, running time, and the effects of doublet detection on DE gene analysis, highly variable gene identification, cell clustering, and trajectory inference. 3. result.xlsx: a tabular file that saves benchmarking results, including AUPRC, AUROC, precision, recall, TNR, and cell clustering. It is also the data source for drawing figures in the paper "Protocol for Benchmarking Computational Doublet-Detection Methods in Single-Cell RNA Sequencing Data Analysis".

This repository contains the real and simulation datasets used in the paper 'Benchmarking computational doublet-detection methods for single-cell RNA sequencing data'. The preprint can be found at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3646565. 1. real_datasets.zip: 16 real scRNA-seq datasets with experimentally annotated doublets. The name of each file corresponds to the names in the benchmark paper. 2. simulation_datasets.zip: simulation datasets used in the benchmark, including different experimental conditions, scalability, stability, running time, and the impact of doublet detection on clustering, DE, HVGs, and trajectory inference.