ArticleBMC bioinformatics2019
A computational method to aid the design and analysis of single cell RNA-seq experiments for cell type identification.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Single-cell and spatial profiling in cancer biology and clinical oncology.Nature cancer · 2026Review
- Single-Cell and Bulk RNA Sequencing Highlights Intra-Tumoral Heterogeneity and Malignant Progression Mechanisms in Prostate Cancer.Journal of cellular and molecular medicine · 2025Article
- IL-23 Promotes γδT Cell Activity in Dry Eye Disease Progression.Investigative ophthalmology & visual science · 2025Article
- Identification and validation of a pyroptosis-related prognostic model for colorectal cancer based on bulk and single-cell RNA sequencing data.World journal of clinical oncology · 2024Article
- An insight into planarian regeneration.Cell proliferation · 2022Review
- scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies.Nature communications · 2021Article
- Identification of Specific Cell Subpopulations and Marker Genes in Ovarian Cancer Using Single-Cell RNA Sequencing.BioMed research international · 2021Article
- Single-cell RNA-seq clustering: datasets, models, and algorithms.RNA biology · 2020Article
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4 authors.
Funding
Abstract
backgroundThe advent of single cell RNA sequencing (scRNA-seq) enabled researchers to study transcriptomic activity within individual cells and identify inherent cell types in the sample. Although numerous computational tools have been developed to analyze single cell transcriptomes, there are no published studies and analytical packages available to guide experimental design and to devise suitable analysis procedure for cell type identification.
resultsWe have developed an empirical methodology to address this important gap in single cell experimental design and analysis into an easy-to-use tool called SCEED (Single Cell Empirical Experimental Design and analysis). With SCEED, user can choose a variety of combinations of tools for analysis, conduct performance analysis of analytical procedures and choose the best procedure, and estimate sample size (number of cells to be profiled) required for a given analytical procedure at varying levels of cell type rarity and other experimental parameters. Using SCEED, we examined 3 single cell algorithms using 48 simulated single cell datasets that were generated for varying number of cell types and their proportions, number of genes expressed per cell, number of marker genes and their fold change, and number of single cells successfully profiled in the experiment.
conclusionsBased on our study, we found that when marker genes are expressed at fold change of 4 or more, either Seurat or SIMLR algorithm can be used to analyze single cell dataset for any number of single cells isolated (minimum 1000 single cells were tested). However, when marker genes are expected to be only up to fold change of 2, choice of the single cell algorithm is dependent on the number of single cells isolated and rarity of cell types to be identified. In conclusion, our work allows the assessment of various single cell methods and also aids in the design of single cell experiments.
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