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.
Highlights
The advent of single cell RNA sequencing enabled researchers to study transcriptomic activity within individual cells and identify inherent cell types in the sample
According to Awesome Single Cell, a site that compiles a list of new single cell analysis methods, 118 methods have recently been created for analyzing single cell sequencing data, including plethora of methods required for cell type identification
For Kmeans clustering approach, k was set equal to the number of cell types simulated
Summary
The 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. 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. [4, 5] Does this first step provide valuable insights into the transcriptomic profiles of individual cell types and states, but it provides a deeper context for the subsequent differential expression analysis. According to Awesome Single Cell (https:// github.com/seandavi/awesome-single-cell), a site that compiles a list of new single cell analysis methods, 118 methods have recently been created for analyzing single cell sequencing data (normalization, dimensionality reduction, clustering and differential expression), including plethora of methods required for cell type identification. There has not been a comprehensive study, assessing whole pipelines and addressing broader issues of experimental design in cell type identification
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