A computational method to aid the design and analysis of single cell RNA-seq experiments for cell type identification

Autor: R. Krishna Murthy Karuturi, Parveen Kumar, Joshy George, Douglas Abrams
Rok vydání: 2019
Předmět:
Cell-type identification
Cell type
Computer science
Cell
RNA-Seq
Computational biology
lcsh:Computer applications to medicine. Medical informatics
Biochemistry
Clustering
Transcriptome
03 medical and health sciences
0302 clinical medicine
Downregulation and upregulation
Structural Biology
Databases
Genetic
medicine
Humans
Computer Simulation
Cluster analysis
lcsh:QH301-705.5
Gene
Molecular Biology
030304 developmental biology
0303 health sciences
Sequence Analysis
RNA
Research
Gene Expression Profiling
Applied Mathematics
RNA
Computational Biology
Single cell RNA-seq
Experimental design
Fold change
Computer Science Applications
Identification (information)
medicine.anatomical_structure
lcsh:Biology (General)
Analysis design
Research Design
Sample size determination
Sample Size
030220 oncology & carcinogenesis
lcsh:R858-859.7
Single-Cell Analysis
DNA microarray
Algorithms
Zdroj: BMC Bioinformatics
BMC Bioinformatics, Vol 20, Iss S11, Pp 1-6 (2019)
ISSN: 1471-2105
Popis: 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 than the rest of the genes, 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 fC 2 upregulated, choice of the single cell algorithm is dependent on the number of single cells isolated and proportion of rare cell type to be identified. In conclusion, our work allows the assessment of various single cell methods and also aids in examining the single cell experimental design.
Databáze: OpenAIRE
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