Selecting single cell clustering parameter values using subsampling-based robustness metrics

Selecting single cell clustering parameter values using subsampling-based robustness metrics

Background: Generating and analysing single-cell data has become a widespread approach to examine tissue heterogeneity, and numerous algorithms exist for clustering these datasets to identify putative cell types with shared transcriptomic signatures. However, many of these clustering workflows rely...

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Bibliographic Details
Main Authors: Levine, A.J (Author), Menon, V. (Author), Patterson-Cross, R.B (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
algorithm
Algorithms
animal experiment
animal tissue
article
benchmarking
Benchmarking
biology
cluster analysis
Cluster analysis
Cluster Analysis
Cluster qualities
Clustering
clustering algorithm
Clustering algorithms
data analysis
Data Analysis
gene expression profiling
Gene Expression Profiling
human
Identified parameter
Iterative clustering
Iterative methods
Leukocytes, Mononuclear
male
Mammals
mononuclear cell
mouse
nonhuman
Optimal parameter
Parameter selection
peripheral blood mononuclear cell
Resolution
Robustness metrics
single cell RNA seq
Single cell RNAseq
spinal cord
Spinal cords
Tissue heterogeneity
workflow
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