• Analysis of total counts from processing pipeline
• Descriptive statistics
• Diminishing returns of sequencing depth
• Exploratory analysis with PCA
• Correlation of covariates with PCs of highly variable genes
• ERCC capture efficiency
• Identifying single cells
• Pairwise correlations
• Paritioning variance across different factors
• Sample quality control
• Filter genes
• Gene set and pathway overdispersion analysis
• Dimensionality reduction
• Identification of noisy genes
• UMI count distribution stratified by genotype
• Single cell/bulk RNA-Seq concordance
• Power to detect QTLs in single cell data
• Mean/dispersion estimation
• QTL mapping pipeline
• Examples of single cell measurement error

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