Lecture 6 : Single Cell
RNA-seq analysis
Modern Genomic Technologies  (LF:DSMGT01 )
Vojta Bystry
vojtech.bystry@ceitec.muni.cz

RNA-seq types
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●Bulk RNA-seq
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●SC-RNA-seq
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●Spatially resolved RNA-seq

Bulk RNA-seq vs. SC-RNA-seq
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●Bulk RNA-seq
○Several samples
○Difference in RNA levels between predetermined set of samples (conditions)
●SC-RNA-seq
○Hundreds to thousand cells
○Distinguish (cluster) cells based on the difference in RNA levels
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SC-RNA-seq primary analysis
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●In principle similar to the bulk RNA-seq
○Map to genomic reference, demultiplex and count reads per gene
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●For 10x Genomics tool from the company - Cell Ranger
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●Cell Ranger - report example

SC-RNA-seq primary analysis results
●per gene (feature) per cell read count
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Pre-processing workflow
●Filtering of cells based on QC metrics
●Detection and filtering of highly variable features
●Data normalization and scaling
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Filtering of cells QC
●Number of genes
●Number of reads
●% of mitochondrial RNA
○Sign of dying cells
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●Example filtering:
○filter cells that have unique feature counts over 2,500 or less than 200
○filter cells that have >5% mitochondrial counts

Identification of highly variable features



Determine the ‘dimensionality’ of the dataset
●How many clusters (cell types) is in the experiment (we want to explore)
●Analysis of PCA results
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●Better to choose higher value then smaller

Cluster the cells
●graph-based algorithms
●UMAP/tSNE visualization

Analysing clusters
●Finding differentially expressed features (cluster biomarkers)


Assigning cell type identity to clusters



Advanced analysis
●Cell state/development directions
○Pseudo-time
○Velocity

Pseudotime
●does not provide information about directionality of dynamics


Velocity
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www.ceitec.eu
CEITEC
@CEITEC_Brno
Vojta Bystry
vojtech.bystry@ceitec.muni.cz
Thank you for your attention!

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