Day 1 // Exercise 1 // Retina Dataset

Summary

• Published as Voigt et al., 2019

Load packages

library( "Seurat" )
library( "scater" )

Load data

sce <- readRDS( "/home/idies/workspace/practical_genomics/day1/retina.rds" )
sce <- as.SingleCellExperiment( sce )

Check data

sce

Explore data

plotUMAP – metadata

plotUMAP( sce, colour_by="cell_type" )

plotUMAP – gene

plotUMAP( sce, colour_by="RHO" )

plotExpression – distribution

plotExpression( sce, "RHO", "cell_type" ) +
  theme( axis.text.x=element_text( angle=90 ) )

Task 1: Plot metadata

• Here are the available metadata categories

colnames( colData(sce) )

• Modify plotUMAP() to colour_by a different category

plotUMAP( sce, colour_by="" )

• Repeat to find one or two patterns you find interesting

plotUMAP()

Task 2: Plot gene

• Use this chunk to check the spelling of your gene of interest

goi <- "RHO"
table( goi %in% rownames(sce) )

• Modify plotUMAP() to plot your gene of interest

plotUMAP( sce, colour_by="RHO" )

• Repeat to find one or two patterns you find interesting

Task 3: Plot distributions

• Modify plotExpression() to compare expression across different categories

plotExpression( sce, "RHO", "libraryID" ) +
  theme( axis.text.x=element_text( angle=90 ) )

• Replace libraryID with your category to tabulate how many cells are in your category

table( colData(sce)$libraryID )

• Repeat to find one or two patterns you find interesting

Document software

sessionInfo()