Day 1 // Hands-on 1 // Retina Dataset

Summary

• Published as Voigt et al., 2019

Explore using cellxgene

• https://cellxgene.cziscience.com/collections/7edef704-f63a-462c-8636-4bc86a9472bd

Label clusters

• cell_type > Use as color scale
• Show labels on the graph

Examine expression

• Genes > RHO > Use as color scale
• cell_type

Find differentially expressed genes

• Save current selection for differential expression computation (x2)
• See top 10 differentially expressed genes
• Gene Sets > Pop1 high
• Color by …

Load packages

• Introduce scater and tidyverse

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

library( "tidyverse" )

System has not been booted with systemd as init system (PID 1). Can't operate.
Failed to create bus connection: Host is down
running command 'timedatectl' had status 1

Load data

Seurat -> SingleCellExperiment

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

Check data

sce

class: SingleCellExperiment 
dim: 21599 8217 
metadata(0):
assays(1): logcounts
rownames(21599): FAM138A RP11-34P13.7 ... AC145205.1 AC145205.1-1
rowData names(1): hgnc_gene_symbol
colnames(8217): AAACCCAGTCGGCACT-1 AAACGAACAGGGACTA-1 ...
  TTTGGAGCAGTTTGGT-6 TTTGGAGTCGTTCTAT-6
colData names(18): final_cluster_labels libraryID ... cell_type
  ident
reducedDimNames(1): UMAP
altExpNames(0):

OSCA/scater functions

Visualize clusters

• Find help using help( plotUMAP )

plotUMAP( sce, colour_by="cell_type" )

Count cell_type

table( sce$cell_type )

                amacrine cell                bipolar neuron 
                           98                          2391 
             endothelial cell     foveal cone photoreceptor 
                          254                           236 
                   glial cell               microglial cell 
                         2413                           154 
                pericyte cell peripheral cone photoreceptor 
                          111                            68 
       retina horizontal cell         retinal ganglion cell 
                           82                           326 
             retinal rod cell                       unknown 
                         1590                           494 

Compare expression

• Google “ggplot rotate x axis”

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

Bioconductor SingleCellExperiment

Check class

class( sce )

[1] "SingleCellExperiment"
attr(,"package")
[1] "SingleCellExperiment"

Access counts

• http://bioconductor.org/books/release/OSCA/data-infrastructure.html#filling-the-assays-slot

m_retina <- assay( sce, "logcounts" )
class( m_retina )

[1] "matrix" "array" 

m_retina[ 1:5, 1:3 ]

              AAACCCAGTCGGCACT-1 AAACGAACAGGGACTA-1 AAACGAAGTTCTCCAC-1
FAM138A                        0           0.000000                  0
RP11-34P13.7                   0           0.000000                  0
AL627309.1                     0           0.000000                  0
AP006222.2                     0           1.443678                  0
RP4-669L17.10                  0           0.000000                  0

Access cell metadata

• http://bioconductor.org/books/release/OSCA/data-infrastructure.html#on-the-columns

df_cells <- colData( sce ) %>% data.frame()
df_cells

Access dimension reductions

• http://bioconductor.org/books/release/OSCA/data-infrastructure.html#dimensionality-reduction-results

df_umap <- reducedDim( sce, "UMAP" ) %>% data.frame()
df_umap

Document software

• Quit the current R session
• Run > Run All

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 8

Matrix products: default
BLAS:   /home/idies/R/lib64/R/lib/libRblas.so
LAPACK: /home/idies/R/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] forcats_0.5.1               stringr_1.4.0              
 [3] dplyr_1.0.7                 purrr_0.3.4                
 [5] readr_1.4.0                 tidyr_1.1.3                
 [7] tibble_3.1.1                tidyverse_1.3.1            
 [9] scater_1.18.6               ggplot2_3.3.3              
[11] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[13] Biobase_2.50.0              GenomicRanges_1.42.0       
[15] GenomeInfoDb_1.26.7         IRanges_2.24.1             
[17] S4Vectors_0.28.1            BiocGenerics_0.36.1        
[19] MatrixGenerics_1.2.1        matrixStats_0.58.0         
[21] SeuratObject_4.0.0          Seurat_4.0.1               

loaded via a namespace (and not attached):
  [1] readxl_1.3.1              backports_1.2.1          
  [3] plyr_1.8.6                igraph_1.2.6             
  [5] lazyeval_0.2.2            splines_4.0.3            
  [7] BiocParallel_1.24.1       listenv_0.8.0            
  [9] scattermore_0.7           digest_0.6.27            
 [11] htmltools_0.5.1.1         viridis_0.6.1            
 [13] fansi_0.4.2               magrittr_2.0.1           
 [15] tensor_1.5                cluster_2.1.0            
 [17] ROCR_1.0-11               globals_0.14.0           
 [19] modelr_0.1.8              spatstat.sparse_2.0-0    
 [21] colorspace_2.0-0          rvest_1.0.0              
 [23] ggrepel_0.9.1             haven_2.4.1              
 [25] xfun_0.22                 crayon_1.4.1             
 [27] RCurl_1.98-1.3            jsonlite_1.7.2           
 [29] spatstat.data_2.1-0       survival_3.2-7           
 [31] zoo_1.8-9                 glue_1.4.2               
 [33] polyclip_1.10-0           gtable_0.3.0             
 [35] zlibbioc_1.36.0           XVector_0.30.0           
 [37] leiden_0.3.7              DelayedArray_0.16.3      
 [39] BiocSingular_1.6.0        future.apply_1.7.0       
 [41] abind_1.4-5               scales_1.1.1             
 [43] DBI_1.1.1                 miniUI_0.1.1.1           
 [45] Rcpp_1.0.6                viridisLite_0.4.0        
 [47] xtable_1.8-4              reticulate_1.19          
 [49] spatstat.core_2.1-2       rsvd_1.0.5               
 [51] htmlwidgets_1.5.3         httr_1.4.2               
 [53] RColorBrewer_1.1-2        ellipsis_0.3.2           
 [55] ica_1.0-2                 farver_2.1.0             
 [57] pkgconfig_2.0.3           scuttle_1.0.4            
 [59] dbplyr_2.1.1              sass_0.3.1               
 [61] uwot_0.1.10               deldir_0.2-10            
 [63] utf8_1.2.1                labeling_0.4.2           
 [65] tidyselect_1.1.1          rlang_0.4.10             
 [67] reshape2_1.4.4            later_1.2.0              
 [69] cellranger_1.1.0          munsell_0.5.0            
 [71] tools_4.0.3               cli_2.5.0                
 [73] generics_0.1.0            broom_0.7.6              
 [75] ggridges_0.5.3            evaluate_0.14            
 [77] fastmap_1.1.0             yaml_2.2.1               
 [79] goftest_1.2-2             fs_1.5.0                 
 [81] knitr_1.33                fitdistrplus_1.1-3       
 [83] RANN_2.6.1                pbapply_1.4-3            
 [85] future_1.21.0             nlme_3.1-149             
 [87] sparseMatrixStats_1.2.1   mime_0.10                
 [89] xml2_1.3.2                rstudioapi_0.13          
 [91] compiler_4.0.3            beeswarm_0.3.1           
 [93] plotly_4.9.3              png_0.1-7                
 [95] spatstat.utils_2.1-0      reprex_2.0.0             
 [97] bslib_0.2.4               stringi_1.5.3            
 [99] lattice_0.20-41           Matrix_1.2-18            
[101] vctrs_0.3.8               pillar_1.6.0             
[103] lifecycle_1.0.0           spatstat.geom_2.1-0      
[105] lmtest_0.9-38             jquerylib_0.1.4          
[107] RcppAnnoy_0.0.18          BiocNeighbors_1.8.2      
[109] data.table_1.14.0         cowplot_1.1.1            
[111] bitops_1.0-7              irlba_2.3.3              
[113] httpuv_1.6.0              patchwork_1.1.0.9000     
[115] R6_2.5.0                  promises_1.2.0.1         
[117] KernSmooth_2.23-17        gridExtra_2.3            
[119] vipor_0.4.5               parallelly_1.25.0        
[121] codetools_0.2-16          MASS_7.3-53              
[123] assertthat_0.2.1          withr_2.4.2              
[125] sctransform_0.3.2         GenomeInfoDbData_1.2.4   
[127] hms_1.0.0                 mgcv_1.8-33              
[129] grid_4.0.3                rpart_4.1-15             
[131] beachmat_2.6.4            rmarkdown_2.7            
[133] DelayedMatrixStats_1.12.3 Rtsne_0.15               
[135] lubridate_1.7.10          shiny_1.6.0              
[137] ggbeeswarm_0.6.0