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Knit directory: scRNA-seq-workshop-Fall-2019/

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Interactive visulization with iSEE package

We primary used Seurat package to work with the single-cell data. However, there are a lot of other cool bioconductor packages to work with single-cell data as well. In this section, I will introduce you to iSEE package which provides cool interactive visulization for the single-cell data sets. In fact, there are a suite of packages that work with single-cell data in the bioconductor ecosystem. Please read https://osca.bioconductor.org/ if you are interested.

iSEE is a bioconductor package and it works with the SingleCellExperiment object not the Seurat object. Let’s convert it first.

More converion examples can be found https://satijalab.org/seurat/v3.0/conversion_vignette.html

library(Seurat)
library(iSEE)

Loading required package: SummarizedExperiment

Loading required package: GenomicRanges

Loading required package: stats4

Loading required package: BiocGenerics

Loading required package: parallel


Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    anyDuplicated, append, as.data.frame, basename, cbind,
    colMeans, colnames, colSums, dirname, do.call, duplicated,
    eval, evalq, Filter, Find, get, grep, grepl, intersect,
    is.unsorted, lapply, lengths, Map, mapply, match, mget, order,
    paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind,
    Reduce, rowMeans, rownames, rowSums, sapply, setdiff, sort,
    table, tapply, union, unique, unsplit, which, which.max,
    which.min

Loading required package: S4Vectors


Attaching package: 'S4Vectors'

The following object is masked from 'package:base':

    expand.grid

Loading required package: IRanges

Loading required package: GenomeInfoDb

Loading required package: Biobase

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: DelayedArray

Loading required package: matrixStats


Attaching package: 'matrixStats'

The following objects are masked from 'package:Biobase':

    anyMissing, rowMedians

Loading required package: BiocParallel


Attaching package: 'DelayedArray'

The following objects are masked from 'package:matrixStats':

    colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges

The following objects are masked from 'package:base':

    aperm, apply

Loading required package: SingleCellExperiment

#read in the 5k pmbc data we created before
pbmc<- readRDS("data/pbmc5k/pbmc_5k_v3.rds")
# Seurat object
pbmc

An object of class Seurat 
18791 features across 4595 samples within 1 assay 
Active assay: RNA (18791 features)
 3 dimensional reductions calculated: pca, umap, tsne

pbmc.sce <- as.SingleCellExperiment(pbmc)
# SingleCellExperiment object
pbmc.sce

class: SingleCellExperiment 
dim: 18791 4595 
metadata(0):
assays(2): counts logcounts
rownames(18791): AL627309.1 AL627309.3 ... AL354822.1 AC240274.1
rowData names(5): vst.mean vst.variance vst.variance.expected
  vst.variance.standardized vst.variable
colnames(4595): AAACCCAAGCGTATGG AAACCCAGTCCTACAA ...
  TTTGTTGTCCTTGGAA TTTGTTGTCGCACGAC
colData names(7): orig.ident nCount_RNA ... seurat_clusters ident
reducedDimNames(3): PCA UMAP TSNE
spikeNames(0):

## feed to iSEE
iSEE(pbmc.sce)

It opens the Shiny Application and now we are ready to do some interactive exploration of the data set!

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] iSEE_1.2.4                  SingleCellExperiment_1.4.0 
 [3] SummarizedExperiment_1.12.0 DelayedArray_0.8.0         
 [5] BiocParallel_1.16.2         matrixStats_0.54.0         
 [7] Biobase_2.42.0              GenomicRanges_1.34.0       
 [9] GenomeInfoDb_1.18.1         IRanges_2.16.0             
[11] S4Vectors_0.20.1            BiocGenerics_0.28.0        
[13] Seurat_3.0.2               

loaded via a namespace (and not attached):
  [1] Rtsne_0.15             colorspace_1.4-1       ggridges_0.5.1        
  [4] rprojroot_1.3-2        XVector_0.22.0         fs_1.2.6              
  [7] listenv_0.7.0          npsurv_0.4-0           DT_0.5                
 [10] bit64_0.9-7            ggrepel_0.8.0          AnnotationDbi_1.44.0  
 [13] codetools_0.2-16       splines_3.5.1          R.methodsS3_1.7.1     
 [16] lsei_1.2-0             knitr_1.21             jsonlite_1.6          
 [19] workflowr_1.4.0        ica_1.0-2              cluster_2.0.7-1       
 [22] png_0.1-7              R.oo_1.22.0            shinydashboard_0.7.1  
 [25] shiny_1.2.0            sctransform_0.2.0      rentrez_1.2.2         
 [28] compiler_3.5.1         httr_1.4.0             backports_1.1.3       
 [31] assertthat_0.2.0       Matrix_1.2-15          lazyeval_0.2.1        
 [34] later_0.7.5            htmltools_0.3.6        tools_3.5.1           
 [37] rsvd_1.0.0             igraph_1.2.2           gtable_0.2.0          
 [40] glue_1.3.0             GenomeInfoDbData_1.2.0 RANN_2.6              
 [43] reshape2_1.4.3         dplyr_0.8.0.1          Rcpp_1.0.0            
 [46] gdata_2.18.0           ape_5.2                nlme_3.1-137          
 [49] rintrojs_0.2.2         gbRd_0.4-11            lmtest_0.9-36         
 [52] xfun_0.4               stringr_1.3.1          globals_0.12.4        
 [55] mime_0.6               miniUI_0.1.1.1         irlba_2.3.2           
 [58] gtools_3.8.1           XML_3.98-1.16          shinyAce_0.4.0        
 [61] future_1.10.0          MASS_7.3-51.1          zlibbioc_1.28.0       
 [64] zoo_1.8-4              scales_1.0.0           colourpicker_1.0      
 [67] promises_1.0.1         RColorBrewer_1.1-2     yaml_2.2.0            
 [70] memoise_1.1.0          reticulate_1.10        pbapply_1.3-4         
 [73] gridExtra_2.3          ggplot2_3.1.0          RSQLite_2.1.1         
 [76] stringi_1.2.4          caTools_1.17.1.1       bibtex_0.4.2          
 [79] Rdpack_0.10-1          SDMTools_1.1-221       rlang_0.3.1           
 [82] pkgconfig_2.0.2        bitops_1.0-6           evaluate_0.12         
 [85] lattice_0.20-38        ROCR_1.0-7             purrr_0.2.5           
 [88] htmlwidgets_1.3        bit_1.1-14             cowplot_0.9.3         
 [91] tidyselect_0.2.5       plyr_1.8.4             magrittr_1.5          
 [94] R6_2.3.0               gplots_3.0.1           DBI_1.0.0             
 [97] mgcv_1.8-26            pillar_1.3.1           whisker_0.3-2         
[100] fitdistrplus_1.0-11    survival_2.43-3        RCurl_1.95-4.11       
[103] tibble_2.0.1           future.apply_1.0.1     tsne_0.1-3            
[106] crayon_1.3.4           KernSmooth_2.23-15     plotly_4.8.0          
[109] rmarkdown_1.11         grid_3.5.1             data.table_1.11.8     
[112] blob_1.1.1             git2r_0.23.0           metap_1.0             
[115] digest_0.6.18          xtable_1.8-3           httpuv_1.4.5.1        
[118] tidyr_0.8.2            R.utils_2.7.0          munsell_0.5.0         
[121] viridisLite_0.3.0      vipor_0.4.5            shinyjs_1.0

Part 4

Interactive visulization with iSEE package