Part 4
Last updated: 2019-08-08
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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)
#read in the 5k pmbc data we created before
pbmc<- readRDS("data/pbmc5k/pbmc_5k_v3.rds")
# Seurat object
pbmcAn object of class Seurat
18791 features across 4595 samples within 1 assay
Active assay: RNA (18791 features)
3 dimensional reductions calculated: pca, umap, tsnepbmc.sce <- as.SingleCellExperiment(pbmc)
# SingleCellExperiment object
pbmc.sceclass: 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