Chapter 29 Human pancreas dataset (Lawlor)

29.1 Introduction

This performs an analysis of the Lawlor et al. (2017) dataset, consisting of human pancreas cells from various donors.

29.3 Results

29.3.4 Clustering

##     
##      Acinar Alpha Beta Delta Ductal Gamma/PP None/Other Stellate
##   1       0     1    0     1      1        0          5       19
##   2       0     0  216     2      1        0          1        0
##   3       0     0    0     2     20        0          3        0
##   4       1    17    4     1      0        0          4        0
##   5       0    93    1     0      0        1          0        0
##   6      22     0    0     0      0        0          0        0
##   7       0   128    0     0      0        0          1        0
##   8       0     0    0    17      0        0          0        0
##   9       0     0   35     0      0        0          0        0
##   10      0     0    0     0      1       17          0        0
##     
##      ACCG268 ACCR015A ACEK420A ACEL337 ACHY057 ACIB065 ACIW009 ACJV399
##   1        7        1        1       1       0       4       9       4
##   2       41       13        7      46      10      24      28      51
##   3        1        1        4       1       2       1      12       3
##   4        1        3       14       2       0       1       4       2
##   5       39       17        1       5       8       7       5      13
##   6        0        2       13       0       0       0       5       2
##   7       30       16        1      12      12      14      17      27
##   8        4        3        3       2       2       1       1       1
##   9        8        0        0      24       1       0       0       2
##   10       4        0        0       2       3       3       6       0

Session Info

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 14.04.5 LTS

Matrix products: default
BLAS:   /home/ramezqui/Rbuild/danbuild/R-3.6.1/lib/libRblas.so
LAPACK: /home/ramezqui/Rbuild/danbuild/R-3.6.1/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
 [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] BiocSingular_1.2.0          scran_1.14.0               
 [3] scater_1.14.0               ggplot2_3.2.1              
 [5] ensembldb_2.10.0            AnnotationFilter_1.10.0    
 [7] GenomicFeatures_1.38.0      AnnotationDbi_1.48.0       
 [9] AnnotationHub_2.18.0        BiocFileCache_1.10.0       
[11] dbplyr_1.4.2                scRNAseq_1.99.8            
[13] SingleCellExperiment_1.8.0  SummarizedExperiment_1.16.0
[15] DelayedArray_0.12.0         BiocParallel_1.20.0        
[17] matrixStats_0.55.0          Biobase_2.46.0             
[19] GenomicRanges_1.38.0        GenomeInfoDb_1.22.0        
[21] IRanges_2.20.0              S4Vectors_0.24.0           
[23] BiocGenerics_0.32.0         Cairo_1.5-10               
[25] BiocStyle_2.14.0            OSCAUtils_0.0.1            

loaded via a namespace (and not attached):
 [1] Rtsne_0.15                    ggbeeswarm_0.6.0             
 [3] colorspace_1.4-1              XVector_0.26.0               
 [5] BiocNeighbors_1.4.0           bit64_0.9-7                  
 [7] interactiveDisplayBase_1.24.0 codetools_0.2-16             
 [9] knitr_1.25                    zeallot_0.1.0                
[11] Rsamtools_2.2.0               shiny_1.4.0                  
[13] BiocManager_1.30.9            compiler_3.6.1               
[15] httr_1.4.1                    dqrng_0.2.1                  
[17] backports_1.1.5               assertthat_0.2.1             
[19] Matrix_1.2-17                 fastmap_1.0.1                
[21] lazyeval_0.2.2                limma_3.42.0                 
[23] later_1.0.0                   htmltools_0.4.0              
[25] prettyunits_1.0.2             tools_3.6.1                  
[27] rsvd_1.0.2                    igraph_1.2.4.1               
[29] gtable_0.3.0                  glue_1.3.1                   
[31] GenomeInfoDbData_1.2.2        dplyr_0.8.3                  
[33] rappdirs_0.3.1                Rcpp_1.0.2                   
[35] vctrs_0.2.0                   Biostrings_2.54.0            
[37] ExperimentHub_1.12.0          rtracklayer_1.46.0           
[39] DelayedMatrixStats_1.8.0      xfun_0.10                    
[41] stringr_1.4.0                 mime_0.7                     
[43] irlba_2.3.3                   statmod_1.4.32               
[45] XML_3.98-1.20                 edgeR_3.28.0                 
[47] zlibbioc_1.32.0               scales_1.0.0                 
[49] hms_0.5.2                     promises_1.1.0               
[51] ProtGenerics_1.18.0           yaml_2.2.0                   
[53] curl_4.2                      memoise_1.1.0                
[55] gridExtra_2.3                 biomaRt_2.42.0               
[57] stringi_1.4.3                 RSQLite_2.1.2                
[59] BiocVersion_3.10.1            rlang_0.4.1                  
[61] pkgconfig_2.0.3               bitops_1.0-6                 
[63] evaluate_0.14                 lattice_0.20-38              
[65] purrr_0.3.3                   labeling_0.3                 
[67] GenomicAlignments_1.22.0      cowplot_1.0.0                
[69] bit_1.1-14                    tidyselect_0.2.5             
[71] magrittr_1.5                  bookdown_0.14                
[73] R6_2.4.0                      DBI_1.0.0                    
[75] pillar_1.4.2                  withr_2.1.2                  
[77] RCurl_1.95-4.12               tibble_2.1.3                 
[79] crayon_1.3.4                  rmarkdown_1.16               
[81] viridis_0.5.1                 progress_1.2.2               
[83] locfit_1.5-9.1                grid_3.6.1                   
[85] blob_1.2.0                    digest_0.6.22                
[87] xtable_1.8-4                  httpuv_1.5.2                 
[89] openssl_1.4.1                 munsell_0.5.0                
[91] beeswarm_0.2.3                viridisLite_0.3.0            
[93] vipor_0.4.5                   askpass_1.1                  

Bibliography

Lawlor, N., J. George, M. Bolisetty, R. Kursawe, L. Sun, V. Sivakamasundari, I. Kycia, P. Robson, and M. L. Stitzel. 2017. “Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes.” Genome Res. 27 (2):208–22.