Chapter 24 PBMC 10X dataset (unfiltered)

24.1 Introduction

Here, we describe a brief analysis of the peripheral blood mononuclear cell (PBMC) dataset from 10X Genomics (Zheng et al. 2017). The data are publicly available from the 10X Genomics website, from which we download the raw gene/barcode count matrices, i.e., before cell calling from the CellRanger pipeline.

24.2 Analysis code

24.2.4 Quality control

We use a relaxed QC strategy and only remove cells with large mitochondrial proportions, using it as a proxy for cell damage. This reduces the risk of removing cell types with low RNA content, especially in a heterogeneous PBMC population with many different cell types.

24.3 Results

24.3.4 Dimensionality reduction

## [1] 8

24.3.5 Clustering

## 
##   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18 
## 585 518 364 458 170 791 295 107  45  46 152  84  40  60 142  16  28  21

24.3.6 Interpretation

We examine the markers for cluster 7 in more detail. High expression of CD14, CD68 and MNDA combined with low expression of CD16 suggests that this cluster contains monocytes, compared to macrophages in cluster 12.

##                  p.value        FDR
## FCN1          4.882e-137 1.645e-132
## LGALS2        3.729e-133 6.282e-129
## CSTA          1.427e-131 1.603e-127
## CFD           1.207e-102  1.017e-98
## FGL2           8.567e-93  5.773e-89
## IFI30          7.823e-80  4.393e-76
## CLEC7A         6.052e-79  2.913e-75
## MS4A6A         1.958e-78  8.247e-75
## CFP            8.802e-73  3.295e-69
## S100A8         6.193e-70  2.087e-66
## LYZ            9.327e-70  2.857e-66
## LGALS3         1.496e-69  4.200e-66
## RP11-1143G9.4  1.673e-69  4.336e-66
## VCAN           2.661e-68  6.404e-65
## SERPINA1       5.716e-65  1.284e-61
## CPVL           1.373e-64  2.890e-61
## CD14           4.392e-61  8.704e-58
## S100A12        3.343e-59  6.257e-56
## TNFSF13B       7.416e-59  1.315e-55
## NAMPT          3.018e-57  5.084e-54
## CD302          2.232e-56  3.581e-53
## S100A9         2.213e-54  3.390e-51
## MNDA           7.045e-54  1.032e-50
## FCGRT          2.045e-53  2.871e-50
## IGSF6          3.575e-53  4.818e-50
## CD68           1.122e-52  1.454e-49
## AIF1           1.471e-52  1.835e-49
## NCF2           2.139e-52  2.574e-49
## MPEG1          4.167e-52  4.841e-49
## CEBPB          5.306e-51  5.789e-48

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] scran_1.14.0                EnsDb.Hsapiens.v86_2.99.0  
 [3] ensembldb_2.10.0            AnnotationFilter_1.10.0    
 [5] GenomicFeatures_1.38.0      AnnotationDbi_1.48.0       
 [7] scater_1.14.0               ggplot2_3.2.1              
 [9] DropletUtils_1.6.1          SingleCellExperiment_1.8.0 
[11] SummarizedExperiment_1.16.0 DelayedArray_0.12.0        
[13] BiocParallel_1.20.0         matrixStats_0.55.0         
[15] Biobase_2.46.0              GenomicRanges_1.38.0       
[17] GenomeInfoDb_1.22.0         IRanges_2.20.0             
[19] S4Vectors_0.24.0            BiocGenerics_0.32.0        
[21] BiocFileCache_1.10.0        dbplyr_1.4.2               
[23] Cairo_1.5-10                BiocStyle_2.14.0           
[25] 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] RSpectra_0.15-0          codetools_0.2-16        
 [9] R.methodsS3_1.7.1        knitr_1.25              
[11] zeallot_0.1.0            Rsamtools_2.2.0         
[13] R.oo_1.22.0              uwot_0.1.4              
[15] HDF5Array_1.14.0         BiocManager_1.30.9      
[17] compiler_3.6.1           httr_1.4.1              
[19] dqrng_0.2.1              backports_1.1.5         
[21] assertthat_0.2.1         Matrix_1.2-17           
[23] lazyeval_0.2.2           limma_3.42.0            
[25] BiocSingular_1.2.0       htmltools_0.4.0         
[27] prettyunits_1.0.2        tools_3.6.1             
[29] rsvd_1.0.2               igraph_1.2.4.1          
[31] gtable_0.3.0             glue_1.3.1              
[33] GenomeInfoDbData_1.2.2   dplyr_0.8.3             
[35] rappdirs_0.3.1           Rcpp_1.0.2              
[37] vctrs_0.2.0              Biostrings_2.54.0       
[39] rtracklayer_1.46.0       DelayedMatrixStats_1.8.0
[41] xfun_0.10                stringr_1.4.0           
[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                ProtGenerics_1.18.0     
[51] rhdf5_2.30.0             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] rlang_0.4.1              pkgconfig_2.0.3         
[61] bitops_1.0-6             evaluate_0.14           
[63] lattice_0.20-38          purrr_0.3.3             
[65] Rhdf5lib_1.8.0           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               FNN_1.1.3               
[87] digest_0.6.22            R.utils_2.9.0           
[89] openssl_1.4.1            RcppParallel_4.4.4      
[91] munsell_0.5.0            beeswarm_0.2.3          
[93] viridisLite_0.3.0        vipor_0.4.5             
[95] askpass_1.1             

Bibliography

Zheng, G. X., J. M. Terry, P. Belgrader, P. Ryvkin, Z. W. Bent, R. Wilson, S. B. Ziraldo, et al. 2017. “Massively parallel digital transcriptional profiling of single cells.” Nat Commun 8 (January):14049.