scPipe experiment report
May 04 2018
Data summary
The organism is mmusculus_gene_ensembl, and gene id type is ensembl_gene_id.
Cell barcode statistics
Plot barcode match statistics in pie chart: 
Read alignment statistics
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`Summary and distributions of QC metrics
Datatable of all QC metrics:
## [1] "no ERCC spike-in. Skip `non_ERCC_percent`"Summary of all QC metrics:
Number of reads mapped to exon before UMI deduplication VS number of genes detected:
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`Quality control
Detect outlier cells
A robustified Mahalanobis Distance is calculated for each cell then outliers are detected based on the distance. However, due to the complex nature of single cell transcriptomes and protocol used, such a method can only be used to assist the quality control process. Visual inspection of the quality control metrics is still required. By default we use comp = 1 and the algorithm will try to separate the quality control metrics into two gaussian clusters.
The number of outliers:
## the following QC metrics not found in colData from sce:## non_mt_percentnon_ERCC_percent##
## FALSE TRUE
## 1303 21Pairwise plot for QC metrics, colored by outliers: 
Plot high expression genes
Remove low quality cells and plot highest expression genes.
## [1] "Number of NA in new gene id: 19818. Duplicated id: -0.5"## Note that the names of some metrics have changed, see 'Renamed metrics' in ?calculateQCMetrics.
## Old names are currently maintained for back-compatibility, but may be removed in future releases.
Remove low abundance genes
Plot the log10 average count for each gene: 
As a loose filter we keep genes that are expressed in at least two cells and for cells that express that gene, the average count larger than 1. This is not
## [1] 635 1303We have 635 genes left after removing low abundance genes.
Data normalization
Sample normalization
We perform normalization using scater and scran,
5-point summary of size factors:
## Warning in .computeSumFactors(assay(x, i = assay.type), subset.row =
## subset.row, : encountered negative size factor estimates## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.04311 0.22027 1.05746 0.99688 1.45756 4.09479PCA plot using gene expressions as input, colored by the number of genes.
## Warning in .disambiguate_args(...): non-plotting arguments like
## 'exprs_values' should go in 'run_args'
Normalize the data using size factor and get high variable genes
The highly variable genes are chosen based on trendVar from scran with FDR > 0.05 and biological variation larger than 0.5. If the number of highly variable genes is smaller than 100 we will select the top 100 genes by biological variation. If the number is larger than 500 we will only keep top 500 genes by biological variation.
Heatmap of high variable genes
Dimensionality reduction using high variable genes
Dimensionality reduction by PCA
## Warning in .disambiguate_args(...): non-plotting arguments like
## 'exprs_values' should go in 'run_args'
Dimensionality reduction by t-SNE
## Warning in .disambiguate_args(...): non-plotting arguments like
## 'exprs_values' should go in 'run_args'
## Warning in .disambiguate_args(...): non-plotting arguments like
## 'exprs_values' should go in 'run_args'
## Warning in .disambiguate_args(...): non-plotting arguments like
## 'exprs_values' should go in 'run_args'
Session information
## R version 3.5.0 (2018-04-23)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS High Sierra 10.13.3
##
## Matrix products: default
## BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] Rtsne_0.13 scran_1.8.0
## [3] scater_1.8.0 DT_0.4
## [5] plotly_4.7.1 readr_1.1.1
## [7] scales_0.5.0 Rsubread_1.30.0
## [9] scPipe_1.3.2 SingleCellExperiment_1.2.0
## [11] SummarizedExperiment_1.10.0 DelayedArray_0.6.0
## [13] BiocParallel_1.14.0 matrixStats_0.53.1
## [15] Biobase_2.40.0 GenomicRanges_1.32.0
## [17] GenomeInfoDb_1.16.0 IRanges_2.14.1
## [19] S4Vectors_0.18.1 BiocGenerics_0.26.0
## [21] ggplot2_2.2.1 stringr_1.3.0
## [23] magrittr_1.5
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-6 bit64_0.9-7
## [3] RColorBrewer_1.1-2 progress_1.1.2
## [5] httr_1.3.1 rprojroot_1.3-2
## [7] dynamicTreeCut_1.63-1 tools_3.5.0
## [9] backports_1.1.2 R6_2.2.2
## [11] vipor_0.4.5 DBI_1.0.0
## [13] lazyeval_0.2.1 colorspace_1.3-2
## [15] gridExtra_2.3 prettyunits_1.0.2
## [17] GGally_1.3.2 curl_3.2
## [19] bit_1.1-12 compiler_3.5.0
## [21] labeling_0.3 DEoptimR_1.0-8
## [23] robustbase_0.93-0 digest_0.6.15
## [25] rmarkdown_1.9 XVector_0.20.0
## [27] pkgconfig_2.0.1 htmltools_0.3.6
## [29] limma_3.36.0 htmlwidgets_1.2
## [31] rlang_0.2.0 RSQLite_2.1.0
## [33] FNN_1.1 shiny_1.0.5
## [35] DelayedMatrixStats_1.2.0 bindr_0.1.1
## [37] jsonlite_1.5 crosstalk_1.0.0
## [39] mclust_5.4 dplyr_0.7.4
## [41] RCurl_1.95-4.10 GenomeInfoDbData_1.1.0
## [43] Matrix_1.2-14 Rhdf5lib_1.2.0
## [45] ggbeeswarm_0.6.0 Rcpp_0.12.16
## [47] munsell_0.4.3 viridis_0.5.1
## [49] stringi_1.2.2 yaml_2.1.19
## [51] edgeR_3.22.0 MASS_7.3-50
## [53] zlibbioc_1.26.0 rhdf5_2.24.0
## [55] org.Hs.eg.db_3.6.0 plyr_1.8.4
## [57] grid_3.5.0 blob_1.1.1
## [59] promises_1.0.1 shinydashboard_0.7.0
## [61] lattice_0.20-35 hms_0.4.2
## [63] locfit_1.5-9.1 knitr_1.20
## [65] pillar_1.2.2 igraph_1.2.1
## [67] rjson_0.2.15 reshape2_1.4.3
## [69] biomaRt_2.36.0 XML_3.98-1.11
## [71] glue_1.2.0 evaluate_0.10.1
## [73] data.table_1.11.0 httpuv_1.4.1
## [75] org.Mm.eg.db_3.6.0 gtable_0.2.0
## [77] purrr_0.2.4 tidyr_0.8.0
## [79] reshape_0.8.7 assertthat_0.2.0
## [81] mime_0.5 xtable_1.8-2
## [83] later_0.7.2 viridisLite_0.3.0
## [85] tibble_1.4.2 beeswarm_0.2.3
## [87] AnnotationDbi_1.42.0 memoise_1.1.0
## [89] tximport_1.8.0 bindrcpp_0.2.2
## [91] statmod_1.4.30 Rhtslib_1.12.0