single cell 10x single-cell analysis - part3
UC Davis Bioinformatics Core
Load the Seurat object
load(file="pre_sample_corrected.RData")
experiment.aggregate## Loading required package: Seurat## Loading required package: ggplot2## Loading required package: cowplot##
## Attaching package: 'cowplot'## The following object is masked from 'package:ggplot2':
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## ggsave## Loading required package: Matrix## An object of class seurat in project scRNA workshop
## 11454 genes across 21288 samples.Exploring Batch effects 3 ways, none, Seurat [vars.to.regress] and COMBAT
First lets view the data without any corrections
PCA in prep for tSNE
ScaleData - Scales and centers genes in the dataset.
## [1] "Scaling data matrix"
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|=================================================================| 100%Run PCA 
TSNEPlot 
Correct for sample to sample differences (seurat)
Use vars.to.regress to correct for the sample to sample differences and percent mitochondria
## [1] "Regressing out orig.ident" "Regressing out percent.mito"
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## [1] "Scaling data matrix"
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Corrected TSE Plot 
COMBAT corrected, https://academic.oup.com/biostatistics/article-lookup/doi/10.1093/biostatistics/kxj037
source("https://bioconductor.org/biocLite.R")## Bioconductor version 3.6 (BiocInstaller 1.28.0), ?biocLite for helpbiocLite("sva")## BioC_mirror: https://bioconductor.org## Using Bioconductor 3.6 (BiocInstaller 1.28.0), R 3.4.4 (2018-03-15).## Installing package(s) 'sva'##
## The downloaded binary packages are in
## /var/folders/vv/3xsj6xdd3j7828czt77bcltr0000gn/T//RtmpiBV51F/downloaded_packageslibrary(sva)## Loading required package: mgcv## Loading required package: nlme## This is mgcv 1.8-23. For overview type 'help("mgcv-package")'.## Loading required package: genefilter## Loading required package: BiocParallel?ComBat
m = as.data.frame(as.matrix(experiment.aggregate@data))
com = ComBat(dat=m, batch=experiment.aggregate@meta.data$orig.ident, prior.plots=FALSE, par.prior=TRUE)## Found3batches## Adjusting for0covariate(s) or covariate level(s)## Standardizing Data across genes## Fitting L/S model and finding priors## Finding parametric adjustments## Adjusting the Dataexperiment.aggregate.combat <- experiment.aggregate
experiment.aggregate.combat@data = Matrix(as.matrix(com))
experiment.aggregate.combat = ScaleData(experiment.aggregate.combat)## [1] "Scaling data matrix"
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|=================================================================| 100%experiment.aggregate.combat <- RunPCA(object = experiment.aggregate.combat, pc.genes = experiment.aggregate.combat@var.genes, do.print = FALSE, pcs.compute = 40, weight.by.var = FALSE)
PCAPlot(object = experiment.aggregate.combat, dim.1 = 1, dim.2 = 2)
experiment.aggregate.combat <- RunTSNE(object = experiment.aggregate.combat, dims.use = 1:12, do.fast = TRUE)
TSNEPlot(object = experiment.aggregate.combat)
Question(s)
- Explore differences in regressed variables. eg remove percent.mito, add in nUMI?
Session Information
sessionInfo()## R version 3.4.4 (2018-03-15)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS High Sierra 10.13.3
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/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] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] sva_3.26.0 BiocParallel_1.12.0 genefilter_1.60.0
## [4] mgcv_1.8-23 nlme_3.1-131.1 BiocInstaller_1.28.0
## [7] Seurat_2.2.1 Matrix_1.2-12 cowplot_0.9.2
## [10] ggplot2_2.2.1
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## loaded via a namespace (and not attached):
## [1] backports_1.1.2 Hmisc_4.1-1 VGAM_1.0-5
## [4] sn_1.5-1 plyr_1.8.4 igraph_1.2.1
## [7] lazyeval_0.2.1 splines_3.4.4 digest_0.6.15
## [10] foreach_1.4.4 htmltools_0.3.6 lars_1.2
## [13] gdata_2.18.0 memoise_1.1.0 magrittr_1.5
## [16] checkmate_1.8.5 cluster_2.0.6 mixtools_1.1.0
## [19] ROCR_1.0-7 sfsmisc_1.1-2 limma_3.34.9
## [22] recipes_0.1.2 annotate_1.56.1 gower_0.1.2
## [25] matrixStats_0.53.1 dimRed_0.1.0 R.utils_2.6.0
## [28] colorspace_1.3-2 blob_1.1.0 dplyr_0.7.4
## [31] RCurl_1.95-4.10 bindr_0.1.1 survival_2.41-3
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## [37] DRR_0.0.3 gtable_0.2.0 ipred_0.9-6
## [40] kernlab_0.9-25 ddalpha_1.3.1.1 prabclus_2.2-6
## [43] BiocGenerics_0.24.0 DEoptimR_1.0-8 scales_0.5.0
## [46] mvtnorm_1.0-7 DBI_0.8 Rcpp_0.12.16
## [49] metap_0.8 dtw_1.18-1 xtable_1.8-2
## [52] htmlTable_1.11.2 tclust_1.3-1 bit_1.1-12
## [55] foreign_0.8-69 proxy_0.4-21 mclust_5.4
## [58] SDMTools_1.1-221 Formula_1.2-2 stats4_3.4.4
## [61] tsne_0.1-3 lava_1.6 prodlim_1.6.1
## [64] htmlwidgets_1.0 FNN_1.1 gplots_3.0.1
## [67] RColorBrewer_1.1-2 fpc_2.1-11 acepack_1.4.1
## [70] modeltools_0.2-21 ica_1.0-1 XML_3.98-1.10
## [73] pkgconfig_2.0.1 R.methodsS3_1.7.1 flexmix_2.3-14
## [76] nnet_7.3-12 caret_6.0-78 AnnotationDbi_1.40.0
## [79] tidyselect_0.2.4 labeling_0.3 rlang_0.2.0
## [82] reshape2_1.4.3 munsell_0.4.3 tools_3.4.4
## [85] RSQLite_2.0 ranger_0.9.0 broom_0.4.3
## [88] ggridges_0.4.1 evaluate_0.10.1 stringr_1.3.0
## [91] yaml_2.1.18 bit64_0.9-7 ModelMetrics_1.1.0
## [94] knitr_1.20 robustbase_0.92-8 caTools_1.17.1
## [97] purrr_0.2.4 bindrcpp_0.2 pbapply_1.3-4
## [100] R.oo_1.21.0 RcppRoll_0.2.2 compiler_3.4.4
## [103] rstudioapi_0.7 tibble_1.4.2 stringi_1.1.7
## [106] lattice_0.20-35 trimcluster_0.1-2 psych_1.7.8
## [109] diffusionMap_1.1-0 pillar_1.2.1 data.table_1.10.4-3
## [112] bitops_1.0-6 irlba_2.3.2 R6_2.2.2
## [115] latticeExtra_0.6-28 KernSmooth_2.23-15 gridExtra_2.3
## [118] IRanges_2.12.0 codetools_0.2-15 MASS_7.3-49
## [121] gtools_3.5.0 assertthat_0.2.0 CVST_0.2-1
## [124] rprojroot_1.3-2 withr_2.1.2 mnormt_1.5-5
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## [130] grid_3.4.4 rpart_4.1-13 timeDate_3043.102
## [133] tidyr_0.8.0 class_7.3-14 rmarkdown_1.9
## [136] segmented_0.5-3.0 Rtsne_0.13 numDeriv_2016.8-1
## [139] scatterplot3d_0.3-41 Biobase_2.38.0 lubridate_1.7.3
## [142] base64enc_0.1-3