R Notebook
Last updated: 2018-11-02
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Notebook for alignment analsyis of the 180504 data.
library(Seurat)Loading required package: ggplot2Loading required package: cowplot
Attaching package: 'cowplot'The following object is masked from 'package:ggplot2':
ggsaveLoading required package: Matrixlibrary(ggplot2)load('output/10x-180504-aligned-metageneplot')
all10x.aligned <- readRDS('output/10x-180504-aligned')
all10x.aligned.ccregout <- readRDS('output/10x-180504-ccregout-aligned')
all10x.aligned.discardedcells <- readRDS('output/10x-180504-cca-discardedcells')
all10x.ccregout.aligned.discardedcells <- readRDS('output/10x-180504-ccregout-cca-discardedcells')Alignment of the data with and without cell cycle effects regressed out. Both were aligned on 30 subspaces, tSNE was performed on the first 15 CCs.
Shared correlation per CC
Normal alignment
p1`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Alignment with cell cycle effects regressed out
p2`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Alignment without cell cycle effects regressed out
tSNE of the aligned data.
TSNEPlot(all10x.aligned, group.by='sample_name', pt.size=0.1)
tSNE of the aligned data coloured on cell cycle phase.
TSNEPlot(all10x.aligned, group.by='Phase', pt.size=0.1)
Alignment with cell cycle regressed out
tSNE of the aligned data with cell cycle effects regressed out.
TSNEPlot(all10x.aligned.ccregout, group.by='sample_name', pt.size=0.1)
tSNE of the aligned data with cell cycle effects regressed out, colored by phase.
TSNEPlot(all10x.aligned.ccregout, group.by='Phase', pt.size=0.1)
tSNE of the aligned data with cell cycle effects regressed out, colored by subtissue
TSNEPlot(all10x.aligned.ccregout, group.by='depot', pt.size=0.1)
Discarded cells from alignment
Before aligning the samples, sample-specific cells were discarded. Below: discarded cells from normal alignment.
discarded <- as.data.frame(table(all10x.aligned.discardedcells@meta.data$sample_name))
names(discarded) <- c('Sample', 'Frequency')
p_discarded <-ggplot(data=discarded, aes(x=Sample, y=Frequency)) +
geom_bar(stat="identity") +
theme(axis.text.x = element_text(angle=45, hjust=1))
p_discarded
Discarded cells from alignment with cell cycle effects regressed out.
discarded.ccregout <- as.data.frame(table(all10x.ccregout.aligned.discardedcells@meta.data$sample_name))
names(discarded.ccregout) <- c('Sample', 'Frequency')
p_discarded.ccregout <-ggplot(data=discarded.ccregout, aes(x=Sample, y=Frequency)) +
geom_bar(stat="identity") +
theme(axis.text.x = element_text(angle=45, hjust=1))
p_discarded.ccregout
Figures for report
fig1
Biweight midcorrelation plots.
sfig1 <- plot_grid(
p1,
p2,
labels=c('a', 'b'),
nrow=1
)`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using method = 'loess' and formula 'y ~ x'save_plot("plots/supplementary_figures/sfig_180504_biweightplots.pdf", sfig1, base_width=12, base_height=4)
sfig1
Discarded cells.
sfig2 <- plot_grid(
p_discarded,
p_discarded.ccregout,
labels=c('a', 'b'),
nrow=1
)
save_plot("plots/supplementary_figures/sfig_180504_alignment-discardedcells.pdf", sfig2, base_width=12, base_height=4)
sfig2
Session information
sessionInfo()R version 3.4.3 (2017-11-30)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux
Matrix products: default
BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] Seurat_2.3.4 Matrix_1.2-14 cowplot_0.9.3 ggplot2_3.0.0
loaded via a namespace (and not attached):
[1] Rtsne_0.13 colorspace_1.3-2 class_7.3-14
[4] modeltools_0.2-22 ggridges_0.5.0 mclust_5.4.1
[7] rprojroot_1.3-2 htmlTable_1.12 base64enc_0.1-3
[10] rstudioapi_0.7 proxy_0.4-22 flexmix_2.3-14
[13] bit64_0.9-7 mvtnorm_1.0-8 codetools_0.2-15
[16] splines_3.4.3 R.methodsS3_1.7.1 robustbase_0.93-2
[19] knitr_1.20 Formula_1.2-3 jsonlite_1.5
[22] workflowr_1.1.1 ica_1.0-2 cluster_2.0.7-1
[25] kernlab_0.9-27 png_0.1-7 R.oo_1.22.0
[28] compiler_3.4.3 httr_1.3.1 backports_1.1.2
[31] assertthat_0.2.0 lazyeval_0.2.1 lars_1.2
[34] acepack_1.4.1 htmltools_0.3.6 tools_3.4.3
[37] bindrcpp_0.2.2 igraph_1.2.2 gtable_0.2.0
[40] glue_1.3.0 RANN_2.6 reshape2_1.4.3
[43] dplyr_0.7.6 Rcpp_0.12.18 trimcluster_0.1-2.1
[46] gdata_2.18.0 ape_5.1 nlme_3.1-137
[49] iterators_1.0.10 fpc_2.1-11.1 gbRd_0.4-11
[52] lmtest_0.9-36 stringr_1.3.1 irlba_2.3.2
[55] gtools_3.8.1 DEoptimR_1.0-8 MASS_7.3-50
[58] zoo_1.8-3 scales_1.0.0 doSNOW_1.0.16
[61] parallel_3.4.3 RColorBrewer_1.1-2 yaml_2.2.0
[64] reticulate_1.10 pbapply_1.3-4 gridExtra_2.3
[67] rpart_4.1-13 segmented_0.5-3.0 latticeExtra_0.6-28
[70] stringi_1.2.4 foreach_1.4.4 checkmate_1.8.5
[73] caTools_1.17.1.1 bibtex_0.4.2 Rdpack_0.9-0
[76] SDMTools_1.1-221 rlang_0.2.2 pkgconfig_2.0.2
[79] dtw_1.20-1 prabclus_2.2-6 bitops_1.0-6
[82] evaluate_0.11 lattice_0.20-35 ROCR_1.0-7
[85] purrr_0.2.5 bindr_0.1.1 labeling_0.3
[88] htmlwidgets_1.2 bit_1.1-14 tidyselect_0.2.4
[91] plyr_1.8.4 magrittr_1.5 R6_2.2.2
[94] snow_0.4-2 gplots_3.0.1 Hmisc_4.1-1
[97] pillar_1.3.0 whisker_0.3-2 foreign_0.8-70
[100] withr_2.1.2 fitdistrplus_1.0-9 mixtools_1.1.0
[103] survival_2.42-6 nnet_7.3-12 tsne_0.1-3
[106] tibble_1.4.2 crayon_1.3.4 hdf5r_1.0.0
[109] KernSmooth_2.23-15 rmarkdown_1.10 grid_3.4.3
[112] data.table_1.11.4 git2r_0.23.0 metap_1.0
[115] digest_0.6.15 diptest_0.75-7 tidyr_0.8.1
[118] R.utils_2.7.0 stats4_3.4.3 munsell_0.5.0 This reproducible R Markdown analysis was created with workflowr 1.1.1