Last updated: 2018-12-12
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✔ Repository version: 069d471
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File | Version | Author | Date | Message |
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Rmd | 069d471 | PytrikFolkertsma | 2018-12-12 | wflow_publish(c(“analysis/10x-180831-first-inspection.Rmd”)) |
html | 3bae44a | PytrikFolkertsma | 2018-11-08 | Build site. |
Rmd | aede8bc | PytrikFolkertsma | 2018-11-08 | wflow_publish(c(“analysis/10x-180831-first-inspection.Rmd”)) |
html | b84ca55 | PytrikFolkertsma | 2018-11-08 | Build site. |
Rmd | 92ef278 | PytrikFolkertsma | 2018-11-08 | wflow_publish(c(“analysis/10x-180831-first-inspection.Rmd”)) |
html | c8f6254 | PytrikFolkertsma | 2018-11-08 | Build site. |
Rmd | f9da1a0 | PytrikFolkertsma | 2018-11-08 | wflow_publish(c(“analysis/10x-180831-first-inspection.Rmd”)) |
library(Seurat)
data <- readRDS('output/10x-180831-notcleaned')
TSNE plots of the data.
t1 <- TSNEPlot(data, group.by='timepoint', pt.size=0.1)
Version | Author | Date |
---|---|---|
b84ca55 | PytrikFolkertsma | 2018-11-08 |
c8f6254 | PytrikFolkertsma | 2018-11-08 |
t2 <- TSNEPlot(data, group.by='Phase', pt.size=0.1)
Version | Author | Date |
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b84ca55 | PytrikFolkertsma | 2018-11-08 |
c8f6254 | PytrikFolkertsma | 2018-11-08 |
t3 <- FeaturePlot(data, features.plot=c('nGene'), cols.use=c('gray', 'blue'), no.legend = F)
Version | Author | Date |
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b84ca55 | PytrikFolkertsma | 2018-11-08 |
c8f6254 | PytrikFolkertsma | 2018-11-08 |
#t4 <- DimPlot(data, reduction.use='tsne', cells.highlight = rownames(data@meta.data)[data@meta.data$res.0.5 == 1], cols.highlight = 'blue', cols.use='gray')
TSNEPlot(data, group.by='res.0.5', pt.size=0.1, do.label=T)
Version | Author | Date |
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b84ca55 | PytrikFolkertsma | 2018-11-08 |
c8f6254 | PytrikFolkertsma | 2018-11-08 |
Remove bad quality cluster 1 from the data.
data_cleaned <- SubsetData(data, cells.use=rownames(data@meta.data)[data@meta.data$res.0.5 != 1])
TSNEPlot(data_cleaned, group.by='res.0.5', pt.size=0.1)
Version | Author | Date |
---|---|---|
c8f6254 | PytrikFolkertsma | 2018-11-08 |
Save the cleaned up Seurat object to run preprocessing again (new PCA, clustering and tSNE).
saveRDS(data_cleaned, 'output/10x-180831')
#sfig <- plot_grid(
# t4,
# t1,
# t2,
# t3,
# nrow=2, labels='auto'
#)
#save_plot('plots/supplementary_figures/sfig_180831_bad-quality-cluster.pdf', sfig, base_width=12, base_height = 9)
#sfig
#
sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: Storage
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] bindrcpp_0.2.2 Seurat_2.3.4 Matrix_1.2-14 cowplot_0.9.3
[5] 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] igraph_1.2.2 gtable_0.2.0 glue_1.3.0
[40] RANN_2.6 reshape2_1.4.3 dplyr_0.7.6
[43] Rcpp_0.12.18 trimcluster_0.1-2.1 gdata_2.18.0
[46] ape_5.1 nlme_3.1-137 iterators_1.0.10
[49] fpc_2.1-11.1 gbRd_0.4-11 lmtest_0.9-36
[52] stringr_1.3.1 irlba_2.3.2 gtools_3.8.1
[55] DEoptimR_1.0-8 MASS_7.3-50 zoo_1.8-3
[58] scales_1.0.0 doSNOW_1.0.16 parallel_3.4.3
[61] RColorBrewer_1.1-2 yaml_2.2.0 reticulate_1.10
[64] pbapply_1.3-4 gridExtra_2.3 rpart_4.1-13
[67] segmented_0.5-3.0 latticeExtra_0.6-28 stringi_1.2.4
[70] foreach_1.4.4 checkmate_1.8.5 caTools_1.17.1.1
[73] bibtex_0.4.2 Rdpack_0.9-0 SDMTools_1.1-221
[76] rlang_0.2.2 pkgconfig_2.0.2 dtw_1.20-1
[79] prabclus_2.2-6 bitops_1.0-6 evaluate_0.11
[82] lattice_0.20-35 ROCR_1.0-7 purrr_0.2.5
[85] bindr_0.1.1 labeling_0.3 htmlwidgets_1.2
[88] bit_1.1-14 tidyselect_0.2.4 plyr_1.8.4
[91] magrittr_1.5 R6_2.2.2 snow_0.4-2
[94] gplots_3.0.1 Hmisc_4.1-1 pillar_1.3.0
[97] whisker_0.3-2 foreign_0.8-71 withr_2.1.2
[100] fitdistrplus_1.0-9 mixtools_1.1.0 survival_2.42-6
[103] nnet_7.3-12 tsne_0.1-3 tibble_1.4.2
[106] crayon_1.3.4 hdf5r_1.0.0 KernSmooth_2.23-15
[109] rmarkdown_1.10 grid_3.4.3 data.table_1.11.4
[112] git2r_0.23.0 metap_1.0 digest_0.6.16
[115] diptest_0.75-7 tidyr_0.8.1 R.utils_2.7.0
[118] stats4_3.4.3 munsell_0.5.0
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