“Compare with HCA”
NadineBestard
08/10/2020
Libraries
Load objects
Are the genes de in rodents expressed in the HCA dataset
This first chunck of code aims to check if the genes already highlited as potential “old” markers from the rodents data are present in the human dataset from the HCA
## [1] 49## [1] "Ube2c" "Gp1bb"49 genes are present, Ube2c, Gp1bb are the two absent.
Young adult/ old DE
The differential expression was performed with MAST as it outperforms the default Seurat differential expression (Soneson and Robinson 2018)
## [1] TRUE## [1] FALSETo visualize the top genes violin plots are done comparing the old and young groups. Here the top 4 DE are displayed 
Then this list is compares the DE list against the genes already highlighted in the rodents analyisis. The values displayed correspond to the human values in the first table and to the rodent (rat) values in the second table.
## p_val avg_log2FC pct.old pct.young p_val_adj direction
## CLASP2 5.621458e-43 0.5339917 0.847 0.727 1.390130e-38 UP
## FYN 9.696419e-26 0.4049654 0.793 0.669 2.397827e-21 UP
## GAP43 5.674658e-10 -0.4317121 0.235 0.208 1.403286e-05 DOWN## mean detected sum baseMean_rdt log2FoldChange_rdt pvalue_rdt
## CLASP2 1.7206655 78.54046 7144.203 9199.8710 0.4073614 5.268327e-03
## FYN 1.4543121 72.88054 6038.304 1627.0252 -1.2328815 1.352147e-06
## GAP43 0.2732644 22.10983 1134.594 238.7151 -1.1389178 1.825574e-03
## padj_rdt direction_rdt
## CLASP2 2.420023e-02 UP
## FYN 3.019676e-05 DOWN
## GAP43 1.076863e-02 DOWNVennDiagrams with the common genes between rodents and humans are plotted 
## (polygon[GRID.polygon.189], polygon[GRID.polygon.190], polygon[GRID.polygon.191], polygon[GRID.polygon.192], text[GRID.text.193], text[GRID.text.194], text[GRID.text.195], text[GRID.text.196], text[GRID.text.197])
## (polygon[GRID.polygon.198], polygon[GRID.polygon.199], polygon[GRID.polygon.200], polygon[GRID.polygon.201], text[GRID.text.202], text[GRID.text.203], text[GRID.text.204], text[GRID.text.205], text[GRID.text.206])Violin plots of the common genes are used again with the HCA data. 
TODO
- have a look to all the genes that came up in the rodents analysis in Violin or FeaturePlots
Session information
Click to expand
## R version 4.0.4 (2021-02-15)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19041)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United Kingdom.1252
## [2] LC_CTYPE=English_United Kingdom.1252
## [3] LC_MONETARY=English_United Kingdom.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United Kingdom.1252
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] scales_1.1.1 gridExtra_2.3
## [3] dplyr_1.0.5 scater_1.18.6
## [5] ggplot2_3.3.3 SingleCellExperiment_1.12.0
## [7] SummarizedExperiment_1.20.0 Biobase_2.50.0
## [9] GenomicRanges_1.42.0 GenomeInfoDb_1.26.4
## [11] IRanges_2.24.1 S4Vectors_0.28.1
## [13] BiocGenerics_0.36.0 MatrixGenerics_1.2.1
## [15] matrixStats_0.58.0 SeuratObject_4.0.0
## [17] Seurat_4.0.0 here_1.0.1
##
## loaded via a namespace (and not attached):
## [1] plyr_1.8.6 igraph_1.2.6
## [3] lazyeval_0.2.2 splines_4.0.4
## [5] BiocParallel_1.24.1 listenv_0.8.0
## [7] scattermore_0.7 digest_0.6.27
## [9] htmltools_0.5.1.1 viridis_0.5.1
## [11] fansi_0.4.2 magrittr_2.0.1
## [13] tensor_1.5 cluster_2.1.1
## [15] ROCR_1.0-11 globals_0.14.0
## [17] colorspace_2.0-0 ggrepel_0.9.1
## [19] xfun_0.21 crayon_1.4.1
## [21] RCurl_1.98-1.2 jsonlite_1.7.2
## [23] spatstat_1.64-1 spatstat.data_2.0-0
## [25] survival_3.2-10 zoo_1.8-9
## [27] glue_1.4.2 polyclip_1.10-0
## [29] gtable_0.3.0 zlibbioc_1.36.0
## [31] XVector_0.30.0 leiden_0.3.7
## [33] DelayedArray_0.16.2 BiocSingular_1.6.0
## [35] future.apply_1.7.0 abind_1.4-5
## [37] futile.options_1.0.1 DBI_1.1.1
## [39] miniUI_0.1.1.1 Rcpp_1.0.6
## [41] viridisLite_0.3.0 xtable_1.8-4
## [43] reticulate_1.18 rsvd_1.0.3
## [45] htmlwidgets_1.5.3 httr_1.4.2
## [47] RColorBrewer_1.1-2 ellipsis_0.3.1
## [49] ica_1.0-2 farver_2.1.0
## [51] pkgconfig_2.0.3 scuttle_1.0.4
## [53] sass_0.3.1 uwot_0.1.10
## [55] deldir_0.2-10 utf8_1.2.1
## [57] labeling_0.4.2 tidyselect_1.1.0
## [59] rlang_0.4.10 reshape2_1.4.4
## [61] later_1.1.0.1 munsell_0.5.0
## [63] tools_4.0.4 generics_0.1.0
## [65] ggridges_0.5.3 evaluate_0.14
## [67] stringr_1.4.0 fastmap_1.1.0
## [69] yaml_2.2.1 goftest_1.2-2
## [71] knitr_1.31 fitdistrplus_1.1-3
## [73] purrr_0.3.4 RANN_2.6.1
## [75] pbapply_1.4-3 future_1.21.0
## [77] nlme_3.1-152 sparseMatrixStats_1.2.1
## [79] mime_0.10 formatR_1.8
## [81] compiler_4.0.4 beeswarm_0.3.1
## [83] plotly_4.9.3 png_0.1-7
## [85] spatstat.utils_2.1-0 tibble_3.1.0
## [87] bslib_0.2.4 stringi_1.5.3
## [89] futile.logger_1.4.3 highr_0.8
## [91] lattice_0.20-41 Matrix_1.3-2
## [93] vctrs_0.3.6 pillar_1.5.1
## [95] lifecycle_1.0.0 lmtest_0.9-38
## [97] jquerylib_0.1.3 RcppAnnoy_0.0.18
## [99] BiocNeighbors_1.8.2 data.table_1.14.0
## [101] cowplot_1.1.1 bitops_1.0-6
## [103] irlba_2.3.3 httpuv_1.5.5
## [105] patchwork_1.1.1 R6_2.5.0
## [107] promises_1.2.0.1 KernSmooth_2.23-18
## [109] vipor_0.4.5 parallelly_1.24.0
## [111] codetools_0.2-18 lambda.r_1.2.4
## [113] MASS_7.3-53.1 assertthat_0.2.1
## [115] rprojroot_2.0.2 withr_2.4.1
## [117] sctransform_0.3.2 GenomeInfoDbData_1.2.4
## [119] mgcv_1.8-34 VennDiagram_1.6.20
## [121] grid_4.0.4 rpart_4.1-15
## [123] beachmat_2.6.4 tidyr_1.1.3
## [125] rmarkdown_2.7 DelayedMatrixStats_1.12.3
## [127] Rtsne_0.15 shiny_1.6.0
## [129] ggbeeswarm_0.6.0Reference
Soneson, Charlotte, and Mark D. Robinson. 2018. “Bias, robustness and scalability in single-cell differential expression analysis.” Nature Methods 15 (4): 255–61. https://doi.org/10.1038/nmeth.4612.