Gene level Contrasts
Yasin Kaymaz
1/31/2019
Last updated: 2019-03-15
Checks: 6 0
Knit directory: Harvard-RosenbrockLab/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | b85db50 | Yasin Kaymaz | 2019-03-15 | isoform results |
| html | de005bf | Yasin Kaymaz | 2019-03-15 | Build site. |
| Rmd | 4ce42ab | Yasin Kaymaz | 2019-03-15 | second stage of hook results |
| html | 4ce42ab | Yasin Kaymaz | 2019-03-15 | second stage of hook results |
| html | abeab71 | Yasin Kaymaz | 2019-03-15 | Build site. |
| Rmd | ae57569 | Yasin Kaymaz | 2019-03-15 | isoform results |
| html | 1abd5c7 | Yasin Kaymaz | 2019-03-15 | Build site. |
| Rmd | dbac3ec | Yasin Kaymaz | 2019-03-15 | isoform results |
Gene expression distributions among cells in each group
After count processing and filtration, I group all cells based on mouse age, brain region, and cell subsets. Violin plots show normalized expression distribution of the given gene in each cell (black dots) binned in various groups. y-axis is in log2 scale!
Genes of interest are “Gria1”,“Gria4”,“Grm4”, and “Gpr83”.
Cells are grouped based on mouse age
Cells are grouped based on cell subset
| Version | Author | Date |
|---|---|---|
| abeab71 | Yasin Kaymaz | 2019-03-15 |
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sessionInfo()R version 3.5.0 (2018-04-23)
Platform: x86_64-apple-darwin17.5.0 (64-bit)
Running under: macOS 10.14.3
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libLAPACK.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] Seurat_2.3.4 Matrix_1.2-14 cowplot_0.9.4 here_0.1
[5] forcats_0.4.0 stringr_1.4.0 dplyr_0.8.0.1 purrr_0.3.1
[9] readr_1.3.1 tidyr_0.8.3 tibble_2.0.1 tidyverse_1.2.1
[13] DT_0.5 plotly_4.8.0 ggplot2_3.1.0
loaded via a namespace (and not attached):
[1] readxl_1.3.1 snow_0.4-3 backports_1.1.2
[4] Hmisc_4.2-0 workflowr_1.2.0 plyr_1.8.4
[7] igraph_1.2.4 lazyeval_0.2.1 splines_3.5.0
[10] digest_0.6.18 foreach_1.4.4 htmltools_0.3.6
[13] lars_1.2 gdata_2.18.0 magrittr_1.5
[16] checkmate_1.9.1 cluster_2.0.7-1 mixtools_1.1.0
[19] ROCR_1.0-7 modelr_0.1.4 R.utils_2.8.0
[22] colorspace_1.4-0 rvest_0.3.2 haven_2.1.0
[25] crayon_1.3.4 jsonlite_1.6 survival_2.42-6
[28] zoo_1.8-4 iterators_1.0.10 ape_5.2
[31] glue_1.3.1 gtable_0.2.0 kernlab_0.9-27
[34] prabclus_2.2-7 DEoptimR_1.0-8 scales_1.0.0
[37] mvtnorm_1.0-10 bibtex_0.4.2 Rcpp_1.0.0
[40] metap_1.1 dtw_1.20-1 viridisLite_0.3.0
[43] htmlTable_1.13.1 reticulate_1.11.1 foreign_0.8-70
[46] bit_1.1-14 proxy_0.4-23 mclust_5.4.3
[49] SDMTools_1.1-221 Formula_1.2-3 tsne_0.1-3
[52] stats4_3.5.0 htmlwidgets_1.3 httr_1.4.0
[55] gplots_3.0.1.1 RColorBrewer_1.1-2 fpc_2.1-11.1
[58] acepack_1.4.1 modeltools_0.2-22 ica_1.0-2
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[67] tidyselect_0.2.5 rlang_0.3.1 munsell_0.5.0
[70] cellranger_1.1.0 tools_3.5.0 cli_1.0.1
[73] generics_0.0.2 broom_0.5.1 ggridges_0.5.1
[76] evaluate_0.10.1 yaml_2.2.0 npsurv_0.4-0
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[112] gtools_3.8.1 assertthat_0.2.0 rprojroot_1.3-2
[115] withr_2.1.2 diptest_0.75-7 parallel_3.5.0
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[124] segmented_0.5-3.0 Rtsne_0.15 git2r_0.24.0
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