Data and Clusters
Chih-Hsuan Wu
Last updated: 2023-05-18
Checks: 5 2
Knit directory: DEanalysis/
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Data introduction
In this project, we consider a scRNA-seq dataset containing human NK1, NK2 and NK3 T cells. There are a total of 4553 cells contributed by 5 donors, and 29382 genes sequenced. The raw counts are UMI counts generated from 10X protocols.
There are three different datasets used as inputs in this project.
- Raw data: The raw UMI counts generated from 10X protocols
- Seurat normalized data: The normalized counts from each input sample via ‘Seurat::NormalizeData(x, normalization.method = “LogNormalize”, scale.factor = 10000)’
- Integrated data: The integration data without batch effects (only containing 2000 genes)
Gene expression summary
| Version | Author | Date |
|---|---|---|
| f313764 | C-HW | 2023-05-11 |
There’s one highly expressed gene MALAT1 in raw data. After normalization, the range of the counts changes a lot. (The integrated data doesn’t contain MALAT1) 
| Version | Author | Date |
|---|---|---|
| f313764 | C-HW | 2023-05-11 |
Hippo cluster result
We applied HIPPO (Heterogeneity-Inspired Pre-Processing tOol) on the raw counts to get 20 clusters. Especially, cluster 2, 8, 12, 13, 17, 19 will be used to demonstrate our poisson glmm DE methods.
UMAP
Donor effect variation
| Version | Author | Date |
|---|---|---|
| fc9f4b6 | C-HW | 2023-05-18 |
| Version | Author | Date |
|---|---|---|
| fc9f4b6 | C-HW | 2023-05-18 |
sessionInfo()R version 4.2.2 (2022-10-31)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] SingleCellExperiment_1.20.1 SummarizedExperiment_1.28.0
[3] Biobase_2.58.0 GenomicRanges_1.50.2
[5] GenomeInfoDb_1.34.9 IRanges_2.32.0
[7] S4Vectors_0.36.2 BiocGenerics_0.44.0
[9] MatrixGenerics_1.10.0 matrixStats_0.63.0
[11] ggpubr_0.6.0 dplyr_1.1.2
[13] ggplot2_3.4.2
loaded via a namespace (and not attached):
[1] sass_0.4.5 edgeR_3.40.2 tidyr_1.3.0
[4] jsonlite_1.8.4 carData_3.0-5 bslib_0.4.2
[7] highr_0.10 GenomeInfoDbData_1.2.9 yaml_2.3.7
[10] pillar_1.9.0 backports_1.4.1 lattice_0.21-8
[13] glue_1.6.2 limma_3.54.2 digest_0.6.31
[16] promises_1.2.0.1 ggsignif_0.6.4 XVector_0.38.0
[19] colorspace_2.1-0 cowplot_1.1.1 htmltools_0.5.5
[22] httpuv_1.6.9 Matrix_1.5-4 pkgconfig_2.0.3
[25] broom_1.0.4 zlibbioc_1.44.0 purrr_1.0.1
[28] scales_1.2.1 whisker_0.4.1 later_1.3.0
[31] git2r_0.32.0 tibble_3.2.1 generics_0.1.3
[34] farver_2.1.1 car_3.1-2 cachem_1.0.8
[37] withr_2.5.0 cli_3.6.1 magrittr_2.0.3
[40] evaluate_0.20 fs_1.6.2 fansi_1.0.4
[43] rstatix_0.7.2 tools_4.2.2 lifecycle_1.0.3
[46] stringr_1.5.0 munsell_0.5.0 locfit_1.5-9.7
[49] DelayedArray_0.24.0 compiler_4.2.2 jquerylib_0.1.4
[52] rlang_1.1.1 grid_4.2.2 RCurl_1.98-1.12
[55] rstudioapi_0.14 bitops_1.0-7 labeling_0.4.2
[58] rmarkdown_2.21 gtable_0.3.3 abind_1.4-5
[61] R6_2.5.1 knitr_1.42 fastmap_1.1.1
[64] utf8_1.2.3 workflowr_1.7.0 rprojroot_2.0.3
[67] stringi_1.7.12 Rcpp_1.0.10 vctrs_0.6.2
[70] tidyselect_1.2.0 xfun_0.39