Data and Clusters
Chih-Hsuan Wu
Last updated: 2023-06-20
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Knit directory: DEanalysis/
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| html | d8c99b1 | C-HW | 2023-06-16 | variation description |
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| html | fc9f4b6 | C-HW | 2023-05-18 | add new_criteria |
| html | 7586953 | C-HW | 2023-05-11 | add data_clusters |
Data introduction
In this project, we consider a scRNA-seq dataset containing human NK/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 normalized data removing batch effects (only containing 2000 genes)
- CPM data: CPM (Counts Per Million) are obtained by dividing counts by the library counts sum and multiplying the results by a million.
Library size
Before focusing on NK/T cells, it is important to examine the original dataset, which consists of various cell types. The normalization and integration processes are performed on the entire dataset. It is worth noting that during the preprocessing stage, the specific cell types are typically unknown.
The violin plot presented below illustrates the significant variation in library size among different cell types. This discrepancy can lead to erroneous underestimation or overestimation of gene counts during normalization procedures.
Gene expression summary
| Version | Author | Date |
|---|---|---|
| d8c99b1 | C-HW | 2023-06-16 |
zoom in positive counts 
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) 
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/Celltype/Residual variation
To illustrate the differences in contribution of variation across different datasets, we employed linear regression models (lm(log2(counts + 1) ~ donor + celltype)) to analyze cells in different groups. The donor variation and celltype variation were determined by calculating the variances of their respective components. Additionally, the res variation was obtained by squaring the residual standard error. The following plots exhibit the top 500 genes with the lowest residual variations, showcasing the contributions of these variations as percentages. The genes were organized into bins based on the quantiles of donor variations. Within each bin, the representative percentage was determined by calculating the median value.
The integration of data did partially reduce the donor variations, although it did not eliminate them completely. However, it is worth noting that the normalization and batch effect removal processes employed also resulted in a reduction in celltype variation. This reduction in celltype variation may pose challenges for conducting further differential expression (DE) analysis.
Group 2, 19
Group 18, 19
Group 13, 19
Group 12, 13
Group 4, 9&15
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] SeuratObject_4.1.3 Seurat_4.3.0
[3] reshape_0.8.9 SingleCellExperiment_1.20.1
[5] SummarizedExperiment_1.28.0 Biobase_2.58.0
[7] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
[9] IRanges_2.32.0 S4Vectors_0.36.2
[11] BiocGenerics_0.44.0 MatrixGenerics_1.10.0
[13] matrixStats_0.63.0 ggpubr_0.6.0
[15] dplyr_1.1.2 ggplot2_3.4.2
loaded via a namespace (and not attached):
[1] backports_1.4.1 workflowr_1.7.0 plyr_1.8.8
[4] igraph_1.4.2 lazyeval_0.2.2 sp_1.6-0
[7] splines_4.2.2 listenv_0.9.0 scattermore_0.8
[10] digest_0.6.31 htmltools_0.5.5 fansi_1.0.4
[13] magrittr_2.0.3 tensor_1.5 cluster_2.1.4
[16] ROCR_1.0-11 limma_3.54.2 globals_0.16.2
[19] spatstat.sparse_3.0-1 colorspace_2.1-0 ggrepel_0.9.3
[22] xfun_0.39 RCurl_1.98-1.12 jsonlite_1.8.4
[25] progressr_0.13.0 spatstat.data_3.0-1 survival_3.5-5
[28] zoo_1.8-12 glue_1.6.2 polyclip_1.10-4
[31] gtable_0.3.3 zlibbioc_1.44.0 XVector_0.38.0
[34] leiden_0.4.3 DelayedArray_0.24.0 car_3.1-2
[37] future.apply_1.10.0 abind_1.4-5 scales_1.2.1
[40] edgeR_3.40.2 DBI_1.1.3 spatstat.random_3.1-4
[43] rstatix_0.7.2 miniUI_0.1.1.1 Rcpp_1.0.10
[46] viridisLite_0.4.2 xtable_1.8-4 reticulate_1.28
[49] htmlwidgets_1.6.2 httr_1.4.5 RColorBrewer_1.1-3
[52] ellipsis_0.3.2 ica_1.0-3 farver_2.1.1
[55] pkgconfig_2.0.3 uwot_0.1.14 deldir_1.0-6
[58] sass_0.4.5 locfit_1.5-9.7 utf8_1.2.3
[61] labeling_0.4.2 tidyselect_1.2.0 rlang_1.1.1
[64] reshape2_1.4.4 later_1.3.0 munsell_0.5.0
[67] tools_4.2.2 cachem_1.0.8 cli_3.6.1
[70] generics_0.1.3 broom_1.0.4 ggridges_0.5.4
[73] evaluate_0.20 stringr_1.5.0 fastmap_1.1.1
[76] goftest_1.2-3 yaml_2.3.7 knitr_1.42
[79] fs_1.6.2 fitdistrplus_1.1-11 purrr_1.0.1
[82] RANN_2.6.1 nlme_3.1-162 pbapply_1.7-0
[85] future_1.32.0 whisker_0.4.1 mime_0.12
[88] compiler_4.2.2 rstudioapi_0.14 plotly_4.10.1
[91] png_0.1-8 ggsignif_0.6.4 spatstat.utils_3.0-2
[94] tibble_3.2.1 bslib_0.4.2 stringi_1.7.12
[97] highr_0.10 lattice_0.21-8 Matrix_1.5-4
[100] vctrs_0.6.2 pillar_1.9.0 lifecycle_1.0.3
[103] spatstat.geom_3.1-0 lmtest_0.9-40 jquerylib_0.1.4
[106] RcppAnnoy_0.0.20 data.table_1.14.8 cowplot_1.1.1
[109] bitops_1.0-7 irlba_2.3.5.1 httpuv_1.6.9
[112] patchwork_1.1.2 R6_2.5.1 promises_1.2.0.1
[115] KernSmooth_2.23-20 gridExtra_2.3 parallelly_1.35.0
[118] codetools_0.2-19 MASS_7.3-59 rprojroot_2.0.3
[121] withr_2.5.0 sctransform_0.3.5 GenomeInfoDbData_1.2.9
[124] parallel_4.2.2 grid_4.2.2 tidyr_1.3.0
[127] rmarkdown_2.21 carData_3.0-5 Rtsne_0.16
[130] git2r_0.32.0 spatstat.explore_3.1-0 shiny_1.7.4