Data and Clusters
Chih-Hsuan Wu
Last updated: 2023-06-29
Checks: 5 2
Knit directory: DEanalysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 91d6ad2 | C-HW | 2023-06-24 | adjust ylim |
| html | 87a6bb9 | C-HW | 2023-06-21 | add freq plot |
| html | e7fb941 | C-HW | 2023-06-20 | color variationcomparison |
| html | d8c99b1 | C-HW | 2023-06-16 | variation description |
| html | 366cd53 | C-HW | 2023-06-06 | add group8_17&2_19 |
| html | 13d726d | C-HW | 2023-05-18 | add DE results on different groups |
| html | fc9f4b6 | C-HW | 2023-05-18 | add new_criteria |
| html | 7586953 | C-HW | 2023-05-11 | add data_clusters |
Data introduction
For this project, we are analyzing an scRNA-seq dataset that comprises human immune cells. The dataset consists of a total of 57,182 cells contributed by 5 donors, and it includes sequencing data for 29,382 genes. The raw counts represent UMI counts generated using 10X protocols.
In this project, we utilize three distinct datasets as inputs.
Raw data: This refers to the raw UMI counts generated from 10X protocols without any normalization or adjustments applied.
Seurat normalized data: This data type represents the counts from each input sample after normalization using the ‘Seurat::NormalizeData(x, normalization.method = “LogNormalize”, scale.factor = 10000)’ function. This normalization method helps to account for differences in library sizes between samples and scales the data by a factor of 10,000.
Integrated data: The integrated data refers to the normalized counts after removing batch effects. This dataset only contains information for 2000 genes. The purpose of removing batch effects is to minimize any systematic differences introduced by technical variations across different experimental batches.
CPM data: CPM stands for Counts Per Million. This data is obtained by dividing the counts by the sum of library counts and multiplying the result by a million. The CPM values provide a normalized representation of the expression levels, allowing for meaningful comparisons between samples while accounting for differences in library sizes.
Library size
Before focusing on specific cell types (NK/T), 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.
Raw data(including all genes)
Raw data(only including common genes)
Gene expression frequency
Gene expression distribution
Including all genes
If we zoom in on the positive counts, we may observe different distributions for each dataset. 
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) 
Only including common genes
If we zoom in on the counts greater than 0.2, we may observe different distributions for each dataset. 
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 residual variations. The last plot displays the first quartile and compare the donor variation and celltype variation
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 18, 19
Group 13, 19
| Version | Author | Date |
|---|---|---|
| 87a6bb9 | C-HW | 2023-06-21 |
| Version | Author | Date |
|---|---|---|
| 91d6ad2 | C-HW | 2023-06-24 |
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] reshape2_1.4.4 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 sass_0.4.5
[58] deldir_1.0-6 locfit_1.5-9.7 utf8_1.2.3
[61] labeling_0.4.2 tidyselect_1.2.0 rlang_1.1.1
[64] later_1.3.0 munsell_0.5.0 tools_4.2.2
[67] cachem_1.0.8 cli_3.6.1 generics_0.1.3
[70] broom_1.0.4 ggridges_0.5.4 evaluate_0.20
[73] stringr_1.5.0 fastmap_1.1.1 goftest_1.2-3
[76] yaml_2.3.7 knitr_1.42 fs_1.6.2
[79] fitdistrplus_1.1-11 purrr_1.0.1 RANN_2.6.1
[82] nlme_3.1-162 pbapply_1.7-0 future_1.32.0
[85] whisker_0.4.1 mime_0.12 compiler_4.2.2
[88] rstudioapi_0.14 plotly_4.10.1 png_0.1-8
[91] ggsignif_0.6.4 spatstat.utils_3.0-2 tibble_3.2.1
[94] bslib_0.4.2 stringi_1.7.12 highr_0.10
[97] lattice_0.21-8 Matrix_1.5-4 vctrs_0.6.2
[100] pillar_1.9.0 lifecycle_1.0.3 spatstat.geom_3.1-0
[103] lmtest_0.9-40 jquerylib_0.1.4 RcppAnnoy_0.0.20
[106] data.table_1.14.8 cowplot_1.1.1 bitops_1.0-7
[109] irlba_2.3.5.1 httpuv_1.6.9 patchwork_1.1.2
[112] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20
[115] gridExtra_2.3 parallelly_1.35.0 codetools_0.2-19
[118] MASS_7.3-59 rprojroot_2.0.3 withr_2.5.0
[121] sctransform_0.3.5 GenomeInfoDbData_1.2.9 parallel_4.2.2
[124] grid_4.2.2 tidyr_1.3.0 rmarkdown_2.21
[127] carData_3.0-5 Rtsne_0.16 git2r_0.32.0
[130] spatstat.explore_3.1-0 shiny_1.7.4