Fallopian tubes data overview

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Last updated: 2024-10-19

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Knit directory: DEanalysis/

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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 four distinct assays as inputs.

• Raw counts: This refers to the raw UMI counts generated from 10X protocols without any normalization or adjustments applied.

• (Unused) Seurat normalized counts: 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 counts: The integrated data refers to the normalized counts after removing batch effects. This assay 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 counts: CPM stands for Counts Per Million. This assay 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.

• VST counts: VST (variance stabilizing transformation) is computed via the sctransform package (Hafemeister and Satija 2019), which returns Pearson residuals from a regularized negative binomial regression model that can be interpreted as normalized expression values.

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.

All genes

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Genes shown in integrated dataset

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Gene expression frequency

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Gene expression distribution

All genes

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Genes shown in integrated dataset

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If we zoom in on the positive counts, we may observe different distributions for each dataset.

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VST distribution

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Integrated data distribution

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Single gene

We randomly choose one moderately expressed gene RUNX3. After normalization, the distribution of the counts changes a lot.

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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

Hippo procedure

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Interested groups

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Zero proportion plot

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Donor distribution

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Donor/Celltype/Residual variation

To gain a deeper understanding of the donor effect and cell type effect concerning various types of counts, we conducted a variation analysis across multiple group comparisons. To ensure the consistency of our results, we restricted our analysis to genes presented in all datasets. For each gene, we employed linear models (lm (count ~ donor + group)) and computed the variances attributed to three components: donor, group, and the residual. Logarithm transformation was applied to UMI counts and CPM data to address skewness.

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 2, 19

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Group 18, 19

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Group 13, 19

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Group 12, 13

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Group 4, 9&15

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Session information

R version 4.4.1 (2024-06-14)
Platform: x86_64-apple-darwin20
Running under: macOS Monterey 12.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Chicago
tzcode source: internal

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] Seurat_5.1.0                SeuratObject_5.0.2         
 [3] sp_2.1-4                    reshape2_1.4.4             
 [5] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
 [7] Biobase_2.64.0              GenomicRanges_1.56.2       
 [9] GenomeInfoDb_1.40.1         IRanges_2.38.1             
[11] S4Vectors_0.42.1            BiocGenerics_0.50.0        
[13] MatrixGenerics_1.16.0       matrixStats_1.4.1          
[15] ggpubr_0.6.0                dplyr_1.1.4                
[17] ggplot2_3.5.1              

loaded via a namespace (and not attached):
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  [4] magrittr_2.0.3          spatstat.utils_3.1-0    farver_2.1.2           
  [7] rmarkdown_2.28          fs_1.6.4                zlibbioc_1.50.0        
 [10] vctrs_0.6.5             ROCR_1.0-11             spatstat.explore_3.3-2 
 [13] rstatix_0.7.2           htmltools_0.5.8.1       S4Arrays_1.4.1         
 [16] broom_1.0.7             SparseArray_1.4.8       Formula_1.2-5          
 [19] sctransform_0.4.1       sass_0.4.9              parallelly_1.38.0      
 [22] KernSmooth_2.23-24      bslib_0.8.0             htmlwidgets_1.6.4      
 [25] ica_1.0-3               plyr_1.8.9              plotly_4.10.4          
 [28] zoo_1.8-12              cachem_1.1.0            whisker_0.4.1          
 [31] igraph_2.0.3            mime_0.12               lifecycle_1.0.4        
 [34] pkgconfig_2.0.3         Matrix_1.7-0            R6_2.5.1               
 [37] fastmap_1.2.0           GenomeInfoDbData_1.2.12 fitdistrplus_1.2-1     
 [40] future_1.34.0           shiny_1.9.1             digest_0.6.37          
 [43] colorspace_2.1-1        patchwork_1.3.0         tensor_1.5             
 [46] rprojroot_2.0.4         RSpectra_0.16-2         irlba_2.3.5.1          
 [49] labeling_0.4.3          progressr_0.14.0        spatstat.sparse_3.1-0  
 [52] fansi_1.0.6             polyclip_1.10-7         httr_1.4.7             
 [55] abind_1.4-8             compiler_4.4.1          withr_3.0.1            
 [58] backports_1.5.0         carData_3.0-5           fastDummies_1.7.4      
 [61] highr_0.11              ggsignif_0.6.4          MASS_7.3-61            
 [64] DelayedArray_0.30.1     tools_4.4.1             lmtest_0.9-40          
 [67] httpuv_1.6.15           future.apply_1.11.2     goftest_1.2-3          
 [70] glue_1.8.0              nlme_3.1-166            promises_1.3.0         
 [73] grid_4.4.1              Rtsne_0.17              cluster_2.1.6          
 [76] generics_0.1.3          spatstat.data_3.1-2     gtable_0.3.5           
 [79] tidyr_1.3.1             data.table_1.16.2       car_3.1-3              
 [82] utf8_1.2.4              XVector_0.44.0          spatstat.geom_3.3-3    
 [85] RcppAnnoy_0.0.22        ggrepel_0.9.6           RANN_2.6.2             
 [88] pillar_1.9.0            stringr_1.5.1           spam_2.11-0            
 [91] RcppHNSW_0.6.0          later_1.3.2             splines_4.4.1          
 [94] lattice_0.22-6          deldir_2.0-4            survival_3.7-0         
 [97] tidyselect_1.2.1        pbapply_1.7-2           miniUI_0.1.1.1         
[100] knitr_1.48              git2r_0.33.0            gridExtra_2.3          
[103] scattermore_1.2         xfun_0.48               stringi_1.8.4          
[106] UCSC.utils_1.0.0        lazyeval_0.2.2          workflowr_1.7.1        
[109] yaml_2.3.10             evaluate_1.0.1          codetools_0.2-20       
[112] tibble_3.2.1            cli_3.6.3               uwot_0.2.2             
[115] xtable_1.8-4            reticulate_1.39.0       munsell_0.5.1          
[118] jquerylib_0.1.4         Rcpp_1.0.13             spatstat.random_3.3-2  
[121] globals_0.16.3          png_0.1-8               spatstat.univar_3.0-1  
[124] parallel_4.4.1          dotCall64_1.2           listenv_0.9.1          
[127] viridisLite_0.4.2       scales_1.3.0            ggridges_0.5.6         
[130] leiden_0.4.3.1          purrr_1.0.2             crayon_1.5.3           
[133] rlang_1.1.4             cowplot_1.1.3