Group8_17
Chih-Hsuan Wu
Last updated: 2024-10-22
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Knit directory: DEanalysis/
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Data summary
Difference in library size
Asymptotic two-sample Kolmogorov-Smirnov test
data: subset(count_cell_df, Group == group1, totalcount)$totalcount and subset(count_cell_df, Group == group2, totalcount)$totalcount
D = 0.44612, p-value < 2.2e-16
alternative hypothesis: two-sided
Welch Two Sample t-test
data: subset(count_cell_df, Group == group1, totalcount)$totalcount and subset(count_cell_df, Group == group2, totalcount)$totalcount
t = -5.1593, df = 333.02, p-value = 4.267e-07
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-456.1859 -204.3422
sample estimates:
mean of x mean of y
982.0776 1312.3417 Mean difference in raw data/normalized data
Number of DEGs from each method
Volcano plot
Histogram of p-value/adj.p-value
P-Value comparison across different methods
Log2 fold change comparison across different methods
Violin plot of log2mean of DEGs
Violin plot of gene expression frequency of DEGs
Upset plot
Heatmap of top DEGs
Poisson-glmm DEGs
UMI counts
VST data
CPM data
Additional DEGs from other methods
pb-DESeq2
Binomial-glmm
Wilcox
MAST
DEGs in Poisson-glmm not identified by MMpoisson
In the MMpoisson model, cell type is considered as a random effect. This approach treats certain aspects of cell type variations as random factors. Consequently, it may obscure the true variation in cell types, limiting its ability to accurately reveal the specific differences between different cell types.
Additionally, the library size is employed as an offset to normalize the counts. That is, the model is considering rate instead of counts. Suppose some genes are highly expressed in one cell type than the other, the absolute difference could be eliminate after accounting for library size. This normalization approach may inadvertently mask certain gene expression differences between cell types.
MA plot
Enrichment analysis
GO object
enrichKEGG object
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] UpSetR_1.4.0 SeuratObject_5.0.2
[3] sp_2.1-4 pathview_1.44.0
[5] org.Hs.eg.db_3.19.1 AnnotationDbi_1.66.0
[7] enrichplot_1.24.4 clusterProfiler_4.12.6
[9] reshape_0.8.9 gridExtra_2.3
[11] pheatmap_1.0.12 SingleCellExperiment_1.26.0
[13] SummarizedExperiment_1.34.0 Biobase_2.64.0
[15] GenomicRanges_1.56.2 GenomeInfoDb_1.40.1
[17] IRanges_2.38.1 S4Vectors_0.42.1
[19] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[21] matrixStats_1.4.1 ggpubr_0.6.0
[23] dplyr_1.1.4 ggplot2_3.5.1
loaded via a namespace (and not attached):
[1] splines_4.4.1 later_1.3.2 bitops_1.0-9
[4] ggplotify_0.1.2 tibble_3.2.1 R.oo_1.26.0
[7] polyclip_1.10-7 graph_1.82.0 XML_3.99-0.17
[10] lifecycle_1.0.4 httr2_1.0.5 rstatix_0.7.2
[13] rprojroot_2.0.4 globals_0.16.3 lattice_0.22-6
[16] MASS_7.3-61 backports_1.5.0 magrittr_2.0.3
[19] sass_0.4.9 rmarkdown_2.28 jquerylib_0.1.4
[22] yaml_2.3.10 httpuv_1.6.15 spam_2.11-0
[25] cowplot_1.1.3 DBI_1.2.3 RColorBrewer_1.1-3
[28] abind_1.4-8 zlibbioc_1.50.0 purrr_1.0.2
[31] R.utils_2.12.3 ggraph_2.2.1 RCurl_1.98-1.16
[34] yulab.utils_0.1.7 tweenr_2.0.3 rappdirs_0.3.3
[37] git2r_0.35.0 GenomeInfoDbData_1.2.12 ggrepel_0.9.6
[40] listenv_0.9.1 tidytree_0.4.6 parallelly_1.38.0
[43] codetools_0.2-20 DelayedArray_0.30.1 DOSE_3.30.5
[46] ggforce_0.4.2 tidyselect_1.2.1 aplot_0.2.3
[49] UCSC.utils_1.0.0 farver_2.1.2 viridis_0.6.5
[52] jsonlite_1.8.9 progressr_0.14.0 tidygraph_1.3.1
[55] Formula_1.2-5 ggnewscale_0.5.0 tools_4.4.1
[58] treeio_1.28.0 Rcpp_1.0.13 glue_1.8.0
[61] SparseArray_1.4.8 xfun_0.48 qvalue_2.36.0
[64] withr_3.0.1 fastmap_1.2.0 fansi_1.0.6
[67] digest_0.6.37 R6_2.5.1 gridGraphics_0.5-1
[70] colorspace_2.1-1 GO.db_3.19.1 RSQLite_2.3.7
[73] R.methodsS3_1.8.2 utf8_1.2.4 tidyr_1.3.1
[76] generics_0.1.3 data.table_1.16.2 graphlayouts_1.2.0
[79] httr_1.4.7 S4Arrays_1.4.1 scatterpie_0.2.4
[82] whisker_0.4.1 pkgconfig_2.0.3 gtable_0.3.5
[85] blob_1.2.4 workflowr_1.7.1 XVector_0.44.0
[88] shadowtext_0.1.4 htmltools_0.5.8.1 carData_3.0-5
[91] dotCall64_1.2 fgsea_1.30.0 scales_1.3.0
[94] png_0.1-8 ggfun_0.1.6 knitr_1.48
[97] rstudioapi_0.17.0 reshape2_1.4.4 nlme_3.1-166
[100] cachem_1.1.0 stringr_1.5.1 parallel_4.4.1
[103] pillar_1.9.0 grid_4.4.1 vctrs_0.6.5
[106] promises_1.3.0 car_3.1-3 Rgraphviz_2.48.0
[109] evaluate_1.0.1 KEGGgraph_1.64.0 cli_3.6.3
[112] compiler_4.4.1 rlang_1.1.4 crayon_1.5.3
[115] future.apply_1.11.2 ggsignif_0.6.4 labeling_0.4.3
[118] plyr_1.8.9 fs_1.6.4 stringi_1.8.4
[121] viridisLite_0.4.2 BiocParallel_1.38.0 munsell_0.5.1
[124] Biostrings_2.72.1 lazyeval_0.2.2 GOSemSim_2.30.2
[127] Matrix_1.7-1 patchwork_1.3.0 bit64_4.5.2
[130] future_1.34.0 KEGGREST_1.44.1 highr_0.11
[133] igraph_2.1.1 broom_1.0.7 memoise_2.0.1
[136] bslib_0.8.0 ggtree_3.12.0 fastmatch_1.1-4
[139] bit_4.5.0 ape_5.8 gson_0.1.0