- Data summary
- Mean difference in raw data/normalized data
- Number of hits from each method
- Volcano plot
- Histogram of p-value/adj.p-value
- Violin plot of log2mean of DEGs
- Violin plot of gene expression frequency of DEGs
- Heatmap of top hits
- Poisson-glmm DEGs
- UMI counts
- VST data
- CPM data
- Integrated data
- Additional DEGs from other methods
- pb-DESeq2
- Binomial-glmm
- MAST
- MMpoisson
- DEGs in pois_glmm exclusive to MMpoisson
- MA plot
- Enrichment analysis
- GO object
- enrichKEGG object
group8_17&2_19
Chih-Hsuan Wu
Last updated: 2023-11-29
Checks: 5 2
Knit directory: DEanalysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | e8b0519 | C-HW | 2023-11-29 | update all pairs |
| html | 85fc2fe | C-HW | 2023-09-12 | update |
| html | d7d838c | C-HW | 2023-08-11 | update graph |
| html | 7ee9782 | C-HW | 2023-07-13 | add 8_17 |
| html | ccb68e2 | C-HW | 2023-06-29 | log2fc consistence |
| html | 3121ffb | C-HW | 2023-06-22 | color palette heatmap |
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Heatmap of top hits
Poisson-glmm DEGs
Additional DEGs from other methods
DEGs in pois_glmm exclusive to 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.
| Version | Author | Date |
|---|---|---|
| e8b0519 | C-HW | 2023-11-29 |
Enrichment analysis
enrichKEGG object
| Version | Author | Date |
|---|---|---|
| e8b0519 | C-HW | 2023-11-29 |
| Version | Author | Date |
|---|---|---|
| e8b0519 | C-HW | 2023-11-29 |
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] pathview_1.38.0 org.Hs.eg.db_3.16.0
[3] AnnotationDbi_1.60.2 enrichplot_1.18.4
[5] clusterProfiler_4.6.2 reshape_0.8.9
[7] gridExtra_2.3 pheatmap_1.0.12
[9] SingleCellExperiment_1.20.1 SummarizedExperiment_1.28.0
[11] Biobase_2.58.0 GenomicRanges_1.50.2
[13] GenomeInfoDb_1.34.9 IRanges_2.32.0
[15] S4Vectors_0.36.2 BiocGenerics_0.44.0
[17] MatrixGenerics_1.10.0 matrixStats_1.0.0
[19] ggpubr_0.6.0 dplyr_1.1.2
[21] ggplot2_3.4.2
loaded via a namespace (and not attached):
[1] shadowtext_0.1.2 backports_1.4.1 fastmatch_1.1-3
[4] workflowr_1.7.0 plyr_1.8.8 igraph_1.5.0
[7] lazyeval_0.2.2 splines_4.2.2 BiocParallel_1.32.6
[10] digest_0.6.33 yulab.utils_0.0.6 htmltools_0.5.5
[13] GOSemSim_2.24.0 viridis_0.6.3 GO.db_3.16.0
[16] fansi_1.0.4 magrittr_2.0.3 memoise_2.0.1
[19] Biostrings_2.66.0 graphlayouts_1.0.0 colorspace_2.1-0
[22] blob_1.2.4 ggrepel_0.9.3 xfun_0.39
[25] crayon_1.5.2 RCurl_1.98-1.12 jsonlite_1.8.7
[28] graph_1.76.0 scatterpie_0.2.1 ape_5.7-1
[31] glue_1.6.2 polyclip_1.10-4 gtable_0.3.3
[34] zlibbioc_1.44.0 XVector_0.38.0 DelayedArray_0.24.0
[37] car_3.1-2 Rgraphviz_2.42.0 abind_1.4-5
[40] scales_1.2.1 DOSE_3.24.2 DBI_1.1.3
[43] rstatix_0.7.2 Rcpp_1.0.11 viridisLite_0.4.2
[46] gridGraphics_0.5-1 tidytree_0.4.4 bit_4.0.5
[49] httr_1.4.6 fgsea_1.24.0 RColorBrewer_1.1-3
[52] XML_3.99-0.14 pkgconfig_2.0.3 farver_2.1.1
[55] sass_0.4.7 utf8_1.2.3 labeling_0.4.2
[58] ggplotify_0.1.1 tidyselect_1.2.0 rlang_1.1.1
[61] reshape2_1.4.4 later_1.3.1 munsell_0.5.0
[64] tools_4.2.2 cachem_1.0.8 downloader_0.4
[67] cli_3.6.1 generics_0.1.3 RSQLite_2.3.1
[70] gson_0.1.0 broom_1.0.5 evaluate_0.21
[73] stringr_1.5.0 fastmap_1.1.1 yaml_2.3.7
[76] ggtree_3.6.2 knitr_1.27 bit64_4.0.5
[79] fs_1.6.3 tidygraph_1.2.3 purrr_1.0.1
[82] KEGGREST_1.38.0 ggraph_2.1.0 nlme_3.1-162
[85] whisker_0.4.1 KEGGgraph_1.58.3 aplot_0.1.10
[88] compiler_4.2.2 rstudioapi_0.15.0 png_0.1-8
[91] ggsignif_0.6.4 treeio_1.22.0 tibble_3.2.1
[94] tweenr_2.0.2 bslib_0.5.0 stringi_1.7.12
[97] highr_0.10 lattice_0.21-8 Matrix_1.5-4.1
[100] vctrs_0.6.3 pillar_1.9.0 lifecycle_1.0.3
[103] jquerylib_0.1.4 data.table_1.14.8 cowplot_1.1.1
[106] bitops_1.0-7 httpuv_1.6.11 patchwork_1.1.2
[109] qvalue_2.30.0 R6_2.5.1 promises_1.2.0.1
[112] codetools_0.2-19 MASS_7.3-60 rprojroot_2.0.3
[115] withr_2.5.0 GenomeInfoDbData_1.2.9 parallel_4.2.2
[118] grid_4.2.2 ggfun_0.1.1 tidyr_1.3.0
[121] HDO.db_0.99.1 rmarkdown_2.23 carData_3.0-5
[124] ggnewscale_0.4.9 git2r_0.32.0 ggforce_0.4.1