Group8_17
Chih-Hsuan Wu
Last updated: 2023-12-10
Checks: 7 0
Knit directory: DEanalysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | ae4b56c | C-HW | 2023-12-10 | adjust ylim of p-value histogram |
| html | ac027b0 | C-HW | 2023-12-09 | Build site. |
| Rmd | 582be29 | C-HW | 2023-12-09 | update new DE results |
| html | 2a17159 | C-HW | 2023-12-05 | Build site. |
| Rmd | e7f3de4 | C-HW | 2023-12-05 | fix Wilcox log2FC sign |
| html | bc13544 | C-HW | 2023-12-04 | Build site. |
| Rmd | 39196a3 | C-HW | 2023-12-04 | fix log2FC sign |
| html | 42900f0 | C-HW | 2023-12-01 | Build site. |
| Rmd | c774a2d | C-HW | 2023-12-01 | modify method title |
| Rmd | 031d955 | C-HW | 2023-12-01 | create t score comparison |
| html | 031d955 | C-HW | 2023-12-01 | create t score comparison |
| Rmd | 3803697 | C-HW | 2023-12-01 | upload rmd |
| html | 59b08c2 | C-HW | 2023-11-29 | update index, FD permuation, plots axes |
| html | e8b0519 | C-HW | 2023-11-29 | update all pairs |
| html | d7d838c | C-HW | 2023-08-11 | update graph |
| html | ac090b4 | C-HW | 2023-08-03 | update 8_17 heatmap |
| html | 0e93efd | C-HW | 2023-07-26 | upload again |
| html | f314434 | C-HW | 2023-07-26 | add muscat methods |
| html | 7ee9782 | C-HW | 2023-07-13 | add 8_17 |
Data summary
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
Heatmap of top DEGs
Poisson-glmm DEGs
UMI counts
VST data
CPM data
Additional DEGs from other methods
pb-DESeq2
Binomial-glmm
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.
Enrichment analysis
GO object
enrichKEGG object
| Version | Author | Date |
|---|---|---|
| ac027b0 | C-HW | 2023-12-09 |
| Version | Author | Date |
|---|---|---|
| ac027b0 | C-HW | 2023-12-09 |
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_0.41
[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_1.1.2
[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_0.23
[73] stringr_1.5.1 fastmap_1.1.1 yaml_2.3.7
[76] ggtree_3.6.2 knitr_1.29 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_1.8.2
[97] lattice_0.21-8 Matrix_1.5-4.1 vctrs_0.6.4
[100] pillar_1.9.0 lifecycle_1.0.4 jquerylib_0.1.4
[103] data.table_1.14.8 cowplot_1.1.1 bitops_1.0-7
[106] httpuv_1.6.11 patchwork_1.1.2 qvalue_2.30.0
[109] R6_2.5.1 promises_1.2.0.1 codetools_0.2-19
[112] MASS_7.3-60 rprojroot_2.0.3 withr_2.5.0
[115] GenomeInfoDbData_1.2.9 parallel_4.2.2 grid_4.2.2
[118] ggfun_0.1.1 tidyr_1.3.0 HDO.db_0.99.1
[121] rmarkdown_2.23 carData_3.0-5 ggnewscale_0.4.9
[124] git2r_0.32.0 ggforce_0.4.1