Last updated: 2023-12-03

Checks: 7 0

Knit directory: DEanalysis/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20230508)

The command set.seed(20230508) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: f3b8c1a

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version f3b8c1a. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Untracked files:
    Untracked:  .DS_Store
    Untracked:  .Rhistory
    Untracked:  data/.Rhistory
    Untracked:  data/10X_Kang_DEresult.RData
    Untracked:  data/10X_inputdata.RData
    Untracked:  data/10X_inputdata_DEresult.RData
    Untracked:  data/10X_inputdata_cpm.RData
    Untracked:  data/10X_inputdata_integrated.RData
    Untracked:  data/10X_inputdata_lognorm.RData
    Untracked:  data/10Xdata_annotate.rds
    Untracked:  data/Bcells.Rmd
    Untracked:  data/Bcellsce.rds
    Untracked:  data/data2sce.RData
    Untracked:  data/permutation.RData
    Untracked:  data/vstcounts.Rdata

Unstaged changes:
    Modified:   code/DE_methods.R
    Modified:   code/functions_in_rmd.R

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/FD_analysis.Rmd) and HTML (docs/FD_analysis.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd f3b8c1a C-HW 2023-12-03 wflow_publish("analysis/FD_analysis.Rmd")
html 59b08c2 C-HW 2023-11-29 update index, FD permuation, plots axes

To examine the p-value calibration in real data, we did a permutation on group-of-interest within a null dataset. The cells in the controlled group of B cells were randomly assigned to controlled or stimulated group. We then computed p-values of each gene with different methods. The gene set was restricted to the input genes of Poisson-glmm, and the threshold of Wilcox method was relaxed to prevent filtering out genes. The procedure was repeated 20 times. Each time the proportion of p-value smaller than 0.05 was computed, so as the false discovery DEGs.

From the violin plot below, our glmm methods and Wilcox method are well-calibrated. However, pseudo-bulk methods, MAST and mixed models from Muscat are too conservative. Their overall proportion is way less than 0.05. The histograms of all p-values in these 20 runs are flat for our glmm methods and Wilcox method, which satisfy the null setting. However, the p-values of the other methods are overestimated, resulting conservative results. With either current criteria or our new criteria to determine DEGs, every method detects at most one false discovery each run.

Version Author Date
59b08c2 C-HW 2023-11-29

Version Author Date
59b08c2 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] tidyr_1.3.0                 MAST_1.24.1                
 [3] muscat_1.12.1               SeuratObject_4.1.3         
 [5] Seurat_4.3.0.1              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] scattermore_1.2           bit64_4.0.5              
  [3] knitr_1.29                irlba_2.3.5.1            
  [5] DelayedArray_0.24.0       data.table_1.14.8        
  [7] KEGGREST_1.38.0           RCurl_1.98-1.12          
  [9] doParallel_1.0.17         generics_0.1.3           
 [11] ScaledMatrix_1.6.0        RhpcBLASctl_0.23-42      
 [13] cowplot_1.1.1             RSQLite_2.3.1            
 [15] RANN_2.6.1                future_1.33.0            
 [17] bit_4.0.5                 spatstat.data_3.0-1      
 [19] httpuv_1.6.11             viridis_0.6.3            
 [21] xfun_0.41                 hms_1.1.3                
 [23] jquerylib_0.1.4           evaluate_0.23            
 [25] promises_1.2.0.1          fansi_1.0.4              
 [27] progress_1.2.2            caTools_1.18.2           
 [29] igraph_1.5.0              DBI_1.1.3                
 [31] geneplotter_1.76.0        htmlwidgets_1.6.2        
 [33] spatstat.geom_3.2-4       purrr_1.0.1              
 [35] ellipsis_0.3.2            backports_1.4.1          
 [37] annotate_1.76.0           aod_1.3.2                
 [39] deldir_1.0-9              sparseMatrixStats_1.10.0 
 [41] vctrs_0.6.4               ROCR_1.0-11              
 [43] abind_1.4-5               cachem_1.0.8             
 [45] withr_2.5.0               progressr_0.13.0         
 [47] sctransform_0.3.5         prettyunits_1.1.1        
 [49] goftest_1.2-3             cluster_2.1.4            
 [51] lazyeval_0.2.2            crayon_1.5.2             
 [53] spatstat.explore_3.2-1    labeling_0.4.2           
 [55] edgeR_3.40.2              pkgconfig_2.0.3          
 [57] nlme_3.1-162              vipor_0.4.5              
 [59] blme_1.0-5                rlang_1.1.2              
 [61] globals_0.16.2            lifecycle_1.0.4          
 [63] miniUI_0.1.1.1            rsvd_1.0.5               
 [65] rprojroot_2.0.3           polyclip_1.10-4          
 [67] lmtest_0.9-40             Matrix_1.5-4.1           
 [69] carData_3.0-5             boot_1.3-28.1            
 [71] zoo_1.8-12                beeswarm_0.4.0           
 [73] whisker_0.4.1             ggridges_0.5.4           
 [75] GlobalOptions_0.1.2       png_0.1-8                
 [77] viridisLite_0.4.2         rjson_0.2.21             
 [79] bitops_1.0-7              KernSmooth_2.23-22       
 [81] Biostrings_2.66.0         blob_1.2.4               
 [83] DelayedMatrixStats_1.20.0 workflowr_1.7.0          
 [85] shape_1.4.6               stringr_1.5.1            
 [87] parallelly_1.36.0         spatstat.random_3.1-5    
 [89] remaCor_0.0.16            rstatix_0.7.2            
 [91] ggsignif_0.6.4            beachmat_2.14.2          
 [93] scales_1.2.1              memoise_2.0.1            
 [95] magrittr_2.0.3            plyr_1.8.8               
 [97] ica_1.0-3                 gplots_3.1.3             
 [99] zlibbioc_1.44.0           compiler_4.2.2           
[101] RColorBrewer_1.1-3        clue_0.3-64              
[103] lme4_1.1-34               DESeq2_1.38.3            
[105] fitdistrplus_1.1-11       cli_3.6.1                
[107] XVector_0.38.0            lmerTest_3.1-3           
[109] listenv_0.9.0             patchwork_1.1.2          
[111] pbapply_1.7-2             TMB_1.9.5                
[113] MASS_7.3-60               mgcv_1.9-0               
[115] tidyselect_1.2.0          stringi_1.8.2            
[117] yaml_2.3.7                BiocSingular_1.14.0      
[119] locfit_1.5-9.8            ggrepel_0.9.3            
[121] grid_4.2.2                sass_0.4.7               
[123] tools_4.2.2               future.apply_1.11.0      
[125] parallel_4.2.2            circlize_0.4.15          
[127] rstudioapi_0.15.0         foreach_1.5.2            
[129] git2r_0.32.0              EnvStats_2.8.0           
[131] farver_2.1.1              Rtsne_0.16               
[133] digest_0.6.33             shiny_1.7.4.1            
[135] Rcpp_1.0.11               car_3.1-2                
[137] broom_1.0.5               scuttle_1.8.4            
[139] later_1.3.1               RcppAnnoy_0.0.21         
[141] httr_1.4.6                AnnotationDbi_1.60.2     
[143] ComplexHeatmap_2.14.0     Rdpack_2.4               
[145] colorspace_2.1-0          XML_3.99-0.14            
[147] fs_1.6.3                  tensor_1.5               
[149] reticulate_1.30           splines_4.2.2            
[151] uwot_0.1.16               spatstat.utils_3.0-3     
[153] scater_1.26.1             sp_2.0-0                 
[155] plotly_4.10.2             xtable_1.8-4             
[157] jsonlite_1.8.7            nloptr_2.0.3             
[159] R6_2.5.1                  pillar_1.9.0             
[161] htmltools_0.5.5           mime_0.12                
[163] glue_1.6.2                fastmap_1.1.1            
[165] minqa_1.2.5               BiocParallel_1.32.6      
[167] BiocNeighbors_1.16.0      codetools_0.2-19         
[169] mvtnorm_1.2-2             utf8_1.2.3               
[171] lattice_0.21-8            bslib_0.5.0              
[173] spatstat.sparse_3.0-2     tibble_3.2.1             
[175] pbkrtest_0.5.2            numDeriv_2016.8-1.1      
[177] ggbeeswarm_0.7.2          leiden_0.4.3             
[179] gtools_3.9.4              survival_3.5-5           
[181] limma_3.54.2              glmmTMB_1.1.8            
[183] rmarkdown_2.23            munsell_0.5.0            
[185] GetoptLong_1.0.5          GenomeInfoDbData_1.2.9   
[187] iterators_1.0.14          variancePartition_1.28.9 
[189] reshape2_1.4.4            gtable_0.3.3             
[191] rbibutils_2.2.13