Last updated: 2023-12-08

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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 null dataset 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 shown below, LEMUR glmm methods and Wilcox method are consistently well-calibrated among different choices of null datasets. However, pseudo-bulk methods, and mixed models from Muscat are too conservative. Their overall proportion is way less than 0.05. The performance of MAST is not consistent among three datasets, which is conservative in B cells but not in case study 1. The histograms of all p-values in these 20 runs are flat for LEMUR glmm methods and Wilcox method, which satisfy the null setting. However, the p-values of the other methods are overestimated, resulting conservative results. Note that even though Wilcox performs well in the permutation analysis, it is not powerful to detect real DEGs. With either current criteria or our new criteria to determine DEGs, every method detects at most one false discovery each run.

Different choices of null sets

Cluster 2 in case study 1

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

Cluster 13 in case study 1

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

The control group of B cells in case study 2


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