Hi,

I was using MAST zlm function to fit scRNA-Seq (logCPM) data. The model I used is:

zlm(~ disease +patient, sca). Both disease and patient are categorical variables. Disease is the disease type and patient is patient ID. We were trying to assess the patients fixed effect. However, we got the warning: Coefficients patient207, patient230 are never estimible and will be dropped.

table(patient, disease)

        AD    N   PV
  165    0    0  204
  169    0 1057    0
  170  282    0    0
  173    0    0   99
  194    0    0   85
  198  114    0    0
  199    0    0  195
  204    0  662    0
  207    0  412    0
  211    0    0  216
  222    0    0  353
  230  592    0    0

So when I tried to remove patient 207 and 230 to rerun the model, similar warning appears: Coefficients patient198, patient204 are never estimible and will be dropped. Any suggestions? Thanks!

sessionInfo( )
R version 4.0.3 (2020-10-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19041)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] SeuratObject_4.0.0          Seurat_4.0.0                MAST_1.16.0                
 [4] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0 Biobase_2.50.0             
 [7] GenomicRanges_1.42.0        GenomeInfoDb_1.26.1         IRanges_2.24.0             
[10] S4Vectors_0.28.0            BiocGenerics_0.36.0         MatrixGenerics_1.2.0       
[13] matrixStats_0.57.0          ggplot2_3.3.2               dplyr_1.0.2                

loaded via a namespace (and not attached):
  [1] Rtsne_0.15             minqa_1.2.4            colorspace_2.0-0      
  [4] deldir_0.2-3           ellipsis_0.3.1         ggridges_0.5.2        
  [7] XVector_0.30.0         spatstat.data_1.5-2    rstudioapi_0.13       
 [10] leiden_0.3.5           listenv_0.8.0          farver_2.0.3          
 [13] ggrepel_0.8.2          fansi_0.4.1            codetools_0.2-16      
 [16] splines_4.0.3          knitr_1.30             polyclip_1.10-0       
 [19] jsonlite_1.7.1         nloptr_1.2.2.2         ica_1.0-2             
 [22] cluster_2.1.0          png_0.1-7              uwot_0.1.9            
 [25] shiny_1.5.0            sctransform_0.3.2      compiler_4.0.3        
 [28] httr_1.4.2             assertthat_0.2.1       lazyeval_0.2.2        
 [31] Matrix_1.2-18          fastmap_1.0.1          cli_2.2.0             
 [34] later_1.1.0.1          htmltools_0.5.0        tools_4.0.3           
 [37] igraph_1.2.6           gtable_0.3.0           glue_1.4.2            
 [40] GenomeInfoDbData_1.2.4 RANN_2.6.1             reshape2_1.4.4        
 [43] spatstat_1.64-1        Rcpp_1.0.5             scattermore_0.7       
 [46] vctrs_0.3.5            nlme_3.1-149           lmtest_0.9-38         
 [49] xfun_0.19              stringr_1.4.0          globals_0.14.0        
 [52] lme4_1.1-25            mime_0.9               miniUI_0.1.1.1        
 [55] lifecycle_0.2.0        irlba_2.3.3            goftest_1.2-2         
 [58] statmod_1.4.35         future_1.20.1          zlibbioc_1.36.0       
 [61] MASS_7.3-53            zoo_1.8-8              scales_1.1.1          
 [64] spatstat.utils_1.17-0  promises_1.1.1         RColorBrewer_1.1-2    
 [67] yaml_2.2.1             reticulate_1.18        pbapply_1.4-3         
 [70] gridExtra_2.3          rpart_4.1-15           stringi_1.5.3         
 [73] boot_1.3-25            rlang_0.4.9            pkgconfig_2.0.3       
 [76] bitops_1.0-6           evaluate_0.14          lattice_0.20-41       
 [79] tensor_1.5             ROCR_1.0-11            purrr_0.3.4           
 [82] patchwork_1.1.0        htmlwidgets_1.5.2      labeling_0.4.2        
 [85] cowplot_1.1.0          tidyselect_1.1.0       parallelly_1.21.0     
 [88] RcppAnnoy_0.0.18       plyr_1.8.6             magrittr_2.0.1        
 [91] R6_2.5.0               generics_0.1.0         DelayedArray_0.16.0   
 [94] mgcv_1.8-33            pillar_1.4.7           withr_2.3.0           
 [97] fitdistrplus_1.1-1     survival_3.2-7         abind_1.4-5           
[100] RCurl_1.98-1.2         tibble_3.0.4           future.apply_1.6.0    
[103] crayon_1.3.4           KernSmooth_2.23-17     plotly_4.9.2.1        
[106] rmarkdown_2.5          grid_4.0.3             data.table_1.13.2     
[109] digest_0.6.27          xtable_1.8-4           tidyr_1.1.2           
[112] httpuv_1.5.4           munsell_0.5.0          viridisLite_0.3.0     
[115] sessioninfo_1.1.1