Hi,

I have a 2 factor experiment (2 mutations:A,B), with 2 levels (+/-) in each factor. All together there 4 conditions: wt, A, B, and A+B , with two replicates for each condition. I tried to use LRT test to isolate genes that are correspond to the mutations without interaction. the full model I used is ~A+B+A:B and the reduced is ~A:B I received the following error:

Error in nbinomLRT(object, full = full, reduced = reduced, quiet = quiet,  : 
  less than one degree of freedom, perhaps full and reduced models are not in the correct order

why are the degrees of freedom less than 1? Below I pasted the sessionInfo output. Thanks in advance.

Guy

R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)

Matrix products: default

Random number generation:
 RNG:     Mersenne-Twister 
 Normal:  Inversion 
 Sample:  Rounding 

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

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

other attached packages:
 [1] ggplotify_0.0.5             gridExtra_2.3              
 [3] dplyr_1.0.0                 DESeq2_1.28.1              
 [5] SummarizedExperiment_1.18.1 DelayedArray_0.14.0        
 [7] matrixStats_0.56.0          Biobase_2.48.0             
 [9] GenomicRanges_1.40.0        GenomeInfoDb_1.24.2        
[11] IRanges_2.22.2              S4Vectors_0.26.1           
[13] BiocGenerics_0.34.0         ggplot2_3.3.2              
[15] readxl_1.3.1                data.table_1.12.8          

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.4.6           locfit_1.5-9.4        
 [3] lattice_0.20-41        assertthat_0.2.1      
 [5] digest_0.6.25          R6_2.4.1              
 [7] cellranger_1.1.0       RSQLite_2.2.0         
 [9] pillar_1.4.4           zlibbioc_1.34.0       
[11] rlang_0.4.6            rstudioapi_0.11       
[13] annotate_1.66.0        blob_1.2.1            
[15] Matrix_1.2-18          labeling_0.3          
[17] splines_4.0.2          BiocParallel_1.22.0   
[19] geneplotter_1.66.0     pheatmap_1.0.12       
[21] RCurl_1.98-1.2         bit_1.1-15.2          
[23] munsell_0.5.0          compiler_4.0.2        
[25] pkgconfig_2.0.3        gridGraphics_0.5-0    
[27] tidyselect_1.1.0       tibble_3.0.1          
[29] GenomeInfoDbData_1.2.3 XML_3.99-0.4          
[31] fansi_0.4.1            crayon_1.3.4          
[33] withr_2.2.0            bitops_1.0-6          
[35] xtable_1.8-4           gtable_0.3.0          
[37] lifecycle_0.2.0        DBI_1.1.0             
[39] magrittr_1.5           scales_1.1.1          
[41] cli_2.0.2              farver_2.0.3          
[43] XVector_0.28.0         genefilter_1.70.0     
[45] ellipsis_0.3.1         rvcheck_0.1.8         
[47] generics_0.0.2         vctrs_0.3.1           
[49] RColorBrewer_1.1-2     tools_4.0.2           
[51] bit64_0.9-7            glue_1.4.1            
[53] purrr_0.3.4            survival_3.1-12       
[55] AnnotationDbi_1.50.1   colorspace_1.4-1      
[57] BiocManager_1.30.10    memoise_1.1.0

The reduced model is the null model, but here you've removed the main effects and left only the interaction. Take a look at the LRT section of the vignette.