I used the dexus function from the dexus package on a matrix of count data that had previously been normalized. After filtering the responsibilities matrix in the resulting object according to those genes labelled as "informative", roughly 3% of the remaining genes have exactly zero samples falling under the secondary condition. If a gene is labelled "informative" in the dexus object, shouldn't it have a mixture of samples across both conditions?

The following output shows the function call used to generate the object and that 72 of my remaining genes are labelled with the main condition for each sample.

> dexRes_cpm <- dexus.parallel(resCPM, ncores = 16, normalization = "none")
> INIobject <- INI(dexRes_cpm)
> length(which(rowSums(INIobject@responsibilities) == ncol(INIobject@responsibilities)))
[1] 72

Thanks in advance for any help that may be provided. Below you'll find my sessionInfo and Bioconductor version.

BiocInstaller::biocVersion() 
[1] ‘3.7’ 
sessionInfo() 
R version 3.5.0 (2018-04-23) 
Platform: x86_64-redhat-linux-gnu (64-bit) 
Running under: CentOS release 6.9 (Final)

Matrix products: 
default 
BLAS: /usr/lib64/R/lib/libRblas.so 
LAPACK: /usr/lib64/R/lib/libRlapack.so

locale: 
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C

[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8

[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8

[7] LC_PAPER=en_US.UTF-8 LC_NAME=C

[9] LC_ADDRESS=C LC_TELEPHONE=C

[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

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

[7] datasets methods base

other attached packages: 
[1] bindrcpp_0.2.2 tibble_1.4.2

[3] edgeR_3.22.3 limma_3.36.2

[5] SummarizedExperiment_1.10.1 DelayedArray_0.6.1

[7] BiocParallel_1.14.1 matrixStats_0.53.1

[9] Biobase_2.40.0 GenomicRanges_1.32.3

[11] GenomeInfoDb_1.16.0 IRanges_2.14.10

[13] S4Vectors_0.18.3 ggplot2_3.0.0.9000

[15] dplyr_0.7.5 statmod_1.4.30

[17] dexus_1.20.0 BiocGenerics_0.26.0

loaded via a namespace (and not attached): 
[1] Rcpp_0.12.17 BiocInstaller_1.30.0

[3] plyr_1.8.4 compiler_3.5.0

[5] pillar_1.2.3 XVector_0.20.0

[7] bindr_0.1.1 bitops_1.0-6

[9] tools_3.5.0 zlibbioc_1.26.0

[11] gtable_0.2.0 lattice_0.20-35

[13] pkgconfig_2.0.1 rlang_0.2.1

[15] Matrix_1.2-14 yaml_2.1.19

[17] GenomeInfoDbData_1.1.0 withr_2.1.2

[19] knitr_1.20 locfit_1.5-9.1

[21] grid_3.5.0 tidyselect_0.2.4

[23] glue_1.2.0 R6_2.2.2

[25] purrr_0.2.5 magrittr_1.5

[27] scales_0.5.0 assertthat_0.2.0

[29] colorspace_1.3-2 lazyeval_0.2.1

[31] munsell_0.5.0 RCurl_1.95-4.10