Hello, 

I am trying to run somaticsignatures on mutation data.  

When I run the commands:

snpvr_mm = motifMatrix(snpvr_motif, group = "study", normalize = TRUE)

gof_nmf = identifySignatures(snpvr_mm, 4, decomposition = nmfDecomposition)

I get the following error:

Error: NMF::nmf - Input matrix x contains at least one null or NA-filled row.

The session info for R is:

R version 3.3.1 (2016-06-21)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1

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

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

other attached packages:
 [1] ggplot2_2.2.1              SomaticSignatures_2.8.4    VariantAnnotation_1.18.7   Rsamtools_1.24.0           Biostrings_2.40.2         
 [6] XVector_0.12.1             SummarizedExperiment_1.2.3 Biobase_2.32.0             GenomicRanges_1.24.3       GenomeInfoDb_1.8.7        
[11] IRanges_2.6.1              S4Vectors_0.10.3           BiocGenerics_0.18.0       

loaded via a namespace (and not attached):
 [1] httr_1.2.1                    foreach_1.4.3                 AnnotationHub_2.4.2           splines_3.3.1                 Formula_1.2-1                
 [6] shiny_1.0.0                   assertthat_0.1                interactiveDisplayBase_1.10.3 latticeExtra_0.6-28           RBGL_1.48.1                  
[11] BSgenome_1.40.1               RSQLite_1.1-2                 backports_1.0.5               lattice_0.20-34               biovizBase_1.20.0            
[16] digest_0.6.12                 RColorBrewer_1.1-2            checkmate_1.8.2               colorspace_1.3-2              ggbio_1.20.2                 
[21] htmltools_0.3.5               httpuv_1.3.3                  Matrix_1.2-8                  plyr_1.8.4                    OrganismDbi_1.14.1           
[26] XML_3.98-1.5                  biomaRt_2.28.0                zlibbioc_1.18.0               xtable_1.8-2                  scales_0.4.1                 
[31] BiocParallel_1.6.6            proxy_0.4-16                  htmlTable_1.9                 tibble_1.2                    pkgmaker_0.22                
[36] GenomicFeatures_1.24.5        nnet_7.3-12                   lazyeval_0.2.0                survival_2.40-1               magrittr_1.5                 
[41] mime_0.5                      memoise_1.0.0                 GGally_1.3.0                  doParallel_1.0.10             NMF_0.20.6                   
[46] foreign_0.8-67                graph_1.50.0                  BiocInstaller_1.22.3          registry_0.3                  tools_3.3.1                  
[51] data.table_1.10.4             gridBase_0.4-7                stringr_1.1.0                 munsell_0.4.3                 rngtools_1.2.4               
[56] cluster_2.0.5                 AnnotationDbi_1.34.4          ensembldb_1.4.7               pcaMethods_1.64.0             grid_3.3.1                   
[61] RCurl_1.95-4.8                iterators_1.0.8               dichromat_2.0-0               htmlwidgets_0.8               bitops_1.0-6                 
[66] base64enc_0.1-3               codetools_0.2-15              gtable_0.2.0                  DBI_0.5-1                     reshape_0.8.6                
[71] reshape2_1.4.2                R6_2.2.0                      GenomicAlignments_1.8.4       gridExtra_2.2.1               knitr_1.15.1                 
[76] rtracklayer_1.32.2            Hmisc_4.0-2                   stringi_1.1.2                 Rcpp_0.12.9                   rpart_4.1-10                 
[81] acepack_1.4.1                

Can the problem be identified?

Thanks!

> rowSums(motifMatrix(snpvr_motif, group = "study", normalize = FALSE))
CA A.A CA A.C CA A.G CA A.T CA C.A CA C.C CA C.G CA C.T CA G.A CA G.C CA G.G CA G.T CA T.A CA T.C CA T.G CA T.T CG A.A CG A.C CG A.G CG A.T CG C.A CG C.C 
    15      4      8      5      6      9      7      6      5      1      4      3     16      4      6     14      8      4      7      4      4      0 
CG C.G CG C.T CG G.A CG G.C CG G.G CG G.T CG T.A CG T.C CG T.G CG T.T CT A.A CT A.C CT A.G CT A.T CT C.A CT C.C CT C.G CT C.T CT G.A CT G.C CT G.G CT G.T 
     0      6      6      1      3      1      6      3      0      3     12      6      6     13     17      3      9      4      5      1      5      6 
CT T.A CT T.C CT T.G CT T.T TA A.A TA A.C TA A.G TA A.T TA C.A TA C.C TA C.G TA C.T TA G.A TA G.C TA G.G TA G.T TA T.A TA T.C TA T.G TA T.T TC A.A TC A.C 
    11      3      3     12     14      6     13     11      3      5      5      7      6      4      4      7     10      6     10     16      7     13 
TC A.G TC A.T TC C.A TC C.C TC C.G TC C.T TC G.A TC G.C TC G.G TC G.T TC T.A TC T.C TC T.G TC T.T TG A.A TG A.C TG A.G TG A.T TG C.A TG C.C TG C.G TG C.T 
    15     10      4      3      2      2     10      4      7     12      9      7     13     20      2      4      7      2      1      1      6      1 
TG G.A TG G.C TG G.G TG G.T TG T.A TG T.C TG T.G TG T.T 
     2      1      7      2      2      0     10      2 
> colSums(motifMatrix(snpvr_motif, group = "study", normalize = FALSE))
DJFS_1 DJFS_2 DJFS_3 DJFS_4 DJFS_5 DJFS_6 DJFS_7 DJFS_8 DJFS_9 
    77     57     58     68    121     74     44     58     43 

The error results from the fact that some mutational motifs have not been occurred in any of the samples, i.e. some of the rows of the motif matrix are fully zero. This prevents the decomposition of the matrix with the NMF. I'll try to capture this explicitly in the package and provide a more informative error message.

There are several potential approaches to address such cases, but looking at the excerpt from your data set, I doubt that any of them would be helpful here: The number of mutations per sample, and hence the signal of any mutational process, is very low, and I would not be confident that an analysis of this data would yield reliable or meaningful signatures. I would therefore suggest to think of other experimental designs for an analysis here, such as gathering data from more samples or pooling of samples to few distinct groups with higher number of variants.

Are all of the other NMF-based tools similarly limited by this situation (e.g. WTSI Mutational Signature Framework, MutSpec, BayesNMF, signeR)?

SomaticSignatures would be a really great tool for unbiased classification of individual tumors into different groups, but given the discussion above, it seems this approach is limited to hindsight signatures of pre-defined groups of tumors. I am running into the same issue despite my large test dataset of ~4500 SNVs.