Please if anyone has experience with the use of the BSEQ-SC package for the deconvolution of bulk RNA sequencing data with single cell RNA sequencing data I will be very grateful for your suggestion.

I wish to deconvolve my bulk RNA seq data with single cell RNA seq data from the same tissue. I run in to the following error when running the code line below and I do understand the error but not how I can overcome it. Can any one please provide any useful hints?

csfit <- bseqsc_csdiff(ilc_bulk[genes, ] ~ treatment | fibroblast + macrophage, 
                       verbose = 2, nperms = 100, .rng = 12345)

Then comes the error:

   Groups: a_nacl1=4L | a_nacl2=4L | a_nacl3=4L | a_nacl4=4L | a_nacl5=4L | NA0L
Cell type(s): 'fibroblast', 'macrophage' (2 total)
Fitting mode: auto
Data (filtered): 1684 features x 20 samples
Model has factor effect with more than 2 levels: fitting lm interaction model
Fitting model with nonnegative effects
Model with more than 2 groups: switching to version 2
 Fitting linear interaction model ...  OK
 Computing FDR using 100 permutations ... 101/100
  Alternative 'two.sided' ...   OK
  Alternative 'greater' ...   OK
  Alternative 'less' ...   OK
   user  system elapsed 
  2.136   0.153   2.267 
Error in array(NA_real_, dim = c(nrow(b), ncol(cc), length(lev)), dimnames = c(rownames(b),  : 
  length of 'dimnames' [1687] must match that of 'dims' [3]

> sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=de_DE.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=de_DE.UTF-8       LC_NAME=C                 

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

other attached packages:
 [1] edgeR_3.26.6                limma_3.40.6                forcats_0.4.0               stringr_1.4.0              
 [5] purrr_0.3.2                 readr_1.3.1                 tidyr_0.8.3                 tibble_2.1.3               
 [9] tidyverse_1.2.1             SoupX_0.3.1                 ggplot2_3.2.0               dplyr_0.8.3                
[13] DropletUtils_1.4.3          SingleCellExperiment_1.6.0  SummarizedExperiment_1.14.0 DelayedArray_0.10.0        
[17] BiocParallel_1.18.0         matrixStats_0.54.0          GenomicRanges_1.36.0        GenomeInfoDb_1.20.0        
[21] RColorBrewer_1.1-2          xbioc_0.1.17                AnnotationDbi_1.46.0        IRanges_2.18.1             
[25] S4Vectors_0.22.0            BisqueRNA_1.0               Seurat_3.0.2                preprocessCore_1.46.0      
[29] e1071_1.7-2                 bseqsc_1.0                  csSAM_1.4                   Rcpp_1.0.2                 
[33] openxlsx_4.1.0.1            Biobase_2.44.0              BiocGenerics_0.30.0        

loaded via a namespace (and not attached):
  [1] reticulate_1.13        R.utils_2.9.0          tidyselect_0.2.5       RSQLite_2.1.2          htmlwidgets_1.3       
  [6] grid_3.6.1             Rtsne_0.15             devtools_2.1.0         munsell_0.5.0          codetools_0.2-16      
 [11] ica_1.0-2              future_1.14.0          withr_2.1.2            colorspace_1.4-1       rstudioapi_0.10       
 [16] ROCR_1.0-7             gbRd_0.4-11            listenv_0.7.0          NMF_0.22               Rdpack_0.11-0         
 [21] labeling_0.3           GenomeInfoDbData_1.2.1 bit64_0.9-7            rhdf5_2.28.0           rprojroot_1.3-2       
 [26] vctrs_0.2.0            generics_0.0.2         R6_2.4.0               doParallel_1.0.14      rsvd_1.0.2            
 [31] locfit_1.5-9.1         bitops_1.0-6           assertthat_0.2.1       SDMTools_1.1-221.1     scales_1.0.0          
 [36] gtable_0.3.0           npsurv_0.4-0           globals_0.12.4         processx_3.4.1         rlang_0.4.0           
 [41] zeallot_0.1.0          splines_3.6.1          lazyeval_0.2.2         broom_0.5.2            modelr_0.1.4          
 [46] BiocManager_1.30.4     yaml_2.2.0             reshape2_1.4.3         backports_1.1.4        tools_3.6.1           
 [51] usethis_1.5.1          gridBase_0.4-7         gplots_3.0.1.1         sessioninfo_1.1.1      ggridges_0.5.1        
 [56] plyr_1.8.4             zlibbioc_1.30.0        RCurl_1.95-4.12        ps_1.3.0               prettyunits_1.0.2     
 [61] pbapply_1.4-1          viridis_0.5.1          cowplot_1.0.0          zoo_1.8-6              haven_2.1.1           
 [66] ggrepel_0.8.1          cluster_2.1.0          fs_1.3.1               magrittr_1.5           data.table_1.12.2     
 [71] lmtest_0.9-37          RANN_2.6.1             fitdistrplus_1.0-14    pkgload_1.0.2          hms_0.5.0             
 [76] lsei_1.2-0             xtable_1.8-4           readxl_1.3.1           gridExtra_2.3          testthat_2.2.1        
 [81] compiler_3.6.1         KernSmooth_2.23-15     crayon_1.3.4           R.oo_1.22.0            htmltools_0.3.6       
 [86] Formula_1.2-3          lubridate_1.7.4        DBI_1.0.0              MASS_7.3-51.4          Matrix_1.2-17         
 [91] cli_1.1.0              R.methodsS3_1.7.1      gdata_2.18.0           metap_1.1              igraph_1.2.4.1        
 [96] pkgconfig_2.0.2        registry_0.5-1         plotly_4.9.0           xml2_1.2.1             foreach_1.4.7         
[101] dqrng_0.2.1            rngtools_1.4           pkgmaker_0.28          XVector_0.24.0         rvest_0.3.4           
[106] bibtex_0.4.2           callr_3.3.1            digest_0.6.20          sctransform_0.2.0      tsne_0.1-3            
[111] cellranger_1.1.0       dendextend_1.12.0      curl_4.0               gtools_3.8.1           nlme_3.1-141          
[116] jsonlite_1.6           Rhdf5lib_1.6.0         desc_1.2.0             viridisLite_0.3.0      pillar_1.4.2          
[121] lattice_0.20-38        httr_1.4.0             pkgbuild_1.0.3         survival_2.44-1.1      glue_1.3.1            
[126] remotes_2.1.0          zip_2.0.3              png_0.1-7              iterators_1.0.12       bit_1.1-14            
[131] class_7.3-15           stringi_1.4.3          HDF5Array_1.12.1       blob_1.2.0             caTools_1.17.1.2      
[136] memoise_1.1.0          irlba_2.3.3            future.apply_1.3.0     ape_5.3


1 Answer 1


Answer from @stupidwolf, @benn, @llrs, converted from comments:

The error complains about the 1686 rownames, while you have 1684 features. I think something is wrong with the naming of your rows.

This may be a bug that you should report to the package maintainer. I cannot really tell without more information about your eset. Looking at this line "Cell type(s): 'fibroblast' (1 total)", is it possible you only have one group under fibroblast? It will not make sense to run a random effect for only 1 group.

[please edit to improve this answer, if possible]


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.