Error in array(NA_real_, dim = c(nrow(b), ncol(cc), length(lev)), dimnames = c(rownames(b), : length of 'dimnames' [1686] must match that of 'dims' [3]

[charlesgwellem]

Hi there,

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 not understand why. Can any one please help?

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
 OK
Timing:
   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

locale:
 [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                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=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       

[dudufan1992]

**Hi there,

I wish to analysis celltype specific differentially expressed genes with cell proportion though csSamfit.R, but I got the same problem with charlesgwellem.**

csfit_test <- bseqsc_csdiff(eset[1:5,] ~Timepoint+Infection | + B.Cells + CD4.T.Cells + CD8.T.Cells + Dendritic.Cells + Monocytes, verbose = 2, nperms = 50, .rng = 12345) Groups: Mex4482=15L | Anhui01=15L | NL219=15L | VN1203=14L | NA0L Cell type(s): 'B.Cells', 'CD4.T.Cells', ..., 'Monocytes' (5 total) Fitting mode: auto Data (filtered): 5 features x 59 samples Model has extra covariates: 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 50 permutations ... 51/50 Alternative 'two.sided' ... OK Alternative 'greater' ... OK Alternative 'less' ... OK OK Timing: user system elapsed 0.70 0.00 0.75 Error in array(NAreal, dim = c(nrow(b), ncol(cc), length(lev)), dimnames = c(rownames(b), : length of 'dimnames' [11] must match that of 'dims' [3]

length of 'dimnames' [11] six more than length of gene feature which I input. Looking forward to someone reply

[rammohanshukla]

Hi developers, I am getting the same issues while working with the example dataset given in the website, wondering if there is any solution to this

csfit <- bseqsc_csdiff(eset[genes, ] ~ gender + ageN + hba1c_class2 | alpha + beta + ductal + acinar, verbose = 2, nperms = 5000, .rng = 12345) Groups: Normal=47L | Hyper=23L | NA12L Cell type(s): 'alpha', 'beta', ..., 'acinar' (4 total) Warning: Dropping samples with NA group/value: 'GSM1216754', 'GSM1216756', ..., 'GSM1216831' [12] Fitting mode: auto Groups (filtered): Normal=47L | Hyper=23L | NA0L Data (filtered): 255 features x 70 samples Model has extra covariates: fitting lm interaction model Fitting model with nonnegative effects Fitting linear interaction model ... Error in cbind(covariates, D[, -1L, drop = FALSE]) : number of rows of matrices must match (see arg 2)