Search
Question: Extracting results from a DSA fit to expression data with only a gene markers list
0
gravatar for nrubinstein
18 days ago by
nrubinstein0 wrote:

Hi,

 

I have log-scaled RNA-seq expression data from 13315 genes from 3 samples and a vector of cell-type markers, with 2553 genes corresponding to 8 cell types. 

 

I followed the CellMix tutorial and created an ExpressionMix object from my 13325 x 3 expression matrix (expression.mat) using the ExpressionMix function and setting the gene and sample names using the featureNames and sampleNames functions. I then used the MarkerList function to create a MarkerList object from my named cell-type character vector.

 

I deconvolved the data using DSA method, where the progress message I got is:

 

Using ged algorithm: "DSA"
 Estimating basis and mixture coefficients matrices from marker features [DSA]
 Using 1140/2553 markers to estimate cell proportions: 
                    astrocyte                     endothel                    microglia 
                          237                          142                           75 
  myelinating.oligodendrocyte                       neuron newly.formed.oligodendrocyte 
                          181                          130                          171 
              oligodendrocyte                          opc 
                            1                          203 
  Checking data scale ...   NOTE [log]
  Converting data to linear scale ...   OK [base: 2]
  Computing proportions using DSA method ...   OK
  Estimating basis matrix from mixture coefficients [qprog]
  Not using any marker constraints
Timing:
   user  system elapsed 
  9.455   0.000   9.342 
GED final wrap up ... OK

 

Then trying to plot the results using profplot(my ExpressionMix object, DSA fit object) throws this error:

Error in `rownames<-`(`*tmp*`, value = c("basis_1", "basis_0")) : 
  length of 'dimnames' [1] not equal to array extent

 

So my questions are:

1. Why am I getting this error

2. Is it possible to get a matrix/data.frame with the results per each sample or all samples other than the profplot? I guess that would probably be the contents of the legends in the profplot, but what do they actually mean?

 

 

> sessionInfo()
R version 3.3.2 (2016-10-31)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
 [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] corpcor_1.6.8        omicsUtils_0.1.0     yaml_2.1.14          bindrcpp_0.2        
 [5] hom.Hs.inp.db_3.1.2  rat2302.db_3.2.3     org.Rn.eg.db_3.4.0   BiocInstaller_1.24.0
 [9] org.Hs.eg.db_3.4.0   GEOquery_2.40.0      dplyr_0.7.2          CellMix_1.6.2       
[13] GSEABase_1.36.0      graph_1.50.0         annotate_1.52.0      XML_3.98-1.4        
[17] AnnotationDbi_1.36.0 IRanges_2.8.1        S4Vectors_0.12.1     stringr_1.2.0       
[21] csSAM_1.2.4          NMF_0.20.6           bigmemory_4.5.19     bigmemory.sri_0.1.3 
[25] Biobase_2.34.0       BiocGenerics_0.20.0  cluster_2.0.5        rngtools_1.2.4      
[29] pkgmaker_0.22        registry_0.3        

loaded via a namespace (and not attached):
  [1] Hmisc_3.17-4                  AnnotationHub_2.6.4           VGAM_1.0-3                   
  [4] plyr_1.8.4                    lazyeval_0.2.0                sp_1.2-3                     
  [7] splines_3.3.2                 BiocParallel_1.8.1            GenomeInfoDb_1.10.0          
 [10] ggplot2_2.2.1                 gridBase_0.4-7                digest_0.6.12                
 [13] foreach_1.4.3                 ensembldb_1.6.2               htmltools_0.3.6              
 [16] lmerTest_2.0-33               gdata_2.17.0                  magrittr_1.5                 
 [19] BSgenome_1.42.0               doParallel_1.0.10             Biostrings_2.42.1            
 [22] matrixStats_0.52.2            limSolve_1.5.5.3              sandwich_2.3-4               
 [25] lpSolve_5.6.13                colorspace_1.3-2              ggrepel_0.7.0                
 [28] jsonlite_1.4                  RCurl_1.95-4.8                genefilter_1.56.0            
 [31] lme4_1.1-12                   bindr_0.1                     survival_2.40-1              
 [34] VariantAnnotation_1.20.2      zoo_1.7-13                    iterators_1.0.8              
 [37] glue_1.1.1                    gtable_0.2.0                  zlibbioc_1.20.0              
 [40] XVector_0.14.0                scales_0.4.1.9002             DBI_0.5-1                    
 [43] bibtex_0.4.2                  Rcpp_0.12.13                  viridisLite_0.2.0            
 [46] xtable_1.8-2                  gage_2.24.0                   foreign_0.8-67               
 [49] preprocessCore_1.36.0         Formula_1.2-1                 htmlwidgets_0.8              
 [52] httr_1.2.1                    gplots_3.0.1                  RColorBrewer_1.1-2           
 [55] acepack_1.4.1                 modeltools_0.2-21             pkgconfig_2.0.1              
 [58] flexmix_2.3-13                Gviz_1.18.1                   nnet_7.3-12                  
 [61] labeling_0.3                  rlang_0.1.1                   reshape2_1.4.2               
 [64] munsell_0.4.3                 tools_3.3.2                   RSQLite_1.0.0                
 [67] betareg_3.1-0                 outliers_0.14                 knitr_1.16                   
 [70] caTools_1.17.1                purrr_0.2.2.2                 KEGGREST_1.14.0              
 [73] nlme_3.1-128                  mime_0.5                      UniProt.ws_2.14.0            
 [76] gageData_2.12.0               snpEnrichment_1.7.0           biomaRt_2.30.0               
 [79] doBy_4.5-15                   pbkrtest_0.4-6                plotly_4.7.0                 
 [82] beeswarm_0.2.3                png_0.1-7                     interactiveDisplayBase_1.12.0
 [85] tibble_1.3.3                  stringi_1.1.5                 GenomicFeatures_1.26.0       
 [88] lattice_0.20-34               Matrix_1.2-7.1                nloptr_1.0.4                 
 [91] lmtest_0.9-34                 snpStats_1.24.0               data.table_1.9.6             
 [94] bitops_1.0-6                  httpuv_1.3.3                  rtracklayer_1.34.1           
 [97] GenomicRanges_1.26.4          R6_2.2.2                      latticeExtra_0.6-28          
[100] KernSmooth_2.23-15            gridExtra_2.3                 codetools_0.2-15             
[103] dichromat_2.0-0               MASS_7.3-45                   gtools_3.5.0                 
[106] assertthat_0.2.0              chron_2.3-47                  SummarizedExperiment_1.2.3   
[109] GenomicAlignments_1.8.4       Rsamtools_1.26.1              quadprog_1.5-5               
[112] grid_3.3.2                    rpart_4.1-10                  minqa_1.2.4                  
[115] tidyr_0.7.1                   Rtsne_0.11                    biovizBase_1.22.0            
[118] annotationData_0.1.0          shiny_1.0.2                  
 
 

 

 

 

ADD COMMENTlink modified 18 days ago • written 18 days ago by nrubinstein0
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 2.2.0
Traffic: 221 users visited in the last hour