Seurat object can't be imported into monocle:the object type you want to export to is not supported yet

Question

[yuhongruoyi]

Hello,

I was wondering why my Seurat object can't be imported into monocle? I follow the official website to do it, but it turns out that the importCDS doesn't take that.

Below is the R code and my sessioninfo:

If you do know of how to might help us, that would be incredibly helpful!

Thank you! Sophia

> library(monocle)

mo <- importCDS(tc, import_all = TRUE) Error in importCDS(tc, import_all = TRUE) : the object type you want to export to is not supported yet mo <- importCDS(tc) Error in importCDS(tc) : the object type you want to export to is not supported yet sessionInfo() R version 3.5.2 (2018-12-20) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows >= 8 x64 (build 9200)

Matrix products: default

locale: [1] LC_COLLATE=Chinese (Simplified)_China.936 LC_CTYPE=Chinese (Simplified)_China.936 LC_MONETARY=Chinese (Simplified)_China.936 [4] LC_NUMERIC=C LC_TIME=Chinese (Simplified)_China.936

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

other attached packages: [1] Seurat_3.0.0.9000 monocle_2.8.0 DDRTree_0.1.5 irlba_2.3.3 VGAM_1.1-1 ggplot2_3.1.0 Biobase_2.40.0
[8] BiocGenerics_0.26.0 BiocInstaller_1.30.0 Matrix_1.2-15

loaded via a namespace (and not attached): [1] Rtsne_0.15 colorspace_1.4-0 ggridges_0.5.1 rprojroot_1.3-2 fs_1.2.6 rstudioapi_0.9.0
[7] listenv_0.7.0 npsurv_0.4-0 remotes_2.0.2 ggrepel_0.8.0 codetools_0.2-16 R.methodsS3_1.7.1
[13] docopt_0.6.1 lsei_1.2-0 pkgload_1.0.2 jsonlite_1.6 ica_1.0-2 cluster_2.0.7-1
[19] png_0.1-7 R.oo_1.22.0 pheatmap_1.0.12 httr_1.4.0 compiler_3.5.2 backports_1.1.3
[25] assertthat_0.2.0 lazyeval_0.2.1 limma_3.36.5 cli_1.0.1 htmltools_0.3.6 prettyunits_1.0.2
[31] tools_3.5.2 rsvd_1.0.0 igraph_1.2.4 gtable_0.2.0 glue_1.3.0 RANN_2.6.1
[37] reshape2_1.4.3 dplyr_0.8.0.1 Rcpp_1.0.0 slam_0.1-44 gdata_2.18.0 ape_5.2
[43] nlme_3.1-137 gbRd_0.4-11 lmtest_0.9-36 stringr_1.4.0 globals_0.12.4 ps_1.3.0
[49] testthat_2.0.1 gtools_3.8.1 devtools_2.0.1 future_1.11.1.1 MASS_7.3-51.1 zoo_1.8-4
[55] scales_1.0.0 RColorBrewer_1.1-2 yaml_2.2.0 reticulate_1.10 memoise_1.1.0 pbapply_1.4-0
[61] gridExtra_2.3 fastICA_1.2-1 stringi_1.3.1 desc_1.2.0 caTools_1.17.1.1 densityClust_0.3
[67] pkgbuild_1.0.2 bibtex_0.4.2 Rdpack_0.10-1 SDMTools_1.1-221 rlang_0.3.1 pkgconfig_2.0.2
[73] matrixStats_0.54.0 bitops_1.0-6 qlcMatrix_0.9.7 lattice_0.20-38 ROCR_1.0-7 purrr_0.3.0
[79] htmlwidgets_1.3 cowplot_0.9.4 processx_3.2.1 tidyselect_0.2.5 plyr_1.8.4 magrittr_1.5
[85] R6_2.4.0 gplots_3.0.1.1 combinat_0.0-8 pillar_1.3.1 withr_2.1.2 fitdistrplus_1.0-14
[91] survival_2.43-3 tsne_0.1-3 tibble_2.0.1 future.apply_1.1.0 crayon_1.3.4 KernSmooth_2.23-15
[97] plotly_4.8.0 viridis_0.5.1 usethis_1.4.0 grid_3.5.2 data.table_1.12.0 FNN_1.1.3
[103] callr_3.1.1 HSMMSingleCell_0.114.0 metap_1.1 sparsesvd_0.1-4 digest_0.6.18 tidyr_0.8.2
[109] R.utils_2.8.0 munsell_0.5.0 viridisLite_0.3.0 sessioninfo_1.1.1

[Kevin Blighe]

I would restart the computer and then, in R, run BiocManager::install() to ensure that everything is up to date. Then specifically also re-install monocle and seurat.

[ash274]

I also am having this issue. I have performed some filtering in Seurat and wish to import my Seurat object. > HH10_monocle <- importCDS(HH10, import_all = T)

Error in importCDS(HH10, import_all = T) : the object type you want to export to is not supported yet

Any advice would be welcome!

> attributes(HH10)

$assays
$assays$RNA
Assay data with 11952 features for 2044 cells


$meta.data
                    orig.ident nCount_RNA nFeature_RNA percent.mito
AAACCTGCAATGGACG SeuratProject       1165          640 0.0042918455
....

$active.assay
[1] "RNA"

$active.ident
AAACCTGCAATGGACG
...

Levels: SeuratProject

$graphs
list()

$neighbors
list()

$reductions
list()

$project.name
[1] "SeuratProject"

$misc
list()

$version
[1] ‘3.0.0.9000’

$commands
list()

$tools
list()

$class
[1] "Seurat"
attr(,"package")
[1] "Seurat"`




R version 3.5.2 (2018-12-20)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.2 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=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8   
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] monocle_2.99.3        L1Graph_0.1.1         lpSolveAPI_5.5.2.0-17 DDRTree_0.1.5         irlba_2.3.3           igraph_1.2.4         
 [7] ggplot2_3.1.0         DelayedArray_0.8.0    BiocParallel_1.16.5   matrixStats_0.54.0    Seurat_3.0.0.9000     Rmagic_1.4.0         
[13] Matrix_1.2-15         org.Gg.eg.db_3.7.0    AnnotationDbi_1.44.0  IRanges_2.16.0        S4Vectors_0.20.1      Biobase_2.42.0       
[19] BiocGenerics_0.28.0   dplyr_0.8.0.1        

loaded via a namespace (and not attached):

Answer 1

[lina.kroehling]

I think this has to do with the update from Seurat2 to Seurat3, which changes where data is stored. It worked for me after updating the import script.

I updated the import script for monocle so it refers to the Seurat3 objects and slots, so you can just run that in R first (below):

and following that did:

seurat_data <- newimport(SeuratObject)

###
newimport <- function(otherCDS, import_all = FALSE) {
  if(class(otherCDS)[1] == 'Seurat') {
    requireNamespace("Seurat")
    data <- otherCDS@assays$RNA@counts

    if(class(data) == "data.frame") {
      data <- as(as.matrix(data), "sparseMatrix")
    }

    pd <- tryCatch( {
      pd <- new("AnnotatedDataFrame", data = [e-mail])
      pd
    }, 
    #warning = function(w) { },
    error = function(e) { 
      pData <- data.frame(cell_id = colnames(data), row.names = colnames(data))
      pd <- new("AnnotatedDataFrame", data = pData)

      message("This Seurat object doesn't provide any meta data");
      pd
    })

    # remove filtered cells from Seurat
    if(length(setdiff(colnames(data), rownames(pd))) > 0) {
      data <- data[, rownames(pd)]  
    }

    fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))
    fd <- new("AnnotatedDataFrame", data = fData)
    lowerDetectionLimit <- 0

    if(all(data == floor(data))) {
      expressionFamily <- negbinomial.size()
    } else if(any(data < 0)){
      expressionFamily <- uninormal()
    } else {
      expressionFamily <- tobit()
    }

    valid_data <- data[, row.names(pd)]

    monocle_cds <- newCellDataSet(data,
                                  phenoData = pd, 
                                  featureData = fd,
                                  lowerDetectionLimit=lowerDetectionLimit,
                                  expressionFamily=expressionFamily)

    if(import_all) {
      if("Monocle" %in% names(otherCDS@misc)) {
        otherCDS@misc$Monocle@auxClusteringData$seurat <- NULL
        otherCDS@misc$Monocle@auxClusteringData$scran <- NULL

        monocle_cds <- otherCDS@misc$Monocle
        mist_list <- otherCDS

      } else {
        # mist_list <- list(ident = ident) 
        mist_list <- otherCDS
      }
    } else {
      mist_list <- list()
    }

    if(1==1) {
      var.genes <- setOrderingFilter(monocle_cds, otherCDS@assays$[e-mail])

    }
    monocle_cds@auxClusteringData$seurat <- mist_list

  } else if (class(otherCDS)[1] == 'SCESet') {
    requireNamespace("scater")

    message('Converting the exprs data in log scale back to original scale ...')    
    data <- 2^otherCDS@assayData$exprs - otherCDS@logExprsOffset

    fd <- otherCDS@featureData
    pd <- otherCDS@phenoData
    experimentData = otherCDS@experimentData
    if("is.expr" %in% slotNames(otherCDS))
      lowerDetectionLimit <- [e-mail]
    else 
      lowerDetectionLimit <- 1

    if(all(data == floor(data))) {
      expressionFamily <- negbinomial.size()
    } else if(any(data < 0)){
      expressionFamily <- uninormal()
    } else {
      expressionFamily <- tobit()
    }

    if(import_all) {
      # mist_list <- list(iotherCDS@sc3,
      #                   otherCDS@reducedDimension)
      mist_list <- otherCDS 

    } else {
      mist_list <- list()
    }

    monocle_cds <- newCellDataSet(data,
                                  phenoData = pd, 
                                  featureData = fd,
                                  lowerDetectionLimit=lowerDetectionLimit,
                                  expressionFamily=expressionFamily)
    # monocle_cds@auxClusteringData$sc3 <- otherCDS@sc3
    # monocle_cds@auxOrderingData$scran <- mist_list

    monocle_cds@auxOrderingData$scran <- mist_list

  } else {
    stop('the object type you want to export to is not supported yet')
  }

  return(monocle_cds)
}