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SingleR annotation of seurat clusters

Hello!

I am running the following code in order to annotate my seurat clusters

SingleR(GetAssayData(seu_fin, assay = "RNA", slot = "data"), clusters = Idents(seu_fin),ref = immgen, labels = immgen$label.fine)

or

SingleR(as.SingleCellExperiment(seu_fin), ref=immgen, labels=immgen$label.main, clusters=Idents(seu_fin))

but I am getting in both cases i get the following error:

Error in `rownames<-`(`*tmp*`, value = tolower(rownames(ref_data))) : 
  attempt to set 'rownames' on an object with no dimensions

Is any way to deal wit this? Thanks :)

seurat singler 10x
• 6,083 views • link
updated by jared.andrews07 jared.andrews07 • written by chi.delta • chi.delta

We need the full code used (to retrieve the reference data, etc), and your sessionInfo(). This is a crosspost of the github issue (which is fine, Aaron is just more likely to figure it out and respond there before I do here, so a trail is helpful).

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written by jared.andrews07 jared.andrews07

Thanks a lot for your prompt response, here is the code to generate my seurat clusters:

my_counts<-Read10X(data.dir = "~/experiment/data")

#Creat seurat RNA object
seu_all = CreateSeuratObject(counts = my_counts$`Gene Expression`,min.cells = 3)

#Creat seurat cite seq assay
surface_assay<-CreateAssayObject(counts = my_counts$`Antibody Capture`)

# Split by hashtag/CITE-seq
cite_assay <- CreateAssayObject(counts=surface_assay[1:35,])
hashtag_assay <- CreateAssayObject(surface_assay[36:39,])


#Import Cite seq assay in RNA object
seu_all[["Protein"]]<-cite_assay
seu_all[["Hashtag"]]<-hashtag_assay

seu_all<-NormalizeData(seu_all,assay = "Protein",normalization.method = "CLR")
seu_all<-NormalizeData(seu_all,assay = "Hashtag",normalization.method = "CLR")

##Here I filtered out dead cells, empty droplets, doublets and outliers##

## Clustering

seu_all<-FindVariableFeatures(seu_all)
seu_all <- ScaleData(seu_all,  features = rownames(seu_all))
seu_all<-RunPCA(seu_all,npcs = 80)

ElbowPlot(seu_all,ndims =80)

seu_all<-FindNeighbors(seu_all,dims = 1:60)
seu_all<-FindClusters(seu_all ,resolution = 0.6)
seu_all<-RunUMAP(seu_all,dims = 1:60)

and my session info:

R version 3.6.0 (2019-04-26)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             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] SingleR_1.0.1               readxl_1.3.1                Matrix_1.3-2               
 [4] forcats_0.5.1               stringr_1.4.0               purrr_0.3.4                
 [7] readr_1.4.0                 tidyr_1.1.3                 tibble_3.1.2               
[10] tidyverse_1.3.0             dplyr_1.0.7                 RColorBrewer_1.1-2         
[13] viridis_0.5.1               viridisLite_0.4.0           scater_1.14.6              
[16] ggplot2_3.3.4               scran_1.14.6                SingleCellExperiment_1.8.0 
[19] SummarizedExperiment_1.16.1 DelayedArray_0.12.3         BiocParallel_1.20.1        
[22] matrixStats_0.58.0          Biobase_2.46.0              GenomicRanges_1.38.0       
[25] GenomeInfoDb_1.22.1         IRanges_2.20.2              S4Vectors_0.24.4           
[28] BiocGenerics_0.32.0         Seurat_3.1.3               

loaded via a namespace (and not attached):
  [1] utf8_1.2.1               reticulate_1.18          tidyselect_1.1.1         RSQLite_2.2.5           
  [5] AnnotationDbi_1.48.0     htmlwidgets_1.5.3        grid_3.6.0               Rtsne_0.15              
  [9] devtools_2.3.2           munsell_0.5.0            codetools_0.2-18         mutoss_0.1-12           
 [13] ica_1.0-2                statmod_1.4.35           future_1.21.0            withr_2.4.2             
 [17] colorspace_2.0-1         rstudioapi_0.13          ROCR_1.0-11              pbmcapply_1.5.0         
 [21] listenv_0.8.0            Rdpack_2.1.1             labeling_0.4.2           GenomeInfoDbData_1.2.2  
 [25] mnormt_2.0.2             bit64_4.0.5              pheatmap_1.0.12          farver_2.1.0            
 [29] rprojroot_2.0.2          parallelly_1.24.0        vctrs_0.3.8              generics_0.1.0          
 [33] TH.data_1.0-10           doParallel_1.0.16        R6_2.5.0                 ggbeeswarm_0.6.0        
 [37] rsvd_1.0.3               locfit_1.5-9.4           bitops_1.0-6             cachem_1.0.4            
 [41] assertthat_0.2.1         promises_1.2.0.1         scales_1.1.1             multcomp_1.4-16         
 [45] beeswarm_0.3.1           gtable_0.3.0             globals_0.14.0           processx_3.5.0          
 [49] sandwich_3.0-0           rlang_0.4.11             splines_3.6.0            lazyeval_0.2.2          
 [53] broom_0.7.5              BiocManager_1.30.12      reshape2_1.4.4           modelr_0.1.8            
 [57] backports_1.2.1          httpuv_1.5.5             tools_3.6.0              usethis_2.0.1           
 [61] ellipsis_0.3.2           sessioninfo_1.1.1        ggridges_0.5.3           TFisher_0.2.0           
 [65] Rcpp_1.0.6               plyr_1.8.6               zlibbioc_1.32.0          RCurl_1.98-1.3          
 [69] ps_1.6.0                 prettyunits_1.1.1        pbapply_1.4-3            cowplot_1.1.1           
 [73] zoo_1.8-9                haven_2.3.1              ggrepel_0.9.1            cluster_2.1.1           
 [77] fs_1.5.0                 magrittr_2.0.1.9000      data.table_1.14.0        lmtest_0.9-38           
 [81] reprex_1.0.0             RANN_2.6.1               tmvnsim_1.0-2            mvtnorm_1.1-1           
 [85] fitdistrplus_1.1-3       pkgload_1.2.0            mime_0.10                GSVA_1.34.0             
 [89] xtable_1.8-4             hms_1.0.0                XML_3.99-0.3             gridExtra_2.3           
 [93] testthat_3.0.2           compiler_3.6.0           KernSmooth_2.23-18       crayon_1.4.1            
 [97] htmltools_0.5.1.1        later_1.1.0.1            geneplotter_1.64.0       lubridate_1.7.10        
[101] DBI_1.1.1                dbplyr_2.1.0             MASS_7.3-53.1            cli_2.5.0               
[105] rbibutils_2.0            metap_1.4                igraph_1.2.6             pkgconfig_2.0.3         
[109] sn_2.0.0                 numDeriv_2016.8-1.1      plotly_4.9.3             foreach_1.5.1           
[113] xml2_1.3.2               annotate_1.64.0          vipor_0.4.5              dqrng_0.2.1             
[117] multtest_2.42.0          XVector_0.26.0           doFuture_0.12.0          rvest_1.0.0             
[121] callr_3.6.0              digest_0.6.27            sctransform_0.3.2        RcppAnnoy_0.0.18        
[125] tsne_0.1-3               graph_1.64.0             cellranger_1.1.0         leiden_0.3.7            
[129] uwot_0.1.10              edgeR_3.28.1             GSEABase_1.48.0          DelayedMatrixStats_1.8.0
[133] curl_4.3                 shiny_1.6.0              outliers_0.14            lifecycle_1.0.0         
[137] nlme_3.1-152             jsonlite_1.7.2           BiocNeighbors_1.4.2      desc_1.3.0              
[141] limma_3.42.2             fansi_0.5.0              pillar_1.6.1             lattice_0.20-41         
[145] fastmap_1.1.0            httr_1.4.2               plotrix_3.8-1            pkgbuild_1.2.0          
[149] survival_3.2-10          glue_1.4.2               remotes_2.3.0            iterators_1.0.13        
[153] shinythemes_1.2.0        png_0.1-7                bit_4.0.4                stringi_1.6.2           
[157] blob_1.2.1               singscore_1.6.0          BiocSingular_1.2.2       memoise_2.0.0           
[161] mathjaxr_1.4-0           irlba_2.3.3              future.apply_1.7.0       ape_5.5
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written by chi.delta • chi.delta

How are you getting the reference data (immgen)?

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written by jared.andrews07 jared.andrews07

Oh yes sorry, I get an error there that I didnt notice..

library(celldex)
immgen<- ImmGenData()

Error: 'R_user_dir' is not an exported object from 'namespace:tools'
• 0 views • link
written by chi.delta • chi.delta

This is a problem with your R environment. Does BiocManager::valid() clear okay?

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written by jared.andrews07 jared.andrews07

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