Average expression heatmap returns NA

[kvshams]

I am trying to generate heatmap for average expression of genes to a list of genes. The figure returns NA instead of gene list. TopFeatures works perfectly fine

epi_cluster.averages <- AverageExpression(epi_subset, return.seurat = TRUE, add.ident = "group")
Tight_juctionGenes <- Tight_juctionGenes[Tight_juctionGenes %in% rownames(epi_cluster.averages )]
##length(Tight_juctionGenes)
##16
DoHeatmap(epi_cluster.averages, features = Tight_juctionGenes,  size = 3, draw.lines = FALSE)

sessionInfo()

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 19.04

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.8.0
LAPACK: /home/svellari/anaconda2/lib/libmkl_rt.so

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

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

other attached packages:
 [1] viridis_0.5.1               viridisLite_0.3.0          
 [3] KernSmooth_2.23-16          fields_10.3                
 [5] maps_3.3.0                  spam_2.5-1                 
 [7] dotCall64_1.0-0             DoubletFinder_2.0.2        
 [9] future_1.15.1               singlecellmethods_0.1.0    
[11] Seurat_3.1.2.9002           DropletUtils_1.4.3         
[13] SingleCellExperiment_1.6.0  SummarizedExperiment_1.14.1
[15] DelayedArray_0.10.0         BiocParallel_1.18.1        
[17] matrixStats_0.55.0          Biobase_2.44.0             
[19] GenomicRanges_1.36.1        GenomeInfoDb_1.20.0        
[21] IRanges_2.18.3              S4Vectors_0.22.1           
[23] BiocGenerics_0.30.0         ggrastr_0.1.7              
[25] harmony_1.0                 RcppArmadillo_0.9.800.3.0  
[27] Rcpp_1.0.3                  patchwork_1.0.0            
[29] scales_1.1.0                RColorBrewer_1.1-2         
[31] data.table_1.12.8           tidyr_1.0.0                
[33] ggrepel_0.8.1               ggplot2_3.2.1              
[35] dplyr_0.8.3                 Matrix_1.2-18              
[37] magrittr_1.5                ggthemes_4.2.0             
[39] reticulate_1.14            

loaded via a namespace (and not attached):
  [1] uuid_0.1-2             backports_1.1.5        sn_1.5-4              
  [4] plyr_1.8.5             igraph_1.2.4.2         repr_1.0.2            
  [7] lazyeval_0.2.2         splines_3.6.1          listenv_0.8.0         
 [10] TH.data_1.0-10         digest_0.6.23          htmltools_0.4.0       
 [13] gdata_2.18.0           cluster_2.1.0          ROCR_1.0-7            
 [16] limma_3.40.6           globals_0.12.5         RcppParallel_4.4.4    
 [19] R.utils_2.9.2          sandwich_2.5-1         colorspace_1.4-1      
 [22] rappdirs_0.3.1         crayon_1.3.4           RCurl_1.95-4.12       
 [25] jsonlite_1.6           zeallot_0.1.0          survival_3.1-8        
 [28] zoo_1.8-6              ape_5.3                glue_1.3.1            
 [31] gtable_0.3.0           zlibbioc_1.30.0        XVector_0.24.0        
 [34] leiden_0.3.1           Rhdf5lib_1.6.3         future.apply_1.3.0    
 [37] HDF5Array_1.12.3       mvtnorm_1.0-11         edgeR_3.26.8          
 [40] bibtex_0.4.2.2         metap_1.2              plotrix_3.7-7         
 [43] dqrng_0.2.1            rsvd_1.0.2             SDMTools_1.1-221.2    
 [46] tsne_0.1-3             htmlwidgets_1.5.1      httr_1.4.1            
 [49] gplots_3.0.3           TFisher_0.2.0          ica_1.0-2             
 [52] farver_2.0.1           pkgconfig_2.0.3        R.methodsS3_1.7.1     
 [55] uwot_0.1.5             locfit_1.5-9.1         labeling_0.3          
 [58] tidyselect_0.2.5       rlang_0.4.2            reshape2_1.4.3        
 [61] munsell_0.5.0          tools_3.6.1            ggridges_0.5.1        
 [64] evaluate_0.14          stringr_1.4.0          npsurv_0.4-0          
 [67] fitdistrplus_1.0-14    caTools_1.18.0         purrr_0.3.3           
 [70] RANN_2.6.1             pbapply_1.4-2          nlme_3.1-143          
 [73] R.oo_1.23.0            compiler_3.6.1         plotly_4.9.1          
 [76] png_0.1-7              lsei_1.2-0             tibble_2.1.3          
 [79] stringi_1.4.3          RSpectra_0.16-0        lattice_0.20-38       
 [82] IRdisplay_0.7.0        multtest_2.40.0        vctrs_0.2.1           
 [85] mutoss_0.1-12          pillar_1.4.3           lifecycle_0.1.0       
 [88] Rdpack_0.11-1          lmtest_0.9-37          RcppAnnoy_0.0.14      
 [91] cowplot_1.0.0          bitops_1.0-6           irlba_2.3.3           
 [94] gbRd_0.4-11            R6_2.4.1               gridExtra_2.3         
 [97] codetools_0.2-16       MASS_7.3-51.5          gtools_3.8.1          
[100] assertthat_0.2.1       rhdf5_2.28.1           withr_2.1.2           
[103] sctransform_0.2.1      mnormt_1.5-5           multcomp_1.4-11       
[106] GenomeInfoDbData_1.2.1 IRkernel_1.1           Cairo_1.5-10          
[109] Rtsne_0.15             pbdZMQ_0.3-3           numDeriv_2016.8-1.1   
[112] base64enc_0.1-3     

[satijalab]

You need to run ScaleData on the averaged objects before generating the heatmap. Alternately, you can specify that the heatmap should plot from the data slot. That should fix your problem, please feel free to reopen if this does not work.

[kvshams]

Thanks for the reply. I tried with the ScaleData too and get the same issue

DoHeatmap(epi_cluster.averages, features = Tight_juctionGenes,  size = 3, draw.lines = FALSE) ```

[hansong798]

Is your feature list ordered or factor? I solved this problem by removing ordering!