Hello,

I used DEP on SILAC protein data.

Thank you in advance for your great help!

Best,

Yue

> SummarizedExperiment(assays=list(counts=counts_LH),
+                      rowRanges=rowRanges_LH, colData=colData_LH)
class: RangedSummarizedExperiment 
dim: 6674 12 
metadata(0):
assays(1): counts
rownames(6674): 4501853 4501857 ... 386643034 386643035
rowData names(1): feature_id
colnames(12): A B ... K L
colData names(1): Treatment

> plot_frequency(SummarizedExperiment)
Error: se does not inherit from class SummarizedExperiment



> 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=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] shinydashboard_0.7.1        tibble_3.0.4                shiny_1.5.0                 DEP_1.8.0                  
 [5] dplyr_1.0.2                 SummarizedExperiment_1.16.1 DelayedArray_0.12.3         BiocParallel_1.20.1        
 [9] matrixStats_0.57.0          Biobase_2.46.0              GenomicRanges_1.38.0        GenomeInfoDb_1.22.1        
[13] IRanges_2.20.2              S4Vectors_0.24.4            BiocGenerics_0.32.0         ggplot2_3.3.2              
[17] proteus_0.2.14              proteusSILAC_0.1.0          limma_3.42.2               

loaded via a namespace (and not attached):
  [1] backports_1.2.0        circlize_0.4.11        Hmisc_4.4-1            plyr_1.8.6            
  [5] gmm_1.6-5              splines_3.6.1          crosstalk_1.1.0.1      digest_0.6.27         
  [9] foreach_1.5.1          htmltools_0.5.0        viridis_0.5.1          fansi_0.4.1           
 [13] magrittr_1.5           checkmate_2.0.0        memoise_1.1.0          cluster_2.1.0         
 [17] doParallel_1.0.16      readr_1.4.0            ComplexHeatmap_2.5.4   annotate_1.64.0       
 [21] imputeLCMD_2.0         sandwich_3.0-0         jpeg_0.1-8.1           colorspace_1.4-1      
 [25] blob_1.2.1             xfun_0.19              jsonlite_1.7.1         crayon_1.3.4          
 [29] RCurl_1.98-1.2         genefilter_1.68.0      impute_1.60.0          survival_3.2-7        
 [33] zoo_1.8-8              iterators_1.0.13       glue_1.4.2             gtable_0.3.0          
 [37] zlibbioc_1.32.0        XVector_0.26.0         GetoptLong_1.0.4       shape_1.4.5           
 [41] scales_1.1.1           vsn_3.54.0             mvtnorm_1.1-1          DBI_1.1.0             
 [45] Rcpp_1.0.5             mzR_2.20.0             viridisLite_0.3.0      xtable_1.8-4          
 [49] htmlTable_2.1.0        clue_0.3-57            foreign_0.8-76         bit_4.0.4             
 [53] preprocessCore_1.48.0  Formula_1.2-4          DT_0.16                htmlwidgets_1.5.2     
 [57] RColorBrewer_1.1-2     ellipsis_0.3.1         pkgconfig_2.0.3        XML_3.99-0.3          
 [61] farver_2.0.3           nnet_7.3-14            utf8_1.1.4             locfit_1.5-9.4        
 [65] tidyselect_1.1.0       labeling_0.4.2         rlang_0.4.8            reshape2_1.4.4        
 [69] later_1.1.0.1          AnnotationDbi_1.48.0   munsell_0.5.0          tools_3.6.1           
 [73] cli_2.1.0              generics_0.1.0         RSQLite_2.2.1          evaluate_0.14         
 [77] stringr_1.4.0          fastmap_1.0.1          mzID_1.24.0            yaml_2.2.1            
 [81] knitr_1.30             bit64_4.0.5            purrr_0.3.4            ncdf4_1.17            
 [85] mime_0.9               compiler_3.6.1         rstudioapi_0.11        png_0.1-7             
 [89] affyio_1.56.0          geneplotter_1.64.0     stringi_1.5.3          MSnbase_2.12.0        
 [93] lattice_0.20-41        ProtGenerics_1.18.0    Matrix_1.2-17          tmvtnorm_1.4-10       
 [97] vctrs_0.3.4            pillar_1.4.6           norm_1.0-9.5           lifecycle_0.2.0       
[101] BiocManager_1.30.10    MALDIquant_1.19.3      GlobalOptions_0.1.2    data.table_1.13.2     
[105] bitops_1.0-6           httpuv_1.5.4           R6_2.5.0               latticeExtra_0.6-29   
[109] pcaMethods_1.78.0      affy_1.64.0            promises_1.1.1         gridExtra_2.3         
[113] codetools_0.2-18       MASS_7.3-53            assertthat_0.2.1       DESeq2_1.26.0         
[117] rjson_0.2.20           withr_2.3.0            GenomeInfoDbData_1.2.2 hms_0.5.3             
[121] grid_3.6.1             rpart_4.1-15           tidyr_1.1.2            rmarkdown_2.5         
[125] Cairo_1.5-12.2         base64enc_0.1-3

Unless you 'capture' the output from a function with a variable name, it isn't persistent. In other words, if I do

rnorm(10)

That will generate 10 random normal variates, print them to the screen, and then they vanish into thin air. However, if I do

z <- rnorm(10)

Nothing gets printed to the screen, but instead an object called 'z' is generated and contains the 10 random variates. What you have done is essentially the same as what I did in the first example; you generated a SummarizedExperiment, which got printed to the screen and then disappeared. You then tried to run a function called plot_frequency on another function called SummarizedExperiment, which as you should expect failed, since the function SummarizedExperiment is not in fact a SummarizedExperiment itself.