CoveragePlot() adjusting levels

[jeffsteimle]

Hi Tim,

I am using CoveragePlot() to generate tracks from an ATAC object and I am trying to adjust the levels.

For example:

factor.order <- unique([email protected]$factor[order([email protected]$factor),])
[email protected]$factor <- factor([email protected]$factor, levels = factor.order)

When I run CoveragePlot(object = ATAC.object), the resulting plot is identical to the original with no change in order. To make sure I wasn't going crazy, I had also tried this with my RNA object and ExpressionPlot(), but that behaved as expected. I also checked to make sure the levels were set as I expected, and it appeared to be correct.

The only thing I can think that is creating a conflict is that I select a subset of idents to plot. For example:

CoveragePlot(object = ATAC.object, region = "chr1-10000-20000", annotation = FALSE, peaks = FALSE, idents = c(factor.order[1], factor.order[2]))

However, when I allow all idents to be plotted, the order is still the original.

Any help would be greatly appreciated!

sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Mojave 10.14.6

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] dplyr_1.0.5                        stringr_1.4.0                     
 [3] tidyr_1.1.3                        ChIPpeakAnno_3.24.2               
 [5] BSgenome.Mmusculus.UCSC.mm10_1.4.0 BSgenome_1.58.0                   
 [7] rtracklayer_1.50.0                 Biostrings_2.58.0                 
 [9] XVector_0.30.0                     TFBSTools_1.28.0                  
[11] JASPAR2020_0.99.10                 chromVAR_1.12.0                   
[13] GenomicRanges_1.42.0               GenomeInfoDb_1.26.4               
[15] IRanges_2.24.1                     S4Vectors_0.28.1                  
[17] BiocGenerics_0.36.0                patchwork_1.1.1                   
[19] cowplot_1.1.1                      SeuratObject_4.0.0                
[21] Seurat_4.0.1                       Signac_1.1.1                      
[23] ggplot2_3.3.3                     

loaded via a namespace (and not attached):
  [1] rappdirs_0.3.3              SnowballC_0.7.0            
  [3] scattermore_0.7             GGally_2.1.1               
  [5] R.methodsS3_1.8.1           bit64_4.0.5                
  [7] knitr_1.31                  irlba_2.3.3                
  [9] DelayedArray_0.16.3         R.utils_2.10.1             
 [11] data.table_1.14.0           rpart_4.1-15               
 [13] KEGGREST_1.30.1             RCurl_1.98-1.3             
 [15] AnnotationFilter_1.14.0     generics_0.1.0             
 [17] GenomicFeatures_1.42.2      lambda.r_1.2.4             
 [19] RSQLite_2.2.5               RANN_2.6.1                 
 [21] future_1.21.0               bit_4.0.4                  
 [23] spatstat.data_2.1-0         xml2_1.3.2                 
 [25] httpuv_1.5.5                SummarizedExperiment_1.20.0
 [27] assertthat_0.2.1            DirichletMultinomial_1.32.0
 [29] xfun_0.22                   hms_1.0.0                  
 [31] promises_1.2.0.1            fansi_0.4.2                
 [33] progress_1.2.2              caTools_1.18.2             
 [35] dbplyr_2.1.0                igraph_1.2.6               
 [37] DBI_1.1.1                   htmlwidgets_1.5.3          
 [39] futile.logger_1.4.3         reshape_0.8.8              
 [41] spatstat.geom_2.0-1         purrr_0.3.4                
 [43] ellipsis_0.3.1              backports_1.2.1            
 [45] annotate_1.68.0             biomaRt_2.46.3             
 [47] deldir_0.2-10               MatrixGenerics_1.2.1       
 [49] vctrs_0.3.7                 Biobase_2.50.0             
 [51] ensembldb_2.14.0            ROCR_1.0-11                
 [53] abind_1.4-5                 cachem_1.0.4               
 [55] withr_2.4.1                 ggforce_0.3.3              
 [57] checkmate_2.0.0             sctransform_0.3.2          
 [59] GenomicAlignments_1.26.0    prettyunits_1.1.1          
 [61] goftest_1.2-2               cluster_2.1.1              
 [63] lazyeval_0.2.2              seqLogo_1.56.0             
 [65] crayon_1.4.1                labeling_0.4.2             
 [67] pkgconfig_2.0.3             tweenr_1.0.2               
 [69] nlme_3.1-152                ProtGenerics_1.22.0        
 [71] nnet_7.3-15                 rlang_0.4.10               
 [73] globals_0.14.0              lifecycle_1.0.0            
 [75] miniUI_0.1.1.1              BiocFileCache_1.14.0       
 [77] dichromat_2.0-0             VennDiagram_1.6.20         
 [79] polyclip_1.10-0             matrixStats_0.58.0         
 [81] lmtest_0.9-38               graph_1.68.0               
 [83] Matrix_1.3-2                ggseqlogo_0.1              
 [85] zoo_1.8-9                   base64enc_0.1-3            
 [87] ggridges_0.5.3              png_0.1-7                  
 [89] viridisLite_0.3.0           bitops_1.0-6               
 [91] R.oo_1.24.0                 KernSmooth_2.23-18         
 [93] blob_1.2.1                  regioneR_1.22.0            
 [95] parallelly_1.24.0           readr_1.4.0                
 [97] jpeg_0.1-8.1                CNEr_1.26.0                
 [99] scales_1.1.1                memoise_2.0.0              
[101] magrittr_2.0.1              plyr_1.8.6                 
[103] ica_1.0-2                   zlibbioc_1.36.0            
[105] compiler_4.0.3              RColorBrewer_1.1-2         
[107] fitdistrplus_1.1-3          Rsamtools_2.6.0            
[109] listenv_0.8.0               pbapply_1.4-3              
[111] formatR_1.8                 htmlTable_2.1.0            
[113] Formula_1.2-4               MASS_7.3-53.1              
[115] mgcv_1.8-34                 tidyselect_1.1.0           
[117] stringi_1.5.3               askpass_1.1                
[119] latticeExtra_0.6-29         ggrepel_0.9.1              
[121] grid_4.0.3                  VariantAnnotation_1.36.0   
[123] fastmatch_1.1-0             tools_4.0.3                
[125] future.apply_1.7.0          rstudioapi_0.13            
[127] TFMPvalue_0.0.8             foreign_0.8-81             
[129] lsa_0.73.2                  gridExtra_2.3              
[131] farver_2.1.0                Rtsne_0.15                 
[133] digest_0.6.27               BiocManager_1.30.12        
[135] shiny_1.6.0                 pracma_2.3.3               
[137] Rcpp_1.0.6                  later_1.1.0.1              
[139] RcppAnnoy_0.0.18            OrganismDbi_1.32.0         
[141] httr_1.4.2                  AnnotationDbi_1.52.0       
[143] ggbio_1.38.0                biovizBase_1.38.0          
[145] colorspace_2.0-0            XML_3.99-0.6               
[147] tensor_1.5                  reticulate_1.18            
[149] splines_4.0.3               uwot_0.1.10                
[151] RBGL_1.66.0                 RcppRoll_0.3.0             
[153] spatstat.utils_2.1-0        multtest_2.46.0            
[155] plotly_4.9.3                xtable_1.8-4               
[157] futile.options_1.0.1        jsonlite_1.7.2             
[159] poweRlaw_0.70.6             R6_2.5.0                   
[161] Hmisc_4.5-0                 pillar_1.5.1               
[163] htmltools_0.5.1.1           mime_0.10                  
[165] glue_1.4.2                  fastmap_1.1.0              
[167] DT_0.17                     BiocParallel_1.24.1        
[169] codetools_0.2-18            utf8_1.2.1                 
[171] lattice_0.20-41             spatstat.sparse_2.0-0      
[173] tibble_3.1.0                curl_4.3                   
[175] leiden_0.3.7                gtools_3.8.2               
[177] GO.db_3.12.1                openssl_1.4.3              
[179] survival_3.2-10             munsell_0.5.0              
[181] GenomeInfoDbData_1.2.4      reshape2_1.4.4             
[183] gtable_0.3.0                spatstat.core_2.0-0 
```

[jeffsteimle]

I saw now that #269 asked about this previously, but did not explain how it was addressed.

[timoast]

To adjust the levels for the cell identities, you can use the levels<- assignment function, for example:

levels(tcell)

[1] "CD4 Naive" "CD8 Naive" "CD8 TEM"

CoveragePlot(tcell, region = "CD8A")

levels(tcell) <- c("CD8 Naive", "CD8 TEM", "CD4 Naive")
CoveragePlot(tcell, region = "CD8A")

[jeffsteimle]

Hi Tim,

Thank you for showing me that trick, somehow I hadn't come across until now. That is much easier than adjusting the levels within factor(). Not sure why my way didn't work, but I'm glad this way does work.

Thank you again,
Jeff