Hi everyone,

I am using ImpulseDE2 to analyse an RNA-seq time course experiment. I have six time points with three replicates in each time point and no control, so this is a simple case-only differential expression analysis.

I am having problems plotting the read counts together with the model using the function plotGenes, shown here:

 plotGenes(vecGeneIDs = NULL, scaNTopIDs = 1, 
      objectImpulseDE2 = objectImpulseDE2_transient,
      boolCaseCtrl = F, dirOut = NULL, strFileName = NULL, boolSimplePlot = TRUE,
      vecRefPval = NULL, strNameRefMethod = NULL)

When I run the plotGenes function, I receive this warning message from ggplot2 : Removed 18 rows containing missing values (geom_point). I can create the desired plot, but only the model fit is shown, the read counts from the 18 samples are not shown.

I had a look at the plotGenes functions, and it calls many other functions such as evalImpulse_comp, which are also part of the ImpulseDE2 package, but these functions were not installed when I installed the package, so I am wondering if that is the problem. Any help on getting the plotGenes function to work or any other solutions for making these plots would be great.

Thanks in advance, Vanessa

-- Session info:

R version 3.5.3 (2019-03-11) Platform: x86_64-apple-darwin15.6.0 (64-bit) Running under: OS X El Capitan 10.11.6

Matrix products: default BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale: [1] C

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

other attached packages: [1] forcats0.4.0 stringr1.4.0 dplyr0.8.0.1 purrr0.3.2 readr1.3.1
[6] tidyr0.8.3 tibble2.1.1 ggplot23.1.0 tidyverse1.2.1 ImpulseDE21.6.1
[11] Matrix1.2-16 RcppArmadillo0.9.200.7.1

loaded via a namespace (and not attached): [1] nlme3.1-137 bitops1.0-6 matrixStats0.54.0 lubridate1.7.4
[5] bit640.9-7 RColorBrewer1.1-2 httr1.4.0 GenomeInfoDb1.18.2
[9] tools3.5.3 backports1.1.3 R62.4.0 rpart4.1-13
[13] Hmisc4.2-0 DBI1.0.0 lazyeval0.2.2 BiocGenerics0.28.0
[17] colorspace1.4-1 nnet7.3-12 GetoptLong0.1.7 withr2.1.2
[21] tidyselect0.2.5 gridExtra2.3 DESeq21.22.2 bit1.1-14
[25] compiler3.5.3 cli1.1.0 rvest0.3.2 Biobase2.42.0
[29] htmlTable1.13.1 xml21.2.0 DelayedArray0.8.0 labeling0.3
[33] scales1.0.0 checkmate1.9.1 genefilter1.64.0 digest0.6.18
[37] foreign0.8-71 XVector0.22.0 base64enc0.1-3 pkgconfig2.0.2
[41] htmltools0.3.6 readxl1.3.1 htmlwidgets1.3 rlang0.3.2
[45] GlobalOptions0.1.0 rstudioapi0.10 RSQLite2.1.1 shape1.4.4
[49] generics0.0.2 jsonlite1.6 BiocParallel1.16.6 acepack1.4.1
[53] RCurl1.95-4.12 magrittr1.5 GenomeInfoDbData1.2.0 Formula1.2-3
[57] Rcpp1.0.1 munsell0.5.0 S4Vectors0.20.1 stringi1.4.3
[61] yaml2.2.0 SummarizedExperiment1.12.0 zlibbioc1.28.0 plyr1.8.4
[65] grid3.5.3 blob1.1.1 parallel3.5.3 crayon1.3.4
[69] lattice0.20-38 haven2.1.0 cowplot0.9.4 splines3.5.3
[73] annotate1.60.1 circlize0.4.5 hms0.4.2 locfit1.5-9.1
[77] knitr1.22 ComplexHeatmap1.20.0 pillar1.3.1 GenomicRanges1.34.0
[81] rjson0.2.20 geneplotter1.60.0 stats43.5.3 XML3.98-1.19
[85] glue1.3.1 latticeExtra0.6-28 modelr0.1.4 data.table1.12.0
[89] BiocManager1.30.4 cellranger1.1.0 gtable0.3.0 assertthat0.2.1
[93] xfun0.5 xtable1.8-3 broom0.5.1 survival2.43-3
[97] AnnotationDbi1.44.0 memoise1.1.0 IRanges2.16.0 cluster2.0.7-1 enter code here