"plotGenes" plots fits but not data in "case" mode

[mchimenti]

Hi,
I'm trying to plot the fits and data to my DE time-dependent genes. plotGenes() generates plots but does not plot the underlying count data when I call it like this:

imp_res <- runImpulseDE2(matCountData = counts.ncd.mat, dfAnnotation = metadata, scaNProc = 4)
timeplots <- plotGenes(scaNTopIDs = 10, objectImpulseDE2 = imp_res, boolCaseCtrl = FALSE, boolSimplePlot = TRUE)
timeplots[[1]]

I suspect there is a problem somewhere in this code block (lines 258 to 280 in https://github.com/YosefLab/ImpulseDE2/blob/master/R/srcImpulseDE2_plotGenes.R):

dfRaw <- data.frame(
                normCounts = get_matCountDataProc(
                    obj=objectImpulseDE2)[id, vecSamples]/
                    get_vecSizeFactors(obj=objectImpulseDE2)[vecSamples], 
                time = dfAnnot[vecSamples, ]$Time, 
                Batch = vecBatches, 
                condition = dfAnnot[vecSamples, ]$Condition)
            
            if (boolSimplePlot) {
                dfFit <- data.frame(
                    time = vecTimePointsFit, value = vecCaseImpulseValue, 
                    Batch = rep(vecBatchLabelsCase[1], length(vecTimePointsFit)))
                if (!is.null(vecBatchLabelsCase)) {
                    gplotGene <- ggplot() + 
                        geom_point(data = dfRaw, aes(x = time,  y = normCounts, 
                                                     shape = Batch)) + 
                        geom_line(data = dfFit, 
                                  aes(x = time, y = value, linetype = Batch))
                } else {
                    gplotGene <- ggplot() + 
                        geom_point(data = dfRaw, aes(x = time, y = normCounts)) + 
                        geom_line(data = dfFit, aes(x = time, 
                                                    y = value))
                }

The plotting gives a warning:
"Removed 40 rows containing missing values (geom_point)"

^^^ I don't know specifically what this is referring to but I suspect this is why data is not plotting.

Any suggestions would be helpful. Thanks!

> sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.6

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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] grid      parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ImpulseDE2_1.8.0    Vennerable_3.0      xtable_1.8-4        gtools_3.8.1        reshape_0.8.8       RColorBrewer_1.1-2 
 [7] lattice_0.20-38     RBGL_1.60.0         graph_1.62.0        BiocGenerics_0.30.0 forcats_0.4.0       stringr_1.4.0      
[13] dplyr_1.0.2         purrr_0.3.2         readr_1.3.1         tidyr_1.0.0         tibble_3.0.4        ggplot2_3.3.2      
[19] tidyverse_1.2.1     timeSeq_1.0.4       edgeR_3.26.8        limma_3.40.6        

[mchimenti]

UPDATE: I solved the problem myself with this:

timeplots <- plotGenes(scaNTopIDs = 10, objectImpulseDE2 = imp_res, boolCaseCtrl = FALSE, boolSimplePlot = TRUE)
p <- timeplots[[1]]
dat <- counts.ncd.mat["ENSMUSG00000087968",] %>% t() %>% as_tibble()  %>% gather(.,key="sample",value="counts") %>% mutate(time=as.numeric(condition_ncd))
p <- p + geom_point(data = dat, aes(x=time, y=counts))
p

Still, it would be nice if you could fix the original code so I don't have to manually do this for each gene. Thanks!