I am trying to conduct single time course analysis in maSigPro. While the manual says this is straight forward, I'm not convinced. Full disclosure- I am not particularly experienced with R and as a result, am not very good at interpreting its error messages. I have normalized reads in DESEQ2 (though I get the same error with reads normalized in edgeR). I obtained p-values and extracted significant gene expression profiles using the p.vector function in maSigPro. This all works fine. Then when I try to cluster genes based on similar expression profiles using the see.genes function I get an error:

> see.genes(NBp$SELEC, show.fit = TRUE, dis=d$dis, edesign=d$edesign, cluster.method="hclust", groups.vector=d$groups.vector, cluster.data = 1, k=8)
Error in rep(0, (7 - length(a))) : invalid 'times' argument

I am not clear what the "rep(0, (7 - length(a)))" portion of this error refers to and traceback:

> traceback()
2: PlotGroups(data = dat[cut == j, ], show.fit = show.fit, dis = dis, 
       step.method = step.method, min.obs = min.obs, alfa = alfa, 
       nvar.correction = nvar.correction, show.lines = show.lines, 
       time = time, groups = groups, repvect = repvect, summary.mode = summary.mode, 
       xlab = "time", main = paste("Cluster", j, sep = " "), ylim = ylim, 
       cexlab = cexlab, legend = legend, groups.vector = groups.vector, 
       item = item, ...)
1: see.genes(NBp$SELEC, show.fit = TRUE, dis = d$dis, edesign = d$edesign, 
       cluster.method = "hclust", groups.vector = d$groups.vector, 
       cluster.data = 1, k = 8)

Is not especially helpful...

I have looked at the manual and as I said, it says very little about single time course data analysis. For the record, I have tried the very basic approach recommended like so:

exMSP <- maSigPro(normItai, design, vars="each")

and I seem to only get two significant genes?

> exMSP$summary  
                      independ                          rep
1  Itaiw_v1_scaffold_63_g32259  Itaiw_v1_scaffold_63_g32259
2 Itaiw_v1_scaffold_163_g42033 Itaiw_v1_scaffold_163_g42033
                          rep2
1  Itaiw_v1_scaffold_63_g32259
2 Itaiw_v1_scaffold_163_g42033

Likewise I have searched for other people having the same problem and cannot seem to find an answer. Here's what I've done:

## Normalized read counts in DESEQ2:
countData <- as.matrix(read.csv("./gene_count_matrix_21.csv",row.names=1))
colData <- read.csv("./e_design-21-DESEQ.csv",row.names=1)
dds <- DESeqDataSetFromMatrix(countData = countData, colData = colData, design = ~date_time)
dds <- DESeq(dds)
normItai <- counts(dds, normalized = TRUE)
normItai<-normItai[rowSums(normItai)!=0, ]  # remove all zero rows from data

## Identify genes with significant expression profiles:
design<-read.csv("./e_design-21-MSP.csv")
rownames(design) <- design$id
design$id <- NULL
all(rownames(design) == colnames(normItai))  # make sure row names in design object match columns in count data

d <- make.design.matrix(design, time.col=2, repl.col = 1, degree=8) # have to define non-default values for time and replicate

## calculate polynomial regressions for each gene using "negative binomial distribution"
NBp<-p.vector(normItai, d, counts=TRUE)
profiles <- see.genes(NBp$SELEC, show.fit = TRUE, dis=d$dis, edesign=d$edesign, cluster.method="hclust", cluster.data = 1, k=8)

Here is the error message (again) followed by traceback and session Info:

> traceback()
2: PlotGroups(data = dat[cut == j, ], show.fit = show.fit, dis = dis, 
       step.method = step.method, min.obs = min.obs, alfa = alfa, 
       nvar.correction = nvar.correction, show.lines = show.lines, 
       time = time, groups = groups, repvect = repvect, summary.mode = summary.mode, 
       xlab = "time", main = paste("Cluster", j, sep = " "), ylim = ylim, 
       cexlab = cexlab, legend = legend, groups.vector = groups.vector, 
       item = item, ...)
1: see.genes(NBp$SELEC, show.fit = TRUE, dis = d$dis, edesign = d$edesign, 
       cluster.method = "hclust", groups.vector = d$groups.vector, 
       cluster.data = 1, k = 8)
> sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] C

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

other attached packages:
 [1] edgeR_3.24.3                limma_3.38.3               
 [3] Mfuzz_2.42.0                DynDoc_1.60.0              
 [5] widgetTools_1.60.0          e1071_1.7-3                
 [7] maSigPro_1.54.0             ggplot2_3.3.0              
 [9] RColorBrewer_1.1-2          DESeq2_1.22.2              
[11] SummarizedExperiment_1.12.0 DelayedArray_0.8.0         
[13] BiocParallel_1.16.6         matrixStats_0.56.0         
[15] Biobase_2.42.0              GenomicRanges_1.34.0       
[17] GenomeInfoDb_1.18.2         IRanges_2.16.0             
[19] S4Vectors_0.20.1            BiocGenerics_0.28.0        

loaded via a namespace (and not attached):
 [1] bit64_0.9-7            splines_3.5.1          Formula_1.2-3         
 [4] assertthat_0.2.1       latticeExtra_0.6-28    blob_1.2.1            
 [7] GenomeInfoDbData_1.2.0 pillar_1.4.4           RSQLite_2.2.0         
[10] backports_1.1.5        lattice_0.20-40        glue_1.3.2            
[13] digest_0.6.25          XVector_0.22.0         checkmate_2.0.0       
[16] tkWidgets_1.60.0       colorspace_1.4-1       htmltools_0.4.0       
[19] Matrix_1.2-18          XML_3.99-0.3           pkgconfig_2.0.3       
[22] venn_1.9               genefilter_1.64.0      zlibbioc_1.28.0       
[25] purrr_0.3.3            xtable_1.8-4           scales_1.1.1          
[28] htmlTable_1.13.3       tibble_2.1.3           annotate_1.60.1       
[31] admisc_0.8             farver_2.0.3           withr_2.2.0           
[34] nnet_7.3-13            mclust_5.4.5           survival_3.1-11       
[37] magrittr_1.5           crayon_1.3.4           memoise_1.1.0         
[40] MASS_7.3-51.5          class_7.3-15           foreign_0.8-71        
[43] tools_3.5.1            data.table_1.12.8      lifecycle_0.2.0       
[46] stringr_1.4.0          munsell_0.5.0          locfit_1.5-9.1        
[49] cluster_2.1.0          AnnotationDbi_1.44.0   compiler_3.5.1        
[52] rlang_0.4.5            grid_3.5.1             RCurl_1.98-1.1        
[55] rstudioapi_0.11        htmlwidgets_1.5.1      labeling_0.3          
[58] bitops_1.0-6           base64enc_0.1-3        gtable_0.3.0          
[61] DBI_1.1.0              R6_2.4.1               gridExtra_2.3         
[64] knitr_1.28             dplyr_0.8.5            bit_1.1-15.2          
[67] Hmisc_4.3-1            stringi_1.4.6          Rcpp_1.0.4            
[70] vctrs_0.2.4            geneplotter_1.60.0     rpart_4.1-15          
[73] acepack_1.4.1          tidyselect_1.0.0       xfun_0.13  

Had the same problem. Looks like it results from choosing a degree higher than 7 (which seems to be the max allowed for x) in:

make.design.matrix(exp_des, degree = x, time.col = 1, repl.col = 2, group.cols = 3)

when setting up your design.

Hope that solves the issue for you!