Hello,

I am unable to create a 'qPCRset' object from my own data:

cp_matrix[1:5, 1:5]
           VM12B_1_8R VM12B_2_9L VM12B_3_33R VM12B_4_50L VM12B_2_20L
Nppa            36.74      25.33       26.80       26.02       25.05
Myh7            25.37      22.17       25.04       22.33       25.01
Atp2a2          40.00      40.00       40.00       39.16       40.00
Tgm2            32.33      30.02       30.84       29.63       31.28
A9LNM8_RAT      40.00      36.91       40.00       37.95       40.00

dim(cp_matrix)
[1]  32 110

head(pheno_data)
                Animal    Group Side      Sample Experiment Izolation concentration                RIN Plate Column
VM12B_1_8R   VM12B_1_8  Sh_Plac    R  VM12B_1_8R          B        SC        629.11                  8     A      A
VM12B_2_9L   VM12B_2_9 AVF_Plac    L  VM12B_2_9L          B        SA       1176.65 7.5999999999999996     A      B
VM12B_3_33R VM12B_3_33 AVF_Sild    R VM12B_3_33R          B        SC        690.13 8.4000000000000004     A      C
VM12B_4_50L VM12B_4_50 AVF_ACEi    L VM12B_4_50L          B        SB       1004.05 7.2999999999999998     A      E
VM12B_2_20L VM12B_2_20 AVF_Plac    L VM12B_2_20L          B        SD        510.90 8.0999999999999996     A      F
VM12_1_53L   VM12_1_53  Sh_Plac    L  VM12_1_53L          A        SF        623.33 8.1999999999999993     A      G

all.equal(colnames(cp_matrix), rownames(pheno_data))
[1] TRUE

head(feature_data)
           featureName featureType
Nppa              Nppa      Target
Myh7              Myh7      Target
Atp2a2          Atp2a2      Target
Tgm2              Tgm2      Target
A9LNM8_RAT  A9LNM8_RAT      Target
Npr1              Npr1      Target

all.equal(rownames(cp_matrix), rownames(feature_data))
[1] TRUE

feature_category[1:5, 1:5]
           VM12B_1_8R VM12B_2_9L VM12B_3_33R VM12B_4_50L VM12B_2_20L
Nppa               OK         OK          OK          OK          OK
Myh7               OK         OK          OK          OK          OK
Atp2a2             OK         OK          OK          OK          OK
Tgm2               OK         OK          OK          OK          OK
A9LNM8_RAT         OK         OK          OK          OK          OK

all.equal(rownames(cp_matrix), rownames(feature_category))
[1] TRUE
all.equal(colnames(cp_matrix), colnames(feature_category))
[1] TRUE

qpcr_set <- new(
  "qPCRset",
  exprs = cp_matrix,
  featureCategory = feature_category,
  phenoData = as(pheno_data, "AnnotatedDataFrame"),
  featureData = as(feature_data, "AnnotatedDataFrame")
)

sampleNames(qpcr_set) <- colnames(cp_matrix)
featureNames(qpcr_set) <- rownames(cp_matrix)

qpcr_set
An object of class "qPCRset"
Error in dimnames(x) <- dn :
  length of 'dimnames' [1] not equal to array extent

I found some answer: HT qPCR; Creating qPCRset from expression matrix, but even the example isn't working:

mat <- matrix(rnorm(9*96), ncol = 6, nrow = 96, byrow = FALSE)
raw <- new("qPCRset", exprs = mat, featureCategory = as.data.frame(array("OK", dim=dim(mat))))
sampleNames(raw) <- paste("S", 1:6, sep = "")
Error in (function (od, vd)  :
  object and replacement value dimnames differ
featureNames(raw) <- paste("A", 1:96, sep = "")

raw
An object of class "qPCRset"
Size:  96 features, 6 samples
Feature types:		  
Feature names:		 A1 A2 A3 ...
Feature classes:		  
Feature categories:	 OK 
Sample names:		 1 2 3 ...
# Sample names not set...

Thanks in advance for help!

sessionInfo()
R version 3.4.2 (2017-09-28)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Linux Mint 18.2

Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=cs_CZ.UTF-8   
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=cs_CZ.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=cs_CZ.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] bindrcpp_0.2.2      nondetects_2.8.0    HTqPCR_1.32.0       limma_3.34.9        RColorBrewer_1.1-2  Biobase_2.38.0      BiocGenerics_0.24.0
 [8] heatmaply_0.15.2    viridis_0.5.1       viridisLite_0.3.0   plotly_4.8.0        ggthemes_4.0.0      gridExtra_2.3       ggpubr_0.1.7       
[15] magrittr_1.5        reshape2_1.4.3      readxl_1.1.0        forcats_0.3.0       stringr_1.3.1       dplyr_0.7.6         purrr_0.2.5        
[22] readr_1.1.1         tidyr_0.8.1         tibble_1.4.2        ggplot2_3.0.0       tidyverse_1.2.1    

loaded via a namespace (and not attached):
 [1] nlme_3.1-137          bitops_1.0-6          lubridate_1.7.4       webshot_0.5.0         httr_1.3.1            prabclus_2.2-6        tools_3.4.2          
 [8] backports_1.1.2       affyio_1.48.0         R6_2.2.2              KernSmooth_2.23-15    lazyeval_0.2.1        colorspace_1.3-2      trimcluster_0.1-2.1  
[15] nnet_7.3-12           withr_2.1.2           tidyselect_0.2.4      preprocessCore_1.40.0 compiler_3.4.2        cli_1.0.0             rvest_0.3.2          
[22] TSP_1.1-6             xml2_1.2.0            diptest_0.75-7        caTools_1.17.1.1      scales_0.5.0          DEoptimR_1.0-8        mvtnorm_1.0-8        
[29] robustbase_0.93-1.1   affy_1.56.0           digest_0.6.15         pkgconfig_2.0.1       htmltools_0.3.6       htmlwidgets_1.2       rlang_0.2.1          
[36] rstudioapi_0.7        BiocInstaller_1.28.0  bindr_0.1.1           jsonlite_1.5          mclust_5.4.1          gtools_3.8.1          dendextend_1.8.0     
[43] modeltools_0.2-22     Rcpp_0.12.18          munsell_0.5.0         stringi_1.2.4         whisker_0.3-2         zlibbioc_1.24.0       MASS_7.3-50          
[50] flexmix_2.3-14        gplots_3.0.1          plyr_1.8.4            grid_3.4.2            gdata_2.18.0          crayon_1.3.4          lattice_0.20-35      
[57] haven_1.1.2           hms_0.4.2             knitr_1.20            pillar_1.3.0          fpc_2.1-11.1          codetools_0.2-15      stats4_3.4.2         
[64] glue_1.3.0            gclus_1.3.1           data.table_1.11.4     modelr_0.1.2          foreach_1.4.4         cellranger_1.1.0      gtable_0.2.0         
[71] kernlab_0.9-26        assertthat_0.2.0      broom_0.5.0           class_7.3-14          seriation_1.2-3       iterators_1.0.10      registry_0.5         
[78] cluster_2.0.7-1