I am analysing data from a chemical screen (~3000 compounds spread over plates of 96 wells) which was performed in different cell lines, and within a cell line I am studying 3 conditions, WT, Mut1, Mut2. But no replicates are available for most of the plates. Each time a plate is performed is done across all cell lines and conditions.
So I came across cell2HTS. But I am encountering some problems.
1- It starts with uploading the data, my Platelist file is
Filename Plate Replicate
SC1_D1_WT.txt 1 1
SC1_D1_1.txt 1 1
SC1_D1_7.txt 1 1
SC2_D1_WT.txt 2 1
SC2_D1_1.txt 2 1
SC2_D1_7.txt 2 1
But I am getting an error, like it it doesn’t seem to read the first column.
x <- readPlateList("Platelist.txt",name=experimentName, path="/home/andreia/daIL7: found data in 8 x 12 (96 well) format.
Error in readPlateList("Platelist.txt", name = experimentName, path = "/home/andreia/data_HTS") :
The following rows are duplicated in the plateList table:
Plate Replicate Channel
2 1 1
3 1 1
5 2 1
6 2 1
so from this message it seems that the structure of the platelist is different than the explained in the manual?
2- Regarding normalization, as we have positive and negative controls located in the edges of the plate and in the middle of the plate, I was thinking on using NPI method. And then scoring, using score Replicates. However, my estimates using excel are different then the ones using cellHTS2, tried using the mean and stdev of the normalised plate(sample only) or I even tried to use the non-normalised and results are different. I estimated the score, considering the mean and the stdev including the well classified as other , and still the values are very different, can someone explain this. How are the mean and standard deviation estimated in the z score in scoreReplicates?
3- Figure 5 of the paper, how do I access the data of controls so that I can plot the distribution of the data?
Thanks in advance for your attention and your help.
R version 3.3.1 (2016-06-21)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: CentOS release 6.8 (Final)
 LC_CTYPE=C LC_NUMERIC=C
 LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
 LC_PAPER=en_US.UTF-8 LC_NAME=C
 LC_ADDRESS=C LC_TELEPHONE=C
 LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
 grid parallel stats graphics grDevices utils datasets
 methods base
other attached packages:
 cellHTS2_2.38.0 locfit_1.5-9.1 hwriter_1.3.2
 vsn_3.42.3 splots_1.40.0 genefilter_1.56.0
 Biobase_2.34.0 BiocGenerics_0.20.0 RColorBrewer_1.1-2
loaded via a namespace (and not attached):
 pcaPP_1.9-61 Rcpp_0.12.7 prada_1.50.0
 DEoptimR_1.0-6 BiocInstaller_1.24.0 plyr_1.8.4
 bitops_1.0-6 tools_3.3.1 zlibbioc_1.20.0
 annotate_1.52.0 RSQLite_1.0.0 tibble_1.2
 preprocessCore_1.36.0 gtable_0.2.0 lattice_0.20-34
 graph_1.52.0 Matrix_1.2-7.1 Category_2.40.0
 DBI_0.5-1 mvtnorm_1.0-5 cluster_2.0.5
 S4Vectors_0.12.0 IRanges_2.8.1 stats4_3.3.1
 GSEABase_1.36.0 robustbase_0.92-6 rrcov_1.4-3
 AnnotationDbi_1.36.0 RBGL_1.50.0 XML_3.98-1.5
 survival_2.40-1 limma_3.30.3 ggplot2_2.2.0
 MASS_7.3-45 scales_0.4.1 splines_3.3.1
 assertthat_0.1 xtable_1.8-2 colorspace_1.3-0
 affy_1.52.0 RCurl_1.95-4.8 lazyeval_0.2.0
 munsell_0.4.3 affyio_1.44.0
With kind regards,