Hello Deepak, On 1 Jun 2010, at 16:21, deepak roshan wrote: > Dear Sir, > > > Thank you for helps.Again iam facing some problems in raedCtData > step. i changed exFiles$File[c(1:30) it reads 30 sample but when i > change to exFiles$File[c(1:99) then it is showing error. could you > tell me how to read 99 sample or what is the maximum number of sample > we can analysis. As I've mentioned before, there's no inherent limit to how many files can be read in. In your example below you get an error somewhere between file number 30 and 99, indicating that there's some input that readCtData() doesn't expect. It could either be a bug in readCtData, or some issue with one of your files. To narrow down a bit where the problem is, why don't you try: for (i in 1:99) { print(i) test <- readCtData(files=exFiles$File[i], path=exPath, n.features = 48, flag = 4, feature = 6, type = 7, position = 3, Ct = 8, header = FALSE, SDS = FALSE, n.data = 1, na.value = 40) } Also, in general using traceback() after an error can help you (/us) narrow down where the error occurred, in case it's in a sub-function. > Second question is how to give feature class like in example it is > given featureClass(raw) <- factor(c("Marker", "TF", "Kinase") in my > case it is cyclins and cyclin inhibitors where to change that. > You can change the featureClass information anywhere along the way, such as right after you read in the data. If you have 48 features for each plate, and for example the first 10 are cyclins and the last 38 cyclin inhibitors, you can say: featureClass(raw) <- factor(rep(c("Cyclin", "Cyclin_Inhibitor"), times=c(10,38))) HTH \Heidi > Details of the session is given below > > Deepak > > > > data(qPCRraw) > > data(qPCRpros) > > class(qPCRraw) > > [1] "qPCRset" > attr(,"package") > [1] ".GlobalEnv" > > > path <- "F:/HTqPCR/TLDA_modi" > > exPath <- "F:/HTqPCR/TLDA_modi" > > exFiles <- read.delim(file.path(exPath, "files.txt")) > > raw <- readCtData(files=exFiles$File[c(1:30)], path=exPath, > n.features = 48, flag = 4, feature = 6, type = 7, position = 3, Ct > = 8, header = FALSE, SDS = FALSE, n.data = 1, na.value = 40) > > raw > > An object of class "qPCRset" > Size: 48 features, 30 samples > Feature types: Endogenous Control, Target > Feature names: CCNF-Hs00171049_m1 RBL2-Hs00180562_m1 CCND1- > Hs00277039_m1 ... > Feature classes: > Feature categories: OK, Undetermined > Sample names: Sample4 Sample67 Sample80 ... > > raw <- readCtData(files=exFiles$File[c(1:99)], path=exPath, > n.features = 48, flag = 4, feature = 6, type = 7, position = 3, Ct > = 8, header = FALSE, SDS = FALSE, n.data = 1, na.value = 40) > > Error in out@exprs[, cols] <- apply(data, 2, function(x) as.numeric > (as.character(x))) : > number of items to replace is not a multiple of replacement length > > sessionInfo() > R version 2.11.0 (2010-04-22) > x86_64-pc-mingw32 > > locale: > [1] LC_COLLATE=English_India.1252 LC_CTYPE=English_India.1252 > LC_MONETARY=English_India.1252 LC_NUMERIC=C > LC_TIME=English_India.1252 > > attached base packages: > [1] stats graphics grDevices utils datasets methods base > > other attached packages: > [1] HTqPCR_1.2.0 limma_3.4.1 RColorBrewer_1.0-2 > Biobase_2.8.0 > > loaded via a namespace (and not attached): > [1] affy_1.26.1 affyio_1.16.0 > gdata_2.8.0 gplots_2.7.4 gtools_2.6.2 > preprocessCore_1.10.0 tools_2.11.0 > > > > > On Mon, May 31, 2010 at 6:46 PM, Heidi Dvinge <heidi@ebi.ac.uk> wrote: >> >> Dear sir, >> >> thank you for your all help. iam facing some problems in >> raedCtData step. i have 99 sample but it is deading only 6 >> samples.how the sample numbers can be incereased to 99. details of >> the session is given below >> >> deepak >> >> > n.samples <- 99 >> > readCtData(files = files$File, path = path, n.features = 48, >> flag = 4, feature = 6, type = 7, position = 3, Ct = 8, header = >> FALSE, SDS = FALSE, n.data = 1, samples, na.value = 40) >> An object of class "qPCRset" >> Size: 48 features, 6 samples >> Feature types: Endogenous Control, Target >> Feature names: CCNF-Hs00171049_m1 RBL2-Hs00180562_m1 >> CCND1-Hs00277039_m1 ... >> Feature classes: >> Feature categories: OK >> Sample names: Sample1 Sample2 Sample3 ... >> Warning message: >> In readCtData(files = files$File, path = path, n.features = 48, : >> Not enough sample names provided; using Sample1, Sample2, ... >> instead >> > > Hello Deepak, > > there is nothing in readCtData specifying that only 6 samples can > be read. Defining a variable n.samples isn't going to change that, > since you don't actually use this variable anywhere. What matters > here is if your length(files$File) is 99. What does just: > > > files$File > > say? > > In the example data there are only 6 files, but if I read some of > them multiple times, I can say: > > > exPath <- system.file("exData", package="HTqPCR") > > exFiles <- read.delim(file.path(exPath, "files.txt")) > > raw <- readCtData(files=exFiles$File[c(1:6,2:5,1:6)], path=exPath) > > raw > An object of class "qPCRset" > Size: 384 features, 16 samples > Feature types: Endogenous Control, Target > Feature names: Gene1 Gene2 Gene3 ... > Feature classes: > Feature categories: OK, Undetermined > Sample names: sample1 sample2 sample3 ... > > However, note that you do get a warning message about sample > names. In your readCtData command, you include "samples". This is > meant to be a character vector of sample names, if you don't want > to use file names as sample names. Have you actually defined a > variable called "samples" anywhere?? If you have your sample names > in e.g. a vector called my.samplenames, then you need to run > readCtData with: > > readCtData(files = files$File, path = path, n.features = 48, flag = > 4, feature = 6, type = 7, position = 3, Ct = 8, header = FALSE, SDS > = FALSE, n.data = 1, samples=my.samplenames, na.value = 40) > > If not, then skip the "samples" parameter altogether and say: > > readCtData(files = files$File, path = path, n.features = 48, flag = > 4, feature = 6, type = 7, position = 3, Ct = 8, header = FALSE, SDS > = FALSE, n.data = 1, na.value = 40) > > Does that change anything? > \Heidi > > > >> > head(read.delim(file.path(path, "files.txt"))) >> File Treatment >> 1 Sample4.txt Tumor >> 2 Sample67.txt Tumor >> 3 Sample80.txt Tumor >> 4 Sample44.txt Tumor >> 5 Sample76.txt Tumor >> 6 Sample94.txt Normal >> > tail(read.delim(file.path(path, "files.txt"))) >> File Treatment >> 94 Sample37.txt Tumor >> 95 Sample32.txt Tumor >> 96 Sample33.txt Tumor >> 97 Sample34.txt Tumor >> 98 Sample25.txt Tumor >> 99 Sample26.txt Normal >> >> > sessionInfo() >> R version 2.11.0 (2010-04-22) >> i386-pc-mingw32 >> locale: >> [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United >> States.1252 LC_MONETARY=English_United States.1252 >> [4] LC_NUMERIC=C LC_TIME=English_United >> States.1252 >> attached base packages: >> [1] stats graphics grDevices utils datasets methods base >> other attached packages: >> [1] HTqPCR_1.2.0 limma_3.4.0 RColorBrewer_1.0-2 >> Biobase_2.8.0 >> loaded via a namespace (and not attached): >> [1] affy_1.26.1 affyio_1.16.0 >> gdata_2.8.0 gplots_2.7.4 gtools_2.6.2 >> preprocessCore_1.10.0 >> [7] tools_2.11.0 >> >> > > > > > -- > Deepak Roshan V G > Laboratory Of Cell Cycle Regulation & Molecular Oncology > Division of Cancer Research > Regional Cancer Centre > Thiruvananthapuram > Kerala, India 695 011 [[alternative HTML version deleted]]

> As iam basic biologist iam facing problem in each step. Have you already had a look at some of the R tutorials available? Familiarising yourself with the basic data structures and commands in R might take a bit of time, but it'll make your life much easier down the line. "An Introduction to R" at http://www.r-project.org/ -> Manuals is a good place to start. In the HTqPCR installation notes at http://www.ebi.ac.uk/bertone/software.html we mention a few other links. > getting difficulty in understanding error. could u help in getting > in details of commands other than the pdfs which we get with this > package. The details of R commands are generally available by typing ? in front of the command in the R terminal, such as e.g. ?plotCtOverview These help pages provide information about all input parameters, output formats, details of functions and example of how to run them. Together with the package vignettes available through openVignette() they form the core of the available BioConductor package documentation. I'm afraid that at least for HTqPCR there's nothing beyond that. Except of course the archives of the BioConductor mailing list... Therefore, please keep all replies to the mailing list, so that maybe others in the future can benefit from the questions and answers provided here. > all that i have.. > deepak > > > plotCtOverview(raw, genes=g, xlim=c(0,50), groups=files$Treatment, > + calibrator="Control") > Error in t(log2(t(M)/cali.mean)) : > dims [product 20] do not match the length of object [0] Did you manage to solve the problem you had previously with reading in all 99 samples? If not, it could be that you have <99 samples present in the qPCRset object "raw", but 99 different groups listed in "files$Treatment". Also, are any of your samples actually labelled "Control"? From your previous questions it looks like files$Treatment is either "Tumor" or "Normal". In that case you ahve to select one of these two as the calibrator, i.e. the sample group you want to compare other group(s) against. Please see ?plotCtOverview for the explanation of what the different parameters d, and what sort of input they require. HTH \Heidi > > traceback() > 2: t(log2(t(M)/cali.mean)) > 1: plotCtOverview(raw, genes = g, xlim = c(0, 50), groups = files > $Treatment, > calibrator = "Control") > > > sessionInfo() > R version 2.11.0 (2010-04-22) > x86_64-pc-mingw32 > > locale: > [1] LC_COLLATE=English_India.1252 LC_CTYPE=English_India.1252 > LC_MONETARY=English_India.1252 LC_NUMERIC=C > LC_TIME=English_India.1252 > > attached base packages: > [1] stats graphics grDevices utils datasets methods base > > other attached packages: > [1] HTqPCR_1.2.0 limma_3.4.1 RColorBrewer_1.0-2 > Biobase_2.8.0 > > loaded via a namespace (and not attached): > [1] affy_1.26.1 affyio_1.16.0 > gdata_2.8.0 gplots_2.7.4 gtools_2.6.2 > preprocessCore_1.10.0 tools_2.11.0 > > > > > On Tue, Jun 1, 2010 at 9:26 PM, Heidi Dvinge <heidi@ebi.ac.uk> wrote: > Hello Deepak, > > On 1 Jun 2010, at 16:21, deepak roshan wrote: > >> Dear Sir, >> >> >> Thank you for helps.Again iam facing some problems in raedCtData >> step. i changed exFiles$File[c(1:30) it reads 30 sample but when i >> change to exFiles$File[c(1:99) then it is showing error. could you >> tell me how to read 99 sample or what is the maximum number of sample >> we can analysis. > > As I've mentioned before, there's no inherent limit to how many > files can be read in. In your example below you get an error > somewhere between file number 30 and 99, indicating that there's > some input that readCtData() doesn't expect. It could either be a > bug in readCtData, or some issue with one of your files. > > To narrow down a bit where the problem is, why don't you try: > > for (i in 1:99) { > print(i) > test <- readCtData(files=exFiles$File[i], path=exPath, n.features > = 48, flag = 4, feature = 6, type = 7, position = 3, Ct = 8, header > = FALSE, SDS = FALSE, n.data = 1, na.value = 40) > } > > Also, in general using traceback() after an error can help you (/ > us) narrow down where the error occurred, in case it's in a sub- > function. > >> Second question is how to give feature class like in example it is >> given featureClass(raw) <- factor(c("Marker", "TF", "Kinase") in >> my case it is cyclins and cyclin inhibitors where to change that. >> > You can change the featureClass information anywhere along the way, > such as right after you read in the data. If you have 48 features > for each plate, and for example the first 10 are cyclins and the > last 38 cyclin inhibitors, you can say: > > featureClass(raw) <- factor(rep(c("Cyclin", "Cyclin_Inhibitor"), > times=c(10,38))) > > HTH > \Heidi > >> Details of the session is given below >> >> Deepak >> >> >> > data(qPCRraw) >> > data(qPCRpros) >> > class(qPCRraw) >> >> [1] "qPCRset" >> attr(,"package") >> [1] ".GlobalEnv" >> >> > path <- "F:/HTqPCR/TLDA_modi" >> > exPath <- "F:/HTqPCR/TLDA_modi" >> > exFiles <- read.delim(file.path(exPath, "files.txt")) >> > raw <- readCtData(files=exFiles$File[c(1:30)], path=exPath, >> n.features = 48, flag = 4, feature = 6, type = 7, position = 3, Ct >> = 8, header = FALSE, SDS = FALSE, n.data = 1, na.value = 40) >> > raw >> >> An object of class "qPCRset" >> Size: 48 features, 30 samples >> Feature types: Endogenous Control, Target >> Feature names: CCNF-Hs00171049_m1 RBL2-Hs00180562_m1 CCND1- >> Hs00277039_m1 ... >> Feature classes: >> Feature categories: OK, Undetermined >> Sample names: Sample4 Sample67 Sample80 ... >> > raw <- readCtData(files=exFiles$File[c(1:99)], path=exPath, >> n.features = 48, flag = 4, feature = 6, type = 7, position = 3, Ct >> = 8, header = FALSE, SDS = FALSE, n.data = 1, na.value = 40) >> >> Error in out@exprs[, cols] <- apply(data, 2, function(x) as.numeric >> (as.character(x))) : >> number of items to replace is not a multiple of replacement length >> > sessionInfo() >> R version 2.11.0 (2010-04-22) >> x86_64-pc-mingw32 >> >> locale: >> [1] LC_COLLATE=English_India.1252 LC_CTYPE=English_India.1252 >> LC_MONETARY=English_India.1252 LC_NUMERIC=C >> LC_TIME=English_India.1252 >> >> attached base packages: >> [1] stats graphics grDevices utils datasets methods base >> >> other attached packages: >> [1] HTqPCR_1.2.0 limma_3.4.1 RColorBrewer_1.0-2 >> Biobase_2.8.0 >> >> loaded via a namespace (and not attached): >> [1] affy_1.26.1 affyio_1.16.0 >> gdata_2.8.0 gplots_2.7.4 gtools_2.6.2 >> preprocessCore_1.10.0 tools_2.11.0 >> > >> >> >> On Mon, May 31, 2010 at 6:46 PM, Heidi Dvinge <heidi@ebi.ac.uk> >> wrote: >>> >>> Dear sir, >>> >>> thank you for your all help. iam facing some problems in >>> raedCtData step. i have 99 sample but it is deading only 6 >>> samples.how the sample numbers can be incereased to 99. details >>> of the session is given below >>> >>> deepak >>> >>> > n.samples <- 99 >>> > readCtData(files = files$File, path = path, n.features = 48, >>> flag = 4, feature = 6, type = 7, position = 3, Ct = 8, header = >>> FALSE, SDS = FALSE, n.data = 1, samples, na.value = 40) >>> An object of class "qPCRset" >>> Size: 48 features, 6 samples >>> Feature types: Endogenous Control, Target >>> Feature names: CCNF-Hs00171049_m1 RBL2-Hs00180562_m1 >>> CCND1-Hs00277039_m1 ... >>> Feature classes: >>> Feature categories: OK >>> Sample names: Sample1 Sample2 Sample3 ... >>> Warning message: >>> In readCtData(files = files$File, path = path, n.features = 48, : >>> Not enough sample names provided; using Sample1, Sample2, ... >>> instead >>> >> >> Hello Deepak, >> >> there is nothing in readCtData specifying that only 6 samples can >> be read. Defining a variable n.samples isn't going to change that, >> since you don't actually use this variable anywhere. What matters >> here is if your length(files$File) is 99. What does just: >> >> > files$File >> >> say? >> >> In the example data there are only 6 files, but if I read some of >> them multiple times, I can say: >> >> > exPath <- system.file("exData", package="HTqPCR") >> > exFiles <- read.delim(file.path(exPath, "files.txt")) >> > raw <- readCtData(files=exFiles$File[c(1:6,2:5,1:6)], path=exPath) >> > raw >> An object of class "qPCRset" >> Size: 384 features, 16 samples >> Feature types: Endogenous Control, Target >> Feature names: Gene1 Gene2 Gene3 ... >> Feature classes: >> Feature categories: OK, Undetermined >> Sample names: sample1 sample2 sample3 ... >> >> However, note that you do get a warning message about sample >> names. In your readCtData command, you include "samples". This is >> meant to be a character vector of sample names, if you don't want >> to use file names as sample names. Have you actually defined a >> variable called "samples" anywhere?? If you have your sample names >> in e.g. a vector called my.samplenames, then you need to run >> readCtData with: >> >> readCtData(files = files$File, path = path, n.features = 48, flag >> = 4, feature = 6, type = 7, position = 3, Ct = 8, header = FALSE, >> SDS = FALSE, n.data = 1, samples=my.samplenames, na.value = 40) >> >> If not, then skip the "samples" parameter altogether and say: >> >> readCtData(files = files$File, path = path, n.features = 48, flag >> = 4, feature = 6, type = 7, position = 3, Ct = 8, header = FALSE, >> SDS = FALSE, n.data = 1, na.value = 40) >> >> Does that change anything? >> \Heidi >> >> >> >>> > head(read.delim(file.path(path, "files.txt"))) >>> File Treatment >>> 1 Sample4.txt Tumor >>> 2 Sample67.txt Tumor >>> 3 Sample80.txt Tumor >>> 4 Sample44.txt Tumor >>> 5 Sample76.txt Tumor >>> 6 Sample94.txt Normal >>> > tail(read.delim(file.path(path, "files.txt"))) >>> File Treatment >>> 94 Sample37.txt Tumor >>> 95 Sample32.txt Tumor >>> 96 Sample33.txt Tumor >>> 97 Sample34.txt Tumor >>> 98 Sample25.txt Tumor >>> 99 Sample26.txt Normal >>> >>> > sessionInfo() >>> R version 2.11.0 (2010-04-22) >>> i386-pc-mingw32 >>> locale: >>> [1] LC_COLLATE=English_United States.1252 >>> LC_CTYPE=English_United States.1252 LC_MONETARY=English_United >>> States.1252 >>> [4] LC_NUMERIC=C LC_TIME=English_United >>> States.1252 >>> attached base packages: >>> [1] stats graphics grDevices utils datasets methods base >>> other attached packages: >>> [1] HTqPCR_1.2.0 limma_3.4.0 RColorBrewer_1.0-2 >>> Biobase_2.8.0 >>> loaded via a namespace (and not attached): >>> [1] affy_1.26.1 affyio_1.16.0 >>> gdata_2.8.0 gplots_2.7.4 gtools_2.6.2 >>> preprocessCore_1.10.0 >>> [7] tools_2.11.0 >>> >>> >> >> >> >> >> -- >> Deepak Roshan V G >> Laboratory Of Cell Cycle Regulation & Molecular Oncology >> Division of Cancer Research >> Regional Cancer Centre >> Thiruvananthapuram >> Kerala, India 695 011 > > > > > -- > Deepak Roshan V G > Laboratory Of Cell Cycle Regulation & Molecular Oncology > Division of Cancer Research > Regional Cancer Centre > Thiruvananthapuram > Kerala, India 695 011 [[alternative HTML version deleted]]

Dear sir, The previous problem was solved that was due to the Ct min. Because as default ct min was 10 but in my card i used 18sRNA which had ct below 10 along with that the quantile i changed to NULL from 0.8 because i didn’t had replicates of samples i change the whole command as following then it worked >raw.cat <- setCategory(raw, Ct.max = 38, Ct.min = 5, replicates = TRUE, quantile = NULL, groups, flag = TRUE, flag.out = "Failed", verbose = TRUE, plot = FALSE) and two type of normalization worked but the norm.rank and deltact type normalization didn’t worked details are given below. Without delta ct normalization i not able to further analysis. deepak > nr.norm <- normalizeCtDataraw.cat, norm="norm.rank") Normalizing Ct values Using rank invariant genes: Sample4: 16 rank invariant genes Sample67: 19 rank invariant genes Sample80: 21 rank invariant genes Sample44: 21 rank invariant genes Sample76: 18 rank invariant genes Sample94: 22 rank invariant genes Sample17: 14 rank invariant genes Sample37: 2 rank invariant genes Sample32: 14 rank invariant genes Sample33: 12 rank invariant genes Sample34: 14 rank invariant genes Sample25: 11 rank invariant genes Sample26: 10 rank invariant genes Warning message: In approx(ri.sub$n.curve$y, ri.sub$n.curve$x, xout = data[, i], : collapsing to unique 'x' values > traceback() No traceback available > normalizeCtDataraw.cat, norm = "deltaCt", deltaCt.genes = c("Gene11"), scale.rank.samples, Ct.max = 38, verbose = TRUE) Calculating deltaCt values Using control gene(s): Gene11 Card 1: Mean=NaN Stdev=NA Card 2: Mean=NaN Stdev=NA Card 3: Mean=NaN Stdev=NA Card 4: Mean=NaN Stdev=NA Card 5: Mean=NaN Stdev=NA Card 6: Mean=NaN Stdev=NA Card 7: Mean=NaN Stdev=NA Card 8: Mean=NaN Stdev=NA Card 9: Mean=NaN Stdev=NA Card 92: Mean=NaN Stdev=NA Card 93: Mean=NaN Stdev=NA Card 94: Mean=NaN Stdev=NA Card 95: Mean=NaN Stdev=NA Card 96: Mean=NaN Stdev=NA Card 97: Mean=NaN Stdev=NA Card 98: Mean=NaN Stdev=NA Card 99: Mean=NaN Stdev=NA An object of class "qPCRset" Size: 48 features, 99 samples Feature types: Endogenous Control, Target Feature names: CCNF RBL2 CCND1 ... Feature classes: Feature categories: OK, Undetermined Sample names: Sample4 Sample67 Sample80 ... > traceback() No traceback available > sessionInfo() R version 2.11.0 (2010-04-22) x86_64-pc-mingw32 locale: [1] LC_COLLATE=English_India.1252 LC_CTYPE=English_India.1252 LC_MONETARY=English_India.1252 LC_NUMERIC=C LC_TIME=English_India.1252 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] HTqPCR_1.2.0 limma_3.4.1 RColorBrewer_1.0-2 Biobase_2.8.0 loaded via a namespace (and not attached): [1] affy_1.26.1 affyio_1.16.0 gdata_2.8.0 gplots_2.7.4 gtools_2.6.2 preprocessCore_1.10.0 > deepak On 6/4/10, Heidi Dvinge <heidi@ebi.ac.uk> wrote: > > Hello Deepak, > > > please keep conversations on the BioC email list. As I mentioned in my > previous email to you, the BioC email archive is a valuable source of > information in addition to vignettes and the default "?" help pages. > > Dear sir, > The previous problem was solved and that occurred due one of sample had > only 47 gene data instead of 48. > > > > Glad to hear that has worked out. > > >when i give raw.cat <- setCategory(raw, groups=files$Treatment, > quantile=0.8) for all 99 sample Unreliable, Undetermined, OK values came all > 99 sample showed 48 gene when we add Unreliable, Undetermined, OK but in > error it is shown as *missing values* are not allowed in subscripted > assignments of data frames. > > deepak > > > > > path <- "F:/HTqPCR/TLDA" > > > exPath <- "F:/HTqPCR/TLDA" > > > exFiles <- read.delim(file.path(exPath, "files.txt")) > > > raw <- readCtData(files=exFiles$File[c(1:99)], path=exPath, n.features = > 48, flag = 4, feature = 6, type = 7, position = 3, Ct = 8, header = FALSE, > SDS = FALSE, n.data = 1, na.value = 40) > > > raw > > An object of class "qPCRset" > > Size: 48 features, 99 samples > > Feature types: Endogenous Control, Target > > Feature names: CCNF RBL2 CCND1 ... > > Feature classes: > > Feature categories: OK, Undetermined > > Sample names: Sample4 Sample67 Sample80 ... > > > path <- "F:/HTqPCR/TLDA" > > > files <- read.delim(file.path(path, "files.txt")) > > > g <- featureNames(raw)[1:48] > > > plotCtOverview(raw, genes=g, xlim=c(0,150), groups=files$Treatment, > conf.int=TRUE, > > + ylim=c(0,55)) > > > plotCtOverview(raw, genes=g, xlim=c(0,150), groups=files$Treatment, > > + calibrator="Normal") > > > plotCtCard(raw, col.range=c(10,35), well.size=2.6) > > > featureClass(raw) <- factor(rep(c("Cyclin", "Cyclin_Inhibitor"), > times=c(10,38))) > > > plotCtCard(raw, plot="class", well.size=2.6) > > > plotCtCard(raw, col.range=c(10,35), well.size=2.6) > > > > > > > raw.cat <- setCategory(raw, groups=files$Treatment, quantile=0.8) > > Categories after Ct.max and Ct.min filtering: > > > > Error in `[<-.data.frame`(`*tmp*`, index, value = "Unreliable") : > > missing values are not allowed in subscripted assignments of data frames > > > > I have seen something like this once before (it has been fixed in HTqPCR > devel), but that was after using the rbind and cbind functions so I'm not > sure what's going on here. What does: > > > class(featureCategory(raw)) > class(featureCategory(raw)[,1]) > > > say? If the second says "factor" instead of "character", then try typing: > > > featureCategory(raw) <- data.frame(apply(featureCategory(raw), > 2, as.character), stringsAsFactors=FALSE) > > > Also, what's currently in your featureCategory? Try saying: > > > head(featureCategory(raw)) > > > HTH > \Heidi > > > > > traceback() > > 4: stop("missing values are not allowed in subscripted assignments of data > frames") > > 3: `[<-.data.frame`(`*tmp*`, index, value = "Unreliable") > > 2: `[<-`(`*tmp*`, index, value = "Unreliable") > > 1: setCategory(raw, groups = files$Treatment, quantile = 0.8) > > > sessionInfo() > > R version 2.11.0 (2010-04-22) > > x86_64-pc-mingw32 > > > > locale: > > [1] LC_COLLATE=English_India.1252 LC_CTYPE=English_India.1252 > > [3] LC_MONETARY=English_India.1252 LC_NUMERIC=C > > [5] LC_TIME=English_India.1252 > > > > attached base packages: > > [1] stats graphics grDevices utils datasets methods base > > > > other attached packages: > > [1] HTqPCR_1.2.0 limma_3.4.1 RColorBrewer_1.0-2 Biobase_2.8.0 > > > > > loaded via a namespace (and not attached): > > [1] affy_1.26.1 affyio_1.16.0 gdata_2.8.0 > > [4] gplots_2.7.4 gtools_2.6.2 preprocessCore_1.10.0 > > > > > -- > Deepak Roshan V G > Laboratory Of Cell Cycle Regulation & Molecular Oncology > Division of Cancer Research > Regional Cancer Centre > Thiruvananthapuram > Kerala, India 695 011 > > > > -- Deepak Roshan V G Laboratory Of Cell Cycle Regulation & Molecular Oncology Division of Cancer Research Regional Cancer Centre Thiruvananthapuram Kerala, India 695 011 [[alternative HTML version deleted]]

> Dear sir, > Hello Deepak, again, please keep all discussion to the list! I.e. reply to all (including bioconductor at stat.math.ethz.ch) and not just the person who answered your question. > > The previous problem was solved that was due to the Ct min. Because as > default ct min was 10 but in my card i used 18sRNA which had ct below 10 > along with that the quantile i changed to NULL from 0.8 because i didn?t > had > replicates of samples i change the whole command as following then it > worked > >>raw.cat <- setCategory(raw, Ct.max = 38, Ct.min = 5, replicates = TRUE, > quantile = NULL, groups, flag = TRUE, flag.out = "Failed", verbose = TRUE, > plot = FALSE) > Glad to hear that it worked out. > > and two type of normalization worked but the norm.rank and deltact type > normalization didn?t worked details are given below. Without delta ct > normalization i not able to further analysis. > Why not? DeltaCt is just one type of normalisation, out of 4 currently implemented in HTqPCR. As such, you can select just a single normalisation, or none at all depending on how much you trust your initial data, and proceed with that. That being said, in your example below you use the command: >> normalizeCtDataraw.cat, norm = "deltaCt", deltaCt.genes = c("Gene11"), > scale.rank.samples, Ct.max = 38, verbose = TRUE) I.e. you normalise everything to the expression of a feature on your card called "Gene11". Do you have a gene by that name in your featureNamesraw.cat)? If not, then you're trying to normalise against nothing... Maybe you want deltaCt.genes="18sRNA"? HTH \Heidi > deepak > > > >> nr.norm <- normalizeCtDataraw.cat, norm="norm.rank") > > Normalizing Ct values > > Using rank invariant genes: > > Sample4: 16 rank invariant genes > > Sample67: 19 rank invariant genes > > Sample80: 21 rank invariant genes > > Sample44: 21 rank invariant genes > > Sample76: 18 rank invariant genes > > Sample94: 22 rank invariant genes > > Sample17: 14 rank invariant genes > > Sample37: 2 rank invariant genes > > Sample32: 14 rank invariant genes > > Sample33: 12 rank invariant genes > > Sample34: 14 rank invariant genes > > Sample25: 11 rank invariant genes > > Sample26: 10 rank invariant genes > > Warning message: > > In approx(ri.sub$n.curve$y, ri.sub$n.curve$x, xout = data[, i], : > > collapsing to unique 'x' values > >> traceback() > > No traceback available > > > > > >> normalizeCtDataraw.cat, norm = "deltaCt", deltaCt.genes = c("Gene11"), > scale.rank.samples, Ct.max = 38, verbose = TRUE) > > Calculating deltaCt values > > Using control gene(s): Gene11 > > Card 1: Mean=NaN Stdev=NA > > Card 2: Mean=NaN Stdev=NA > > Card 3: Mean=NaN Stdev=NA > > Card 4: Mean=NaN Stdev=NA > > Card 5: Mean=NaN Stdev=NA > > Card 6: Mean=NaN Stdev=NA > > Card 7: Mean=NaN Stdev=NA > > Card 8: Mean=NaN Stdev=NA > > Card 9: Mean=NaN Stdev=NA > > > > > > > > Card 92: Mean=NaN Stdev=NA > > Card 93: Mean=NaN Stdev=NA > > Card 94: Mean=NaN Stdev=NA > > Card 95: Mean=NaN Stdev=NA > > Card 96: Mean=NaN Stdev=NA > > Card 97: Mean=NaN Stdev=NA > > Card 98: Mean=NaN Stdev=NA > > Card 99: Mean=NaN Stdev=NA > > An object of class "qPCRset" > > Size: 48 features, 99 samples > > Feature types: Endogenous Control, Target > > Feature names: CCNF RBL2 CCND1 ... > > Feature classes: > > Feature categories: OK, Undetermined > > Sample names: Sample4 Sample67 Sample80 ... > >> traceback() > > No traceback available > >> sessionInfo() > > R version 2.11.0 (2010-04-22) > > x86_64-pc-mingw32 > > > > locale: > > [1] LC_COLLATE=English_India.1252 LC_CTYPE=English_India.1252 > LC_MONETARY=English_India.1252 > LC_NUMERIC=C LC_TIME=English_India.1252 > > > > attached base packages: > > [1] stats graphics grDevices utils datasets methods base > > > > other attached packages: > > [1] HTqPCR_1.2.0 limma_3.4.1 RColorBrewer_1.0-2 Biobase_2.8.0 > > > > > loaded via a namespace (and not attached): > > [1] affy_1.26.1 affyio_1.16.0 gdata_2.8.0 > gplots_2.7.4 gtools_2.6.2 preprocessCore_1.10.0 > >> > > > > > > > > deepak > > > On 6/4/10, Heidi Dvinge <heidi at="" ebi.ac.uk=""> wrote: >> >> Hello Deepak, >> >> >> please keep conversations on the BioC email list. As I mentioned in my >> previous email to you, the BioC email archive is a valuable source of >> information in addition to vignettes and the default "?" help pages. >> >> Dear sir, >> The previous problem was solved and that occurred due one of sample had >> only 47 gene data instead of 48. >> >> >> >> Glad to hear that has worked out. >> >> >when i give raw.cat <- setCategory(raw, groups=files$Treatment, >> quantile=0.8) for all 99 sample Unreliable, Undetermined, OK values came >> all >> 99 sample showed 48 gene when we add Unreliable, Undetermined, OK but in >> error it is shown as *missing values* are not allowed in subscripted >> assignments of data frames. >> >> deepak >> >> >> >> > path <- "F:/HTqPCR/TLDA" >> >> > exPath <- "F:/HTqPCR/TLDA" >> >> > exFiles <- read.delim(file.path(exPath, "files.txt")) >> >> > raw <- readCtData(files=exFiles$File[c(1:99)], path=exPath, n.features >> = >> 48, flag = 4, feature = 6, type = 7, position = 3, Ct = 8, header = >> FALSE, >> SDS = FALSE, n.data = 1, na.value = 40) >> >> > raw >> >> An object of class "qPCRset" >> >> Size: 48 features, 99 samples >> >> Feature types: Endogenous Control, Target >> >> Feature names: CCNF RBL2 CCND1 ... >> >> Feature classes: >> >> Feature categories: OK, Undetermined >> >> Sample names: Sample4 Sample67 Sample80 ... >> >> > path <- "F:/HTqPCR/TLDA" >> >> > files <- read.delim(file.path(path, "files.txt")) >> >> > g <- featureNames(raw)[1:48] >> >> > plotCtOverview(raw, genes=g, xlim=c(0,150), groups=files$Treatment, >> conf.int=TRUE, >> >> + ylim=c(0,55)) >> >> > plotCtOverview(raw, genes=g, xlim=c(0,150), groups=files$Treatment, >> >> + calibrator="Normal") >> >> > plotCtCard(raw, col.range=c(10,35), well.size=2.6) >> >> > featureClass(raw) <- factor(rep(c("Cyclin", "Cyclin_Inhibitor"), >> times=c(10,38))) >> >> > plotCtCard(raw, plot="class", well.size=2.6) >> >> > plotCtCard(raw, col.range=c(10,35), well.size=2.6) >> >> >> >> >> >> > raw.cat <- setCategory(raw, groups=files$Treatment, quantile=0.8) >> >> Categories after Ct.max and Ct.min filtering: >> >> >> >> Error in `[<-.data.frame`(`*tmp*`, index, value = "Unreliable") : >> >> missing values are not allowed in subscripted assignments of data >> frames >> >> >> >> I have seen something like this once before (it has been fixed in HTqPCR >> devel), but that was after using the rbind and cbind functions so I'm >> not >> sure what's going on here. What does: >> >> >> class(featureCategory(raw)) >> class(featureCategory(raw)[,1]) >> >> >> say? If the second says "factor" instead of "character", then try >> typing: >> >> >> featureCategory(raw) <- data.frame(apply(featureCategory(raw), >> 2, as.character), stringsAsFactors=FALSE) >> >> >> Also, what's currently in your featureCategory? Try saying: >> >> >> head(featureCategory(raw)) >> >> >> HTH >> \Heidi >> >> >> >> > traceback() >> >> 4: stop("missing values are not allowed in subscripted assignments of >> data >> frames") >> >> 3: `[<-.data.frame`(`*tmp*`, index, value = "Unreliable") >> >> 2: `[<-`(`*tmp*`, index, value = "Unreliable") >> >> 1: setCategory(raw, groups = files$Treatment, quantile = 0.8) >> >> > sessionInfo() >> >> R version 2.11.0 (2010-04-22) >> >> x86_64-pc-mingw32 >> >> >> >> locale: >> >> [1] LC_COLLATE=English_India.1252 LC_CTYPE=English_India.1252 >> >> [3] LC_MONETARY=English_India.1252 LC_NUMERIC=C >> >> [5] LC_TIME=English_India.1252 >> >> >> >> attached base packages: >> >> [1] stats graphics grDevices utils datasets methods base >> >> >> >> other attached packages: >> >> [1] HTqPCR_1.2.0 limma_3.4.1 RColorBrewer_1.0-2 >> Biobase_2.8.0 >> >> >> >> >> loaded via a namespace (and not attached): >> >> [1] affy_1.26.1 affyio_1.16.0 gdata_2.8.0 >> >> [4] gplots_2.7.4 gtools_2.6.2 preprocessCore_1.10.0 >> >> >> >> >> -- >> Deepak Roshan V G >> Laboratory Of Cell Cycle Regulation & Molecular Oncology >> Division of Cancer Research >> Regional Cancer Centre >> Thiruvananthapuram >> Kerala, India 695 011 >> >> >> >> > > > > -- > Deepak Roshan V G > Laboratory Of Cell Cycle Regulation & Molecular Oncology > Division of Cancer Research > Regional Cancer Centre > Thiruvananthapuram > Kerala, India 695 011 >