Question: HTqPCR problems
0
gravatar for Heidi Dvinge
7.1 years ago by
Heidi Dvinge2.0k
Heidi Dvinge2.0k wrote:
Hi Simon, I'm glad you sorted this out. You are correct in saying that HTqPCR (and indeed myself) aren't aware of the various loadings order for all the different qPCR platforms. Given a file, readCtData will simply start at row 1 and continue until the end of the file. I'll add a note to the help files specifying that this is the order in which the samples are being read in. Best, \Heidi > Hi Heidi, > I think I've identified the problem. Currently it appears as though HTqPCR > reads the sample ID's and genes in from top to bottom of the CSV output > from the Biomark. This is not the sample order we load in. As long as > thats made clear in the vignette, it will prevent any confusion. We > typically have a loading list, in which we associate samples with groups > (numbered 1-48, or 1-96 for both formats). I was getting confusing results > (laid out below) as I assumed HTqPCR associated sample IDs in the loading > order, not the CSV format top to bottom. Am I correct here in how HTqPCR > reads in the data from the CSV file? > > thanks again, > > best > > Simon > > On Jun 28, 2012, at 2:11 PM, Simon Melov wrote: > >> Hi Heidi, >> getting there, hopefully if you can clarify the following issue, all >> will be well and good. >> >> After yesterdays correspondence, I'm now producing nice plots, when I >> check the actual values being plotted, they dont match up >> to the sample ID's. In fact, if I dont bother assigning groups, the >> sample ID's dont match to their respective gene CT values. I'm >> worried there is some underlying problem with the data structure I'm not >> understanding. >> >> I understand the code, its just the samples dont match the reported gene >> values in the csv file. >> >> for example >> >>> head(groupID) >> Sample Treatment >> 1 S28 SMY >> 2 L20 LFY >> 3 M26 MMY >> 4 L1 LFR >> 5 L30 LFY >> 6 K13 KMO >> >>> plotCtOverview(raw6, genes=featureNames(raw6)[1], >>> group=groupID$Treatment,legend=FALSE, >>> col=1:length(unique(groupID$Treatment))) >> >> produces a nice plot of a tubulin gene across the groups, as you >> suggested yesterday . Yet if I look at the values, they dont match >> the CSV values for specific genes/samples I used. If I turn off groups, >> and look at samples without merging by group, I can see that the values >> dont match the appropriate gene being >> displayed. My question is, where is the sample order being drawn from in >> the CSV file? Is there a simple check I can use to see that what is >> being plotted, >> is what I think is being plotted? The group ID sample-Treatment is >> correct, and all the samples in the original CSV file are correct. >> >> Is it possible that the package is assigning gene/sample ID in some >> other order than that I've supplied? >> I just want to be sure that when HTqPCR pulls the sample ID and maps it >> to the appropriate gene/Group, some transformation is not happening. >> >> Fluidigm suggests a particular order in loading samples and genes. These >> are numbered 1-48 (sample), and 1-48 (gene) for a 48.48 plate (and the >> same for a 96.96 plate). >> This is the order I supplied the sample IDs in the groupID file above. >> How do you map the raw csv output to gene/sample id? >> >> Is there a way of checking that the sample/gene/group ID is correct? >> >> as always, thanks in advance for your help >> >> best >> >> s >> On Jun 27, 2012, at 3:27 PM, Heidi Dvinge wrote: >> >>>> Hi Heidi, >>>> you are correct, yes 48.48. >>>> The example you provide below is exactly what I needed for >>>> clarification >>>> for groups. I was trying to reverse engineer what you had done with >>>> the >>>> original expression set package for microarrays, but from below, I can >>>> get >>>> this to work now. >>>> >>> Glad it works. Hopefully by the next BioConductor release I'll remember >>> to >>> clarify the plotCtOverview help file. >>> >>>> Just to be clear, I have 5 48.48 plates. Should I normalize each >>>> individually prior to combining, or should I reformat to a 2304x1 >>>> each, >>>> combine, then normalize (not sure if you can do that or not) >>>> >>> Hm, that's one of the questions I've also been asking myself, so I >>> would >>> be curious to hear what your results from this are. >>> >>> If you suspect that there are some major factors influencing the 5 >>> plates >>> systematically, then normalising them in a 2304 x 5 object should >>> (hopefully) correct for that. For example, they may have been run on >>> different days, by different people, or perhaps there was a short power >>> cut during the processing of one of them. This might be visible if you >>> have for example a boxplot of Ct from all 48*5 samples, and you see >>> blocks >>> of them shifted up or down. >>> >>> Obviously, this doesn't take care of normalisation between your samples >>> within each plate though. If you suspect your samples to have some >>> systematic variation that you need to account for, then you can >>> normalise >>> each plate individually (as a 48 x 48) object. Alternatively, you can >>> try >>> to combine within- and between-sample normalisation by taking all 48 x >>> 240 >>> values at once. >>> >>> In principle, you can split, reformat and the recombine the data in >>> however many ways you like. Personally, with any sort of data I prefer >>> to >>> go with as little preprocessing as possible, since each additional step >>> can potentially introduce its own biases into the data. So unless there >>> are some obvious variation between your 5 plates, I'd probably stick >>> with >>> just normalisation between the samples, e.. using a 48 x 240 object. >>> >>> Of course, you may have different preferences, or find out that a >>> completely different approach is required for this particular data set. >>> >>> \Heidi >>> >>>> thanks again for your prompt responses! >>>> >>>> best >>>> >>>> s >>>> >>>> On Jun 27, 2012, at 2:26 PM, Heidi Dvinge wrote: >>>> >>>>> Hi Simon, >>>>> >>>>>> Thanks for the help Heidi, >>>>>> but I'm still having troubles, your comments on the plotting helped >>>>>> me >>>>>> solve the outputs. But if I want to just display some groups (for >>>>>> example >>>>>> the LO group in the example below), how do I associate a group with >>>>>> multiple samples (ie biological reps)? >>>>>> >>>>>> Currently I'm associating genes with samples by reading in the file >>>>>> as >>>>>> below >>>>>> plate6=read.delim("plate6Sample.txt", header=FALSE) >>>>>> #this is a file to associate sample ID with the genes in the biomark >>>>>> data, >>>>>> as currently HTqPCR does not seem to associate the sample info in >>>>>> the >>>>>> Biomark output to the gene IDs >>>>>> >>>>> Erm, no, it doesn't :-/ >>>>> >>>>>> samples=as.vector(t(plate6)) >>>>>> raw6=readCtData(files="Chip6.csv", format="BioMark", n.features=48, >>>>>> n.data=48, samples=samples) >>>>>> #now I have samples and genes similar to your example in the guide, >>>>>> but >>>>>> I >>>>>> want to associate samples to groups now. In the guide, you have an >>>>>> example >>>>>> where you have entire files as distinct samples, but in our runs, we >>>>>> never >>>>>> have that situation. I have a file which associates samples to >>>>>> groups, >>>>>> which I read in... >>>>>> >>>>>> groupID=read.csv("plate6key.csv") >>>>>> >>>>>> but how do I associate the samples with their appropriate groups for >>>>>> biological replicates with any of the functions in HtQPCR? >>>>> >>>>> I'm afraid I'm slightly confused here (sorry, long day). Exactly how >>>>> is >>>>> your data formatted? I.e. are the columns either individual samples, >>>>> or >>>>> from files containing multiple samples? So for example for a single >>>>> 48.48 >>>>> arrays, is your qPCRset object 2304 x 1 or 48 x 48? >>>>> >>>>> From your readCtData command I'm guessing you have 48 x 48, i.e. all >>>>> 48 >>>>> samples from your 1 array are in columns. In that case the 'groups' >>>>> parameter in plotCtOverview will need to be a vector of length 48, >>>>> indicating how you want the 48 columns in your qPCRset object to be >>>>> grouped together. >>>>> >>>>> Below is an example of (admittedly ugly) plots. I don't know if >>>>> that's >>>>> similar to your situation at all. >>>>> >>>>> \Heidi >>>>> >>>>>> # Reading in data >>>>>> exPath <- system.file("exData", package = "HTqPCR") >>>>>> raw1 <- readCtData(files = "BioMark_sample.csv", path = exPath, >>>>>> format >>>>>> = >>>>> "BioMark", n.features = 48, n.data = 48) >>>>>> # Check sample names >>>>>> head(sampleNames(raw1)) >>>>> [1] "Sample1" "Sample2" "Sample3" "Sample4" "Sample5" "Sample6" >>>>>> # Associate samples with (randomly chosen) groups >>>>>> anno <- data.frame(sampleID=sampleNames(raw1), >>>>>> Group=rep(c("A", "B", >>>>> "C", "D"), times=c(4,24,5,15))) >>>>>> head(anno) >>>>> sampleID Group >>>>> 1 Sample1 A >>>>> 2 Sample2 A >>>>> 3 Sample3 A >>>>> 4 Sample4 A >>>>> 5 Sample5 B >>>>> 6 Sample6 B >>>>>> # Plot the first gene - for each sample individually >>>>>> plotCtOverview(raw1, genes=featureNames(raw1)[1], legend=FALSE, >>>>> col=1:nrow(anno)) >>>>>> # Plot the first gene - for each group >>>>>> plotCtOverview(raw1, genes=featureNames(raw1)[1], group=anno$Group, >>>>> legend=FALSE, col=1:length(unique(anno$Group))) >>>>>> # Plot the first gene, using group "A" as a control >>>>>> plotCtOverview(raw1, genes=featureNames(raw1)[1], group=anno$Group, >>>>> legend=FALSE, col=1:length(unique(anno$Group)), calibrator="A") >>>>> >>>>> >>>>> >>>>>> You recommend below using a vector, but I dont see how that helps me >>>>>> associate the samples in the Expression set. >>>>>> >>>>>> thanks again! >>>>>> >>>>>> s >>>>>> >>>>>> On Jun 26, 2012, at 12:48 PM, Heidi Dvinge wrote: >>>>>> >>>>>>>> Hi, >>>>>>>> I'm having some troubles selectively sub-setting, and graphing up >>>>>>>> QPCR >>>>>>>> data within HTqPCR for Biomark plates (both 48.48 and 96.96 >>>>>>>> plates). >>>>>>>> I'd >>>>>>>> like to be able to visualize specific genes, with specific groups >>>>>>>> we >>>>>>>> run >>>>>>>> routinely on our Biomark system. Typical runs are across multiple >>>>>>>> plates, >>>>>>>> and have multiple biological replicates, and usually 2 or more >>>>>>>> technical >>>>>>>> replicates (although we are moving away from technical reps, as >>>>>>>> the >>>>>>>> CVs >>>>>>>> are so tight). >>>>>>>> >>>>>>>> Can anyone help with this? Heidi? >>>>>>>> >>>>>>>> raw6=readCtData(files="Chip6.csv", format="BioMark", >>>>>>>> n.features=48, >>>>>>>> n.data=48, samples=samples) >>>>>>>> #Ive read the samples in from a separate file, as when you read it >>>>>>>> in, >>>>>>>> it >>>>>>>> doesnt take the sample names supplied in the biomark output# >>>>>>>> #Next, I want to plot genes of interest, with samples of interest, >>>>>>>> and >>>>>>>> I'm >>>>>>>> having trouble getting an appropriate output# >>>>>>>> >>>>>>>> g=featureNames(raw6)[1:2] >>>>>>>> plotCtOverview(raw6, genes=g, groups=groupID$Treatment, >>>>>>>> col=rainbow(5)) >>>>>>>> >>>>>>>> #This plots 1 gene across all 48 samples# >>>>>>>> #but the legend doesnt behave, its placed on top of the plot, and >>>>>>>> I >>>>>>>> cant >>>>>>>> get it to display in a non-overlapping fashion# >>>>>>>> #I've tried all sorts of things in par, but nothing seems to shift >>>>>>>> the >>>>>>>> legend's position# >>>>>>>> >>>>>>> As the old saying goes, whenever you want a job done well, you'll >>>>>>> have >>>>>>> to >>>>>>> do it yourself ;). In this case, the easiest thing is probably to >>>>>>> use >>>>>>> legend=FALSE in plotCtOverview, and then afterwards add it yourself >>>>>>> in >>>>>>> the >>>>>>> desired location using legend(). That way, if you have a lot of >>>>>>> different >>>>>>> features or groups to display, you can also use the ncol parameter >>>>>>> in >>>>>>> legend to make several columns within the legend, such as 3x4 >>>>>>> instead >>>>>>> of >>>>>>> the default 12x1. >>>>>>> >>>>>>> Alternatively, you can use either xlim or ylim in plotCtOverview to >>>>>>> add >>>>>>> some empty space on the side where there's then room for the >>>>>>> legend. >>>>>>> >>>>>>>> #I now want to plot a subset of the samples for specific genes# >>>>>>>>> LOY=subset(groupID,groupID$Treatment=="LO" | groupID$Treatment== >>>>>>>>> "LFY") >>>>>>>>> LOY >>>>>>>> Sample Treatment >>>>>>>> 2 L20 LFY >>>>>>>> 5 L30 LFY >>>>>>>> 7 L45 LO >>>>>>>> 20 L40 LO >>>>>>>> 27 L43 LO >>>>>>>> 33 L29 LFY >>>>>>>> 36 L38 LO >>>>>>>> 40 L39 LO >>>>>>>> 43 L23 LFY >>>>>>>> >>>>>>>> >>>>>>>>> plotCtOverview(raw6, genes=g, groups=LOY, col=rainbow(5)) >>>>>>>> Warning messages: >>>>>>>> 1: In split.default(t(x), sample.split) : >>>>>>>> data length is not a multiple of split variable >>>>>>>> 2: In qt(p, df, lower.tail, log.p) : NaNs produced >>>>>>>>> >>>>>>> >>>>>>> Does it make sense if you split by groups=LOY$Treatment? It looks >>>>>>> like >>>>>>> the >>>>>>> object LOY itself is a data frame, rather than the expected vector. >>>>>>> >>>>>>> Also, you may have to 'repeat' the col=rainbow() argument to fit >>>>>>> your >>>>>>> number of features. >>>>>>> >>>>>>>> >>>>>>>> #it displays the two groups defined by treatment, but doesnt do so >>>>>>>> nicely, >>>>>>>> very skinny bars, and the legend doesnt reflect what its >>>>>>>> displaying# >>>>>>>> #again, I've tried monkeying around with par, but not sure what >>>>>>>> HTqPCR >>>>>>>> is >>>>>>>> calling to make the plots# >>>>>>>> >>>>>>> If the bars are very skinny, it's probably because you're >>>>>>> displaying a >>>>>>> lot >>>>>>> of features. Nothing much to do about that, except increasing the >>>>>>> width >>>>>>> or >>>>>>> your plot :(. >>>>>>> >>>>>>> \Heidi >>>>>>> >>>>>>>> please help! >>>>>>>> >>>>>>>> thanks >>>>>>>> >>>>>>>> Simon. >>>>>>>> >>>>>>>> _______________________________________________ >>>>>>>> Bioconductor mailing list >>>>>>>> Bioconductor at r-project.org >>>>>>>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>>>>>>> Search the archives: >>>>>>>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>>>>>>> >>>>>>> >>>>>>> >>>>>> >>>>>> >>>>> >>>>> >>>> >>>> >>> >>> >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor > >
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ADD COMMENTlink modified 7.1 years ago by Simon Melov340 • written 7.1 years ago by Heidi Dvinge2.0k
Answer: HTqPCR problems
0
gravatar for Simon Melov
7.1 years ago by
Simon Melov340
Simon Melov340 wrote:
Hi Heidi, I've a followup from the question from listed below. I've started to merge multiple plates together using rbind as you suggested. I've identical gene order per plate, but different biological samples belonging to different groups per plate. I can manipulate these using your graphing functions on a per plate basis, but I'm unclear how to address comparisons with regards to groups once the plates are merged. For example, I have 5 text files which tie sample ID to specific groups of interest. After importing and merging the 5 separate Biomark files by rbind, my merged object is 48x240. How do I then contrast the different groups? I'm clear on how to do this in a single plate (using one of the 5 individual text files), but confused with regards to the process after merging. Should I have a vector with 240 identifiers for the merged files which is in the same order the merged plates are? (top to bottom (row), and left to right? (by column). thanks Simon On Jun 27, 2012, at 3:27 PM, Heidi Dvinge wrote: >> Hi Heidi, >> you are correct, yes 48.48. >> The example you provide below is exactly what I needed for clarification >> for groups. I was trying to reverse engineer what you had done with the >> original expression set package for microarrays, but from below, I can get >> this to work now. >> > Glad it works. Hopefully by the next BioConductor release I'll remember to > clarify the plotCtOverview help file. > >> Just to be clear, I have 5 48.48 plates. Should I normalize each >> individually prior to combining, or should I reformat to a 2304x1 each, >> combine, then normalize (not sure if you can do that or not) >> > Hm, that's one of the questions I've also been asking myself, so I would > be curious to hear what your results from this are. > > If you suspect that there are some major factors influencing the 5 plates > systematically, then normalising them in a 2304 x 5 object should > (hopefully) correct for that. For example, they may have been run on > different days, by different people, or perhaps there was a short power > cut during the processing of one of them. This might be visible if you > have for example a boxplot of Ct from all 48*5 samples, and you see blocks > of them shifted up or down. > > Obviously, this doesn't take care of normalisation between your samples > within each plate though. If you suspect your samples to have some > systematic variation that you need to account for, then you can normalise > each plate individually (as a 48 x 48) object. Alternatively, you can try > to combine within- and between-sample normalisation by taking all 48 x 240 > values at once. > > In principle, you can split, reformat and the recombine the data in > however many ways you like. Personally, with any sort of data I prefer to > go with as little preprocessing as possible, since each additional step > can potentially introduce its own biases into the data. So unless there > are some obvious variation between your 5 plates, I'd probably stick with > just normalisation between the samples, e.. using a 48 x 240 object. > > Of course, you may have different preferences, or find out that a > completely different approach is required for this particular data set. > > \Heidi > >> thanks again for your prompt responses! >> >> best >> >> s >> >> On Jun 27, 2012, at 2:26 PM, Heidi Dvinge wrote: >> >>> Hi Simon, >>> >>>> Thanks for the help Heidi, >>>> but I'm still having troubles, your comments on the plotting helped me >>>> solve the outputs. But if I want to just display some groups (for >>>> example >>>> the LO group in the example below), how do I associate a group with >>>> multiple samples (ie biological reps)? >>>> >>>> Currently I'm associating genes with samples by reading in the file as >>>> below >>>> plate6=read.delim("plate6Sample.txt", header=FALSE) >>>> #this is a file to associate sample ID with the genes in the biomark >>>> data, >>>> as currently HTqPCR does not seem to associate the sample info in the >>>> Biomark output to the gene IDs >>>> >>> Erm, no, it doesn't :-/ >>> >>>> samples=as.vector(t(plate6)) >>>> raw6=readCtData(files="Chip6.csv", format="BioMark", n.features=48, >>>> n.data=48, samples=samples) >>>> #now I have samples and genes similar to your example in the guide, but >>>> I >>>> want to associate samples to groups now. In the guide, you have an >>>> example >>>> where you have entire files as distinct samples, but in our runs, we >>>> never >>>> have that situation. I have a file which associates samples to groups, >>>> which I read in... >>>> >>>> groupID=read.csv("plate6key.csv") >>>> >>>> but how do I associate the samples with their appropriate groups for >>>> biological replicates with any of the functions in HtQPCR? >>> >>> I'm afraid I'm slightly confused here (sorry, long day). Exactly how is >>> your data formatted? I.e. are the columns either individual samples, or >>> from files containing multiple samples? So for example for a single >>> 48.48 >>> arrays, is your qPCRset object 2304 x 1 or 48 x 48? >>> >>> From your readCtData command I'm guessing you have 48 x 48, i.e. all 48 >>> samples from your 1 array are in columns. In that case the 'groups' >>> parameter in plotCtOverview will need to be a vector of length 48, >>> indicating how you want the 48 columns in your qPCRset object to be >>> grouped together. >>> >>> Below is an example of (admittedly ugly) plots. I don't know if that's >>> similar to your situation at all. >>> >>> \Heidi >>> >>>> # Reading in data >>>> exPath <- system.file("exData", package = "HTqPCR") >>>> raw1 <- readCtData(files = "BioMark_sample.csv", path = exPath, format >>>> = >>> "BioMark", n.features = 48, n.data = 48) >>>> # Check sample names >>>> head(sampleNames(raw1)) >>> [1] "Sample1" "Sample2" "Sample3" "Sample4" "Sample5" "Sample6" >>>> # Associate samples with (randomly chosen) groups >>>> anno <- data.frame(sampleID=sampleNames(raw1), Group=rep(c("A", "B", >>> "C", "D"), times=c(4,24,5,15))) >>>> head(anno) >>> sampleID Group >>> 1 Sample1 A >>> 2 Sample2 A >>> 3 Sample3 A >>> 4 Sample4 A >>> 5 Sample5 B >>> 6 Sample6 B >>>> # Plot the first gene - for each sample individually >>>> plotCtOverview(raw1, genes=featureNames(raw1)[1], legend=FALSE, >>> col=1:nrow(anno)) >>>> # Plot the first gene - for each group >>>> plotCtOverview(raw1, genes=featureNames(raw1)[1], group=anno$Group, >>> legend=FALSE, col=1:length(unique(anno$Group))) >>>> # Plot the first gene, using group "A" as a control >>>> plotCtOverview(raw1, genes=featureNames(raw1)[1], group=anno$Group, >>> legend=FALSE, col=1:length(unique(anno$Group)), calibrator="A") >>> >>> >>> >>>> You recommend below using a vector, but I dont see how that helps me >>>> associate the samples in the Expression set. >>>> >>>> thanks again! >>>> >>>> s >>>> >>>> On Jun 26, 2012, at 12:48 PM, Heidi Dvinge wrote: >>>> >>>>>> Hi, >>>>>> I'm having some troubles selectively sub-setting, and graphing up >>>>>> QPCR >>>>>> data within HTqPCR for Biomark plates (both 48.48 and 96.96 plates). >>>>>> I'd >>>>>> like to be able to visualize specific genes, with specific groups we >>>>>> run >>>>>> routinely on our Biomark system. Typical runs are across multiple >>>>>> plates, >>>>>> and have multiple biological replicates, and usually 2 or more >>>>>> technical >>>>>> replicates (although we are moving away from technical reps, as the >>>>>> CVs >>>>>> are so tight). >>>>>> >>>>>> Can anyone help with this? Heidi? >>>>>> >>>>>> raw6=readCtData(files="Chip6.csv", format="BioMark", n.features=48, >>>>>> n.data=48, samples=samples) >>>>>> #Ive read the samples in from a separate file, as when you read it >>>>>> in, >>>>>> it >>>>>> doesnt take the sample names supplied in the biomark output# >>>>>> #Next, I want to plot genes of interest, with samples of interest, >>>>>> and >>>>>> I'm >>>>>> having trouble getting an appropriate output# >>>>>> >>>>>> g=featureNames(raw6)[1:2] >>>>>> plotCtOverview(raw6, genes=g, groups=groupID$Treatment, >>>>>> col=rainbow(5)) >>>>>> >>>>>> #This plots 1 gene across all 48 samples# >>>>>> #but the legend doesnt behave, its placed on top of the plot, and I >>>>>> cant >>>>>> get it to display in a non-overlapping fashion# >>>>>> #I've tried all sorts of things in par, but nothing seems to shift >>>>>> the >>>>>> legend's position# >>>>>> >>>>> As the old saying goes, whenever you want a job done well, you'll have >>>>> to >>>>> do it yourself ;). In this case, the easiest thing is probably to use >>>>> legend=FALSE in plotCtOverview, and then afterwards add it yourself in >>>>> the >>>>> desired location using legend(). That way, if you have a lot of >>>>> different >>>>> features or groups to display, you can also use the ncol parameter in >>>>> legend to make several columns within the legend, such as 3x4 instead >>>>> of >>>>> the default 12x1. >>>>> >>>>> Alternatively, you can use either xlim or ylim in plotCtOverview to >>>>> add >>>>> some empty space on the side where there's then room for the legend. >>>>> >>>>>> #I now want to plot a subset of the samples for specific genes# >>>>>>> LOY=subset(groupID,groupID$Treatment=="LO" | groupID$Treatment== >>>>>>> "LFY") >>>>>>> LOY >>>>>> Sample Treatment >>>>>> 2 L20 LFY >>>>>> 5 L30 LFY >>>>>> 7 L45 LO >>>>>> 20 L40 LO >>>>>> 27 L43 LO >>>>>> 33 L29 LFY >>>>>> 36 L38 LO >>>>>> 40 L39 LO >>>>>> 43 L23 LFY >>>>>> >>>>>> >>>>>>> plotCtOverview(raw6, genes=g, groups=LOY, col=rainbow(5)) >>>>>> Warning messages: >>>>>> 1: In split.default(t(x), sample.split) : >>>>>> data length is not a multiple of split variable >>>>>> 2: In qt(p, df, lower.tail, log.p) : NaNs produced >>>>>>> >>>>> >>>>> Does it make sense if you split by groups=LOY$Treatment? It looks like >>>>> the >>>>> object LOY itself is a data frame, rather than the expected vector. >>>>> >>>>> Also, you may have to 'repeat' the col=rainbow() argument to fit your >>>>> number of features. >>>>> >>>>>> >>>>>> #it displays the two groups defined by treatment, but doesnt do so >>>>>> nicely, >>>>>> very skinny bars, and the legend doesnt reflect what its displaying# >>>>>> #again, I've tried monkeying around with par, but not sure what >>>>>> HTqPCR >>>>>> is >>>>>> calling to make the plots# >>>>>> >>>>> If the bars are very skinny, it's probably because you're displaying a >>>>> lot >>>>> of features. Nothing much to do about that, except increasing the >>>>> width >>>>> or >>>>> your plot :(. >>>>> >>>>> \Heidi >>>>> >>>>>> please help! >>>>>> >>>>>> thanks >>>>>> >>>>>> Simon. >>>>>> >>>>>> _______________________________________________ >>>>>> Bioconductor mailing list >>>>>> Bioconductor at r-project.org >>>>>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>>>>> Search the archives: >>>>>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>>>>> >>>>> >>>>> >>>> >>>> >>> >>> >> >> > >
ADD COMMENTlink written 7.1 years ago by Simon Melov340
Hi Simon, > Hi Heidi, > I've a followup from the question from listed below. I've started to merge > multiple plates together using rbind as you suggested. I've identical gene > order per plate, but different biological samples belonging to different > groups per plate. I can manipulate these using your graphing functions on > a per plate basis, but I'm unclear how to address comparisons with regards > to groups once the plates are merged. > > For example, I have 5 text files which tie sample ID to specific groups of > interest. After importing and merging the 5 separate Biomark files by > rbind, my merged object is 48x240. How do I then contrast the different > groups? I'm clear on how to do this in a single plate (using one of the 5 > individual text files), but confused with regards to the process after > merging. Should I have a vector with 240 identifiers for the merged files > which is in the same order the merged plates are? (top to bottom (row), > and left to right? (by column). > Yes (presuming I've understood your description correct). You can either also just combine your individual text files with samples descriptions, or create a whole new object describing each of your samples, as long as the order of your descriptions are the same as the columns in your qPCRset object. Alternatively, please note that as of version 1.9.0 (AFAIR) qPCRsets now inherit from class ExpressionSet, i.e. they have a slot called phenoData (an AnnotatedDataFrame) similar to what objects for microarray analysis have. E.g. > data(qPCRraw) > phenoData(qPCRraw) An object of class "AnnotatedDataFrame" sampleNames: sample1 sample2 ... sample6 (6 total) varLabels: sample varMetadata: labelDescription > pData(qPCRraw) sample sample1 1 sample2 2 sample3 3 sample4 4 sample5 5 sample6 6 > pData(qPCRraw)$Genotype <- c("A", "B", "A", "A", "B", "B") > pData(qPCRraw) sample Genotype sample1 1 A sample2 2 B sample3 3 A sample4 4 A sample5 5 B sample6 6 B You can use this to store information about samples within the qPCRset object itself, rather than in 'external' objects. There's more info about this in the general help files for: ?AnnotatedDataFrame ?ExpressionSet \Heidi > thanks > > Simon > On Jun 27, 2012, at 3:27 PM, Heidi Dvinge wrote: > >>> Hi Heidi, >>> you are correct, yes 48.48. >>> The example you provide below is exactly what I needed for >>> clarification >>> for groups. I was trying to reverse engineer what you had done with the >>> original expression set package for microarrays, but from below, I can >>> get >>> this to work now. >>> >> Glad it works. Hopefully by the next BioConductor release I'll remember >> to >> clarify the plotCtOverview help file. >> >>> Just to be clear, I have 5 48.48 plates. Should I normalize each >>> individually prior to combining, or should I reformat to a 2304x1 each, >>> combine, then normalize (not sure if you can do that or not) >>> >> Hm, that's one of the questions I've also been asking myself, so I would >> be curious to hear what your results from this are. >> >> If you suspect that there are some major factors influencing the 5 >> plates >> systematically, then normalising them in a 2304 x 5 object should >> (hopefully) correct for that. For example, they may have been run on >> different days, by different people, or perhaps there was a short power >> cut during the processing of one of them. This might be visible if you >> have for example a boxplot of Ct from all 48*5 samples, and you see >> blocks >> of them shifted up or down. >> >> Obviously, this doesn't take care of normalisation between your samples >> within each plate though. If you suspect your samples to have some >> systematic variation that you need to account for, then you can >> normalise >> each plate individually (as a 48 x 48) object. Alternatively, you can >> try >> to combine within- and between-sample normalisation by taking all 48 x >> 240 >> values at once. >> >> In principle, you can split, reformat and the recombine the data in >> however many ways you like. Personally, with any sort of data I prefer >> to >> go with as little preprocessing as possible, since each additional step >> can potentially introduce its own biases into the data. So unless there >> are some obvious variation between your 5 plates, I'd probably stick >> with >> just normalisation between the samples, e.. using a 48 x 240 object. >> >> Of course, you may have different preferences, or find out that a >> completely different approach is required for this particular data set. >> >> \Heidi >> >>> thanks again for your prompt responses! >>> >>> best >>> >>> s >>> >>> On Jun 27, 2012, at 2:26 PM, Heidi Dvinge wrote: >>> >>>> Hi Simon, >>>> >>>>> Thanks for the help Heidi, >>>>> but I'm still having troubles, your comments on the plotting helped >>>>> me >>>>> solve the outputs. But if I want to just display some groups (for >>>>> example >>>>> the LO group in the example below), how do I associate a group with >>>>> multiple samples (ie biological reps)? >>>>> >>>>> Currently I'm associating genes with samples by reading in the file >>>>> as >>>>> below >>>>> plate6=read.delim("plate6Sample.txt", header=FALSE) >>>>> #this is a file to associate sample ID with the genes in the biomark >>>>> data, >>>>> as currently HTqPCR does not seem to associate the sample info in the >>>>> Biomark output to the gene IDs >>>>> >>>> Erm, no, it doesn't :-/ >>>> >>>>> samples=as.vector(t(plate6)) >>>>> raw6=readCtData(files="Chip6.csv", format="BioMark", n.features=48, >>>>> n.data=48, samples=samples) >>>>> #now I have samples and genes similar to your example in the guide, >>>>> but >>>>> I >>>>> want to associate samples to groups now. In the guide, you have an >>>>> example >>>>> where you have entire files as distinct samples, but in our runs, we >>>>> never >>>>> have that situation. I have a file which associates samples to >>>>> groups, >>>>> which I read in... >>>>> >>>>> groupID=read.csv("plate6key.csv") >>>>> >>>>> but how do I associate the samples with their appropriate groups for >>>>> biological replicates with any of the functions in HtQPCR? >>>> >>>> I'm afraid I'm slightly confused here (sorry, long day). Exactly how >>>> is >>>> your data formatted? I.e. are the columns either individual samples, >>>> or >>>> from files containing multiple samples? So for example for a single >>>> 48.48 >>>> arrays, is your qPCRset object 2304 x 1 or 48 x 48? >>>> >>>> From your readCtData command I'm guessing you have 48 x 48, i.e. all >>>> 48 >>>> samples from your 1 array are in columns. In that case the 'groups' >>>> parameter in plotCtOverview will need to be a vector of length 48, >>>> indicating how you want the 48 columns in your qPCRset object to be >>>> grouped together. >>>> >>>> Below is an example of (admittedly ugly) plots. I don't know if that's >>>> similar to your situation at all. >>>> >>>> \Heidi >>>> >>>>> # Reading in data >>>>> exPath <- system.file("exData", package = "HTqPCR") >>>>> raw1 <- readCtData(files = "BioMark_sample.csv", path = exPath, >>>>> format >>>>> = >>>> "BioMark", n.features = 48, n.data = 48) >>>>> # Check sample names >>>>> head(sampleNames(raw1)) >>>> [1] "Sample1" "Sample2" "Sample3" "Sample4" "Sample5" "Sample6" >>>>> # Associate samples with (randomly chosen) groups >>>>> anno <- data.frame(sampleID=sampleNames(raw1), Group=rep(c("A", "B", >>>> "C", "D"), times=c(4,24,5,15))) >>>>> head(anno) >>>> sampleID Group >>>> 1 Sample1 A >>>> 2 Sample2 A >>>> 3 Sample3 A >>>> 4 Sample4 A >>>> 5 Sample5 B >>>> 6 Sample6 B >>>>> # Plot the first gene - for each sample individually >>>>> plotCtOverview(raw1, genes=featureNames(raw1)[1], legend=FALSE, >>>> col=1:nrow(anno)) >>>>> # Plot the first gene - for each group >>>>> plotCtOverview(raw1, genes=featureNames(raw1)[1], group=anno$Group, >>>> legend=FALSE, col=1:length(unique(anno$Group))) >>>>> # Plot the first gene, using group "A" as a control >>>>> plotCtOverview(raw1, genes=featureNames(raw1)[1], group=anno$Group, >>>> legend=FALSE, col=1:length(unique(anno$Group)), calibrator="A") >>>> >>>> >>>> >>>>> You recommend below using a vector, but I dont see how that helps me >>>>> associate the samples in the Expression set. >>>>> >>>>> thanks again! >>>>> >>>>> s >>>>> >>>>> On Jun 26, 2012, at 12:48 PM, Heidi Dvinge wrote: >>>>> >>>>>>> Hi, >>>>>>> I'm having some troubles selectively sub-setting, and graphing up >>>>>>> QPCR >>>>>>> data within HTqPCR for Biomark plates (both 48.48 and 96.96 >>>>>>> plates). >>>>>>> I'd >>>>>>> like to be able to visualize specific genes, with specific groups >>>>>>> we >>>>>>> run >>>>>>> routinely on our Biomark system. Typical runs are across multiple >>>>>>> plates, >>>>>>> and have multiple biological replicates, and usually 2 or more >>>>>>> technical >>>>>>> replicates (although we are moving away from technical reps, as the >>>>>>> CVs >>>>>>> are so tight). >>>>>>> >>>>>>> Can anyone help with this? Heidi? >>>>>>> >>>>>>> raw6=readCtData(files="Chip6.csv", format="BioMark", n.features=48, >>>>>>> n.data=48, samples=samples) >>>>>>> #Ive read the samples in from a separate file, as when you read it >>>>>>> in, >>>>>>> it >>>>>>> doesnt take the sample names supplied in the biomark output# >>>>>>> #Next, I want to plot genes of interest, with samples of interest, >>>>>>> and >>>>>>> I'm >>>>>>> having trouble getting an appropriate output# >>>>>>> >>>>>>> g=featureNames(raw6)[1:2] >>>>>>> plotCtOverview(raw6, genes=g, groups=groupID$Treatment, >>>>>>> col=rainbow(5)) >>>>>>> >>>>>>> #This plots 1 gene across all 48 samples# >>>>>>> #but the legend doesnt behave, its placed on top of the plot, and I >>>>>>> cant >>>>>>> get it to display in a non-overlapping fashion# >>>>>>> #I've tried all sorts of things in par, but nothing seems to shift >>>>>>> the >>>>>>> legend's position# >>>>>>> >>>>>> As the old saying goes, whenever you want a job done well, you'll >>>>>> have >>>>>> to >>>>>> do it yourself ;). In this case, the easiest thing is probably to >>>>>> use >>>>>> legend=FALSE in plotCtOverview, and then afterwards add it yourself >>>>>> in >>>>>> the >>>>>> desired location using legend(). That way, if you have a lot of >>>>>> different >>>>>> features or groups to display, you can also use the ncol parameter >>>>>> in >>>>>> legend to make several columns within the legend, such as 3x4 >>>>>> instead >>>>>> of >>>>>> the default 12x1. >>>>>> >>>>>> Alternatively, you can use either xlim or ylim in plotCtOverview to >>>>>> add >>>>>> some empty space on the side where there's then room for the legend. >>>>>> >>>>>>> #I now want to plot a subset of the samples for specific genes# >>>>>>>> LOY=subset(groupID,groupID$Treatment=="LO" | groupID$Treatment== >>>>>>>> "LFY") >>>>>>>> LOY >>>>>>> Sample Treatment >>>>>>> 2 L20 LFY >>>>>>> 5 L30 LFY >>>>>>> 7 L45 LO >>>>>>> 20 L40 LO >>>>>>> 27 L43 LO >>>>>>> 33 L29 LFY >>>>>>> 36 L38 LO >>>>>>> 40 L39 LO >>>>>>> 43 L23 LFY >>>>>>> >>>>>>> >>>>>>>> plotCtOverview(raw6, genes=g, groups=LOY, col=rainbow(5)) >>>>>>> Warning messages: >>>>>>> 1: In split.default(t(x), sample.split) : >>>>>>> data length is not a multiple of split variable >>>>>>> 2: In qt(p, df, lower.tail, log.p) : NaNs produced >>>>>>>> >>>>>> >>>>>> Does it make sense if you split by groups=LOY$Treatment? It looks >>>>>> like >>>>>> the >>>>>> object LOY itself is a data frame, rather than the expected vector. >>>>>> >>>>>> Also, you may have to 'repeat' the col=rainbow() argument to fit >>>>>> your >>>>>> number of features. >>>>>> >>>>>>> >>>>>>> #it displays the two groups defined by treatment, but doesnt do so >>>>>>> nicely, >>>>>>> very skinny bars, and the legend doesnt reflect what its >>>>>>> displaying# >>>>>>> #again, I've tried monkeying around with par, but not sure what >>>>>>> HTqPCR >>>>>>> is >>>>>>> calling to make the plots# >>>>>>> >>>>>> If the bars are very skinny, it's probably because you're displaying >>>>>> a >>>>>> lot >>>>>> of features. Nothing much to do about that, except increasing the >>>>>> width >>>>>> or >>>>>> your plot :(. >>>>>> >>>>>> \Heidi >>>>>> >>>>>>> please help! >>>>>>> >>>>>>> thanks >>>>>>> >>>>>>> Simon. >>>>>>> >>>>>>> _______________________________________________ >>>>>>> Bioconductor mailing list >>>>>>> Bioconductor at r-project.org >>>>>>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>>>>>> Search the archives: >>>>>>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>>>>>> >>>>>> >>>>>> >>>>> >>>>> >>>> >>>> >>> >>> >> >> > >
ADD REPLYlink written 7.1 years ago by Heidi Dvinge2.0k
Great, thanks Heidi. This did the job. Quick question, can you make scatterplots instead of barcharts for any of the plot functions? I've looked at plotCtOverview, and I dont see a way to specify the chart type. Its helpful to have some flexibility here, so you can see how tight the spread is (personal preference I know, as you allow CI to be displayed, I just prefer to see the actual data) thanks s On Jul 6, 2012, at 1:07 AM, Heidi Dvinge wrote: > Hi Simon, > >> Hi Heidi, >> I've a followup from the question from listed below. I've started to merge >> multiple plates together using rbind as you suggested. I've identical gene >> order per plate, but different biological samples belonging to different >> groups per plate. I can manipulate these using your graphing functions on >> a per plate basis, but I'm unclear how to address comparisons with regards >> to groups once the plates are merged. >> >> For example, I have 5 text files which tie sample ID to specific groups of >> interest. After importing and merging the 5 separate Biomark files by >> rbind, my merged object is 48x240. How do I then contrast the different >> groups? I'm clear on how to do this in a single plate (using one of the 5 >> individual text files), but confused with regards to the process after >> merging. Should I have a vector with 240 identifiers for the merged files >> which is in the same order the merged plates are? (top to bottom (row), >> and left to right? (by column). >> > Yes (presuming I've understood your description correct). You can either > also just combine your individual text files with samples descriptions, or > create a whole new object describing each of your samples, as long as the > order of your descriptions are the same as the columns in your qPCRset > object. > > Alternatively, please note that as of version 1.9.0 (AFAIR) qPCRsets now > inherit from class ExpressionSet, i.e. they have a slot called phenoData > (an AnnotatedDataFrame) similar to what objects for microarray analysis > have. E.g. > >> data(qPCRraw) >> phenoData(qPCRraw) > An object of class "AnnotatedDataFrame" > sampleNames: sample1 sample2 ... sample6 (6 total) > varLabels: sample > varMetadata: labelDescription >> pData(qPCRraw) > sample > sample1 1 > sample2 2 > sample3 3 > sample4 4 > sample5 5 > sample6 6 >> pData(qPCRraw)$Genotype <- c("A", "B", "A", "A", "B", "B") >> pData(qPCRraw) > sample Genotype > sample1 1 A > sample2 2 B > sample3 3 A > sample4 4 A > sample5 5 B > sample6 6 B > > You can use this to store information about samples within the qPCRset > object itself, rather than in 'external' objects. There's more info about > this in the general help files for: > > ?AnnotatedDataFrame > ?ExpressionSet > > \Heidi > > >> thanks >> >> Simon >> On Jun 27, 2012, at 3:27 PM, Heidi Dvinge wrote: >> >>>> Hi Heidi, >>>> you are correct, yes 48.48. >>>> The example you provide below is exactly what I needed for >>>> clarification >>>> for groups. I was trying to reverse engineer what you had done with the >>>> original expression set package for microarrays, but from below, I can >>>> get >>>> this to work now. >>>> >>> Glad it works. Hopefully by the next BioConductor release I'll remember >>> to >>> clarify the plotCtOverview help file. >>> >>>> Just to be clear, I have 5 48.48 plates. Should I normalize each >>>> individually prior to combining, or should I reformat to a 2304x1 each, >>>> combine, then normalize (not sure if you can do that or not) >>>> >>> Hm, that's one of the questions I've also been asking myself, so I would >>> be curious to hear what your results from this are. >>> >>> If you suspect that there are some major factors influencing the 5 >>> plates >>> systematically, then normalising them in a 2304 x 5 object should >>> (hopefully) correct for that. For example, they may have been run on >>> different days, by different people, or perhaps there was a short power >>> cut during the processing of one of them. This might be visible if you >>> have for example a boxplot of Ct from all 48*5 samples, and you see >>> blocks >>> of them shifted up or down. >>> >>> Obviously, this doesn't take care of normalisation between your samples >>> within each plate though. If you suspect your samples to have some >>> systematic variation that you need to account for, then you can >>> normalise >>> each plate individually (as a 48 x 48) object. Alternatively, you can >>> try >>> to combine within- and between-sample normalisation by taking all 48 x >>> 240 >>> values at once. >>> >>> In principle, you can split, reformat and the recombine the data in >>> however many ways you like. Personally, with any sort of data I prefer >>> to >>> go with as little preprocessing as possible, since each additional step >>> can potentially introduce its own biases into the data. So unless there >>> are some obvious variation between your 5 plates, I'd probably stick >>> with >>> just normalisation between the samples, e.. using a 48 x 240 object. >>> >>> Of course, you may have different preferences, or find out that a >>> completely different approach is required for this particular data set. >>> >>> \Heidi >>> >>>> thanks again for your prompt responses! >>>> >>>> best >>>> >>>> s >>>> >>>> On Jun 27, 2012, at 2:26 PM, Heidi Dvinge wrote: >>>> >>>>> Hi Simon, >>>>> >>>>>> Thanks for the help Heidi, >>>>>> but I'm still having troubles, your comments on the plotting helped >>>>>> me >>>>>> solve the outputs. But if I want to just display some groups (for >>>>>> example >>>>>> the LO group in the example below), how do I associate a group with >>>>>> multiple samples (ie biological reps)? >>>>>> >>>>>> Currently I'm associating genes with samples by reading in the file >>>>>> as >>>>>> below >>>>>> plate6=read.delim("plate6Sample.txt", header=FALSE) >>>>>> #this is a file to associate sample ID with the genes in the biomark >>>>>> data, >>>>>> as currently HTqPCR does not seem to associate the sample info in the >>>>>> Biomark output to the gene IDs >>>>>> >>>>> Erm, no, it doesn't :-/ >>>>> >>>>>> samples=as.vector(t(plate6)) >>>>>> raw6=readCtData(files="Chip6.csv", format="BioMark", n.features=48, >>>>>> n.data=48, samples=samples) >>>>>> #now I have samples and genes similar to your example in the guide, >>>>>> but >>>>>> I >>>>>> want to associate samples to groups now. In the guide, you have an >>>>>> example >>>>>> where you have entire files as distinct samples, but in our runs, we >>>>>> never >>>>>> have that situation. I have a file which associates samples to >>>>>> groups, >>>>>> which I read in... >>>>>> >>>>>> groupID=read.csv("plate6key.csv") >>>>>> >>>>>> but how do I associate the samples with their appropriate groups for >>>>>> biological replicates with any of the functions in HtQPCR? >>>>> >>>>> I'm afraid I'm slightly confused here (sorry, long day). Exactly how >>>>> is >>>>> your data formatted? I.e. are the columns either individual samples, >>>>> or >>>>> from files containing multiple samples? So for example for a single >>>>> 48.48 >>>>> arrays, is your qPCRset object 2304 x 1 or 48 x 48? >>>>> >>>>> From your readCtData command I'm guessing you have 48 x 48, i.e. all >>>>> 48 >>>>> samples from your 1 array are in columns. In that case the 'groups' >>>>> parameter in plotCtOverview will need to be a vector of length 48, >>>>> indicating how you want the 48 columns in your qPCRset object to be >>>>> grouped together. >>>>> >>>>> Below is an example of (admittedly ugly) plots. I don't know if that's >>>>> similar to your situation at all. >>>>> >>>>> \Heidi >>>>> >>>>>> # Reading in data >>>>>> exPath <- system.file("exData", package = "HTqPCR") >>>>>> raw1 <- readCtData(files = "BioMark_sample.csv", path = exPath, >>>>>> format >>>>>> = >>>>> "BioMark", n.features = 48, n.data = 48) >>>>>> # Check sample names >>>>>> head(sampleNames(raw1)) >>>>> [1] "Sample1" "Sample2" "Sample3" "Sample4" "Sample5" "Sample6" >>>>>> # Associate samples with (randomly chosen) groups >>>>>> anno <- data.frame(sampleID=sampleNames(raw1), Group=rep(c("A", "B", >>>>> "C", "D"), times=c(4,24,5,15))) >>>>>> head(anno) >>>>> sampleID Group >>>>> 1 Sample1 A >>>>> 2 Sample2 A >>>>> 3 Sample3 A >>>>> 4 Sample4 A >>>>> 5 Sample5 B >>>>> 6 Sample6 B >>>>>> # Plot the first gene - for each sample individually >>>>>> plotCtOverview(raw1, genes=featureNames(raw1)[1], legend=FALSE, >>>>> col=1:nrow(anno)) >>>>>> # Plot the first gene - for each group >>>>>> plotCtOverview(raw1, genes=featureNames(raw1)[1], group=anno$Group, >>>>> legend=FALSE, col=1:length(unique(anno$Group))) >>>>>> # Plot the first gene, using group "A" as a control >>>>>> plotCtOverview(raw1, genes=featureNames(raw1)[1], group=anno$Group, >>>>> legend=FALSE, col=1:length(unique(anno$Group)), calibrator="A") >>>>> >>>>> >>>>> >>>>>> You recommend below using a vector, but I dont see how that helps me >>>>>> associate the samples in the Expression set. >>>>>> >>>>>> thanks again! >>>>>> >>>>>> s >>>>>> >>>>>> On Jun 26, 2012, at 12:48 PM, Heidi Dvinge wrote: >>>>>> >>>>>>>> Hi, >>>>>>>> I'm having some troubles selectively sub-setting, and graphing up >>>>>>>> QPCR >>>>>>>> data within HTqPCR for Biomark plates (both 48.48 and 96.96 >>>>>>>> plates). >>>>>>>> I'd >>>>>>>> like to be able to visualize specific genes, with specific groups >>>>>>>> we >>>>>>>> run >>>>>>>> routinely on our Biomark system. Typical runs are across multiple >>>>>>>> plates, >>>>>>>> and have multiple biological replicates, and usually 2 or more >>>>>>>> technical >>>>>>>> replicates (although we are moving away from technical reps, as the >>>>>>>> CVs >>>>>>>> are so tight). >>>>>>>> >>>>>>>> Can anyone help with this? Heidi? >>>>>>>> >>>>>>>> raw6=readCtData(files="Chip6.csv", format="BioMark", n.features=48, >>>>>>>> n.data=48, samples=samples) >>>>>>>> #Ive read the samples in from a separate file, as when you read it >>>>>>>> in, >>>>>>>> it >>>>>>>> doesnt take the sample names supplied in the biomark output# >>>>>>>> #Next, I want to plot genes of interest, with samples of interest, >>>>>>>> and >>>>>>>> I'm >>>>>>>> having trouble getting an appropriate output# >>>>>>>> >>>>>>>> g=featureNames(raw6)[1:2] >>>>>>>> plotCtOverview(raw6, genes=g, groups=groupID$Treatment, >>>>>>>> col=rainbow(5)) >>>>>>>> >>>>>>>> #This plots 1 gene across all 48 samples# >>>>>>>> #but the legend doesnt behave, its placed on top of the plot, and I >>>>>>>> cant >>>>>>>> get it to display in a non-overlapping fashion# >>>>>>>> #I've tried all sorts of things in par, but nothing seems to shift >>>>>>>> the >>>>>>>> legend's position# >>>>>>>> >>>>>>> As the old saying goes, whenever you want a job done well, you'll >>>>>>> have >>>>>>> to >>>>>>> do it yourself ;). In this case, the easiest thing is probably to >>>>>>> use >>>>>>> legend=FALSE in plotCtOverview, and then afterwards add it yourself >>>>>>> in >>>>>>> the >>>>>>> desired location using legend(). That way, if you have a lot of >>>>>>> different >>>>>>> features or groups to display, you can also use the ncol parameter >>>>>>> in >>>>>>> legend to make several columns within the legend, such as 3x4 >>>>>>> instead >>>>>>> of >>>>>>> the default 12x1. >>>>>>> >>>>>>> Alternatively, you can use either xlim or ylim in plotCtOverview to >>>>>>> add >>>>>>> some empty space on the side where there's then room for the legend. >>>>>>> >>>>>>>> #I now want to plot a subset of the samples for specific genes# >>>>>>>>> LOY=subset(groupID,groupID$Treatment=="LO" | groupID$Treatment== >>>>>>>>> "LFY") >>>>>>>>> LOY >>>>>>>> Sample Treatment >>>>>>>> 2 L20 LFY >>>>>>>> 5 L30 LFY >>>>>>>> 7 L45 LO >>>>>>>> 20 L40 LO >>>>>>>> 27 L43 LO >>>>>>>> 33 L29 LFY >>>>>>>> 36 L38 LO >>>>>>>> 40 L39 LO >>>>>>>> 43 L23 LFY >>>>>>>> >>>>>>>> >>>>>>>>> plotCtOverview(raw6, genes=g, groups=LOY, col=rainbow(5)) >>>>>>>> Warning messages: >>>>>>>> 1: In split.default(t(x), sample.split) : >>>>>>>> data length is not a multiple of split variable >>>>>>>> 2: In qt(p, df, lower.tail, log.p) : NaNs produced >>>>>>>>> >>>>>>> >>>>>>> Does it make sense if you split by groups=LOY$Treatment? It looks >>>>>>> like >>>>>>> the >>>>>>> object LOY itself is a data frame, rather than the expected vector. >>>>>>> >>>>>>> Also, you may have to 'repeat' the col=rainbow() argument to fit >>>>>>> your >>>>>>> number of features. >>>>>>> >>>>>>>> >>>>>>>> #it displays the two groups defined by treatment, but doesnt do so >>>>>>>> nicely, >>>>>>>> very skinny bars, and the legend doesnt reflect what its >>>>>>>> displaying# >>>>>>>> #again, I've tried monkeying around with par, but not sure what >>>>>>>> HTqPCR >>>>>>>> is >>>>>>>> calling to make the plots# >>>>>>>> >>>>>>> If the bars are very skinny, it's probably because you're displaying >>>>>>> a >>>>>>> lot >>>>>>> of features. Nothing much to do about that, except increasing the >>>>>>> width >>>>>>> or >>>>>>> your plot :(. >>>>>>> >>>>>>> \Heidi >>>>>>> >>>>>>>> please help! >>>>>>>> >>>>>>>> thanks >>>>>>>> >>>>>>>> Simon. >>>>>>>> >>>>>>>> _______________________________________________ >>>>>>>> Bioconductor mailing list >>>>>>>> Bioconductor at r-project.org >>>>>>>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>>>>>>> Search the archives: >>>>>>>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>>>>>>> >>>>>>> >>>>>>> >>>>>> >>>>>> >>>>> >>>>> >>>> >>>> >>> >>> >> >> > >
ADD REPLYlink written 7.1 years ago by Simon Melov340
Hi Simon, > Great, > thanks Heidi. This did the job. Quick question, can you make scatterplots > instead of barcharts for any of the plot functions? I've looked at > plotCtOverview, and I dont see a way to specify the chart type. Its > helpful to have some flexibility here, so you can see how tight the spread > is (personal preference I know, as you allow CI to be displayed, I just > prefer to see the actual data) > All the functions available in HTqPCR are listed under ls("package:HTqPCR"). For plotCtOverview there isn't any option for making scatterplots instead of barplots (not sure how this would look?). However, you can of course add the actual Ct values as points on the plot, by using the points() function after calling plotCtOverview. In general, if you find that the plotting functions in HTqPCR differ too much from the kind of figure you want, then rather than trying to modify the plots extensively, it's probably easier to just construct your own plots from scratch. You can always extract the actual Ct values using either exprs() or getCt(), and unlike some microarray or sequencing data, qPCR data sets are quite small and easy to work with 'manually'. The plots in HTqPCR are all based on simple plotting functions from the core graphics package, i.e. plot(), barplot(), boxplot(). Best \Heidi > thanks > > s > On Jul 6, 2012, at 1:07 AM, Heidi Dvinge wrote: > >> Hi Simon, >> >>> Hi Heidi, >>> I've a followup from the question from listed below. I've started to >>> merge >>> multiple plates together using rbind as you suggested. I've identical >>> gene >>> order per plate, but different biological samples belonging to >>> different >>> groups per plate. I can manipulate these using your graphing functions >>> on >>> a per plate basis, but I'm unclear how to address comparisons with >>> regards >>> to groups once the plates are merged. >>> >>> For example, I have 5 text files which tie sample ID to specific groups >>> of >>> interest. After importing and merging the 5 separate Biomark files by >>> rbind, my merged object is 48x240. How do I then contrast the different >>> groups? I'm clear on how to do this in a single plate (using one of the >>> 5 >>> individual text files), but confused with regards to the process after >>> merging. Should I have a vector with 240 identifiers for the merged >>> files >>> which is in the same order the merged plates are? (top to bottom (row), >>> and left to right? (by column). >>> >> Yes (presuming I've understood your description correct). You can either >> also just combine your individual text files with samples descriptions, >> or >> create a whole new object describing each of your samples, as long as >> the >> order of your descriptions are the same as the columns in your qPCRset >> object. >> >> Alternatively, please note that as of version 1.9.0 (AFAIR) qPCRsets now >> inherit from class ExpressionSet, i.e. they have a slot called phenoData >> (an AnnotatedDataFrame) similar to what objects for microarray analysis >> have. E.g. >> >>> data(qPCRraw) >>> phenoData(qPCRraw) >> An object of class "AnnotatedDataFrame" >> sampleNames: sample1 sample2 ... sample6 (6 total) >> varLabels: sample >> varMetadata: labelDescription >>> pData(qPCRraw) >> sample >> sample1 1 >> sample2 2 >> sample3 3 >> sample4 4 >> sample5 5 >> sample6 6 >>> pData(qPCRraw)$Genotype <- c("A", "B", "A", "A", "B", "B") >>> pData(qPCRraw) >> sample Genotype >> sample1 1 A >> sample2 2 B >> sample3 3 A >> sample4 4 A >> sample5 5 B >> sample6 6 B >> >> You can use this to store information about samples within the qPCRset >> object itself, rather than in 'external' objects. There's more info >> about >> this in the general help files for: >> >> ?AnnotatedDataFrame >> ?ExpressionSet >> >> \Heidi >> >> >>> thanks >>> >>> Simon >>> On Jun 27, 2012, at 3:27 PM, Heidi Dvinge wrote: >>> >>>>> Hi Heidi, >>>>> you are correct, yes 48.48. >>>>> The example you provide below is exactly what I needed for >>>>> clarification >>>>> for groups. I was trying to reverse engineer what you had done with >>>>> the >>>>> original expression set package for microarrays, but from below, I >>>>> can >>>>> get >>>>> this to work now. >>>>> >>>> Glad it works. Hopefully by the next BioConductor release I'll >>>> remember >>>> to >>>> clarify the plotCtOverview help file. >>>> >>>>> Just to be clear, I have 5 48.48 plates. Should I normalize each >>>>> individually prior to combining, or should I reformat to a 2304x1 >>>>> each, >>>>> combine, then normalize (not sure if you can do that or not) >>>>> >>>> Hm, that's one of the questions I've also been asking myself, so I >>>> would >>>> be curious to hear what your results from this are. >>>> >>>> If you suspect that there are some major factors influencing the 5 >>>> plates >>>> systematically, then normalising them in a 2304 x 5 object should >>>> (hopefully) correct for that. For example, they may have been run on >>>> different days, by different people, or perhaps there was a short >>>> power >>>> cut during the processing of one of them. This might be visible if you >>>> have for example a boxplot of Ct from all 48*5 samples, and you see >>>> blocks >>>> of them shifted up or down. >>>> >>>> Obviously, this doesn't take care of normalisation between your >>>> samples >>>> within each plate though. If you suspect your samples to have some >>>> systematic variation that you need to account for, then you can >>>> normalise >>>> each plate individually (as a 48 x 48) object. Alternatively, you can >>>> try >>>> to combine within- and between-sample normalisation by taking all 48 x >>>> 240 >>>> values at once. >>>> >>>> In principle, you can split, reformat and the recombine the data in >>>> however many ways you like. Personally, with any sort of data I prefer >>>> to >>>> go with as little preprocessing as possible, since each additional >>>> step >>>> can potentially introduce its own biases into the data. So unless >>>> there >>>> are some obvious variation between your 5 plates, I'd probably stick >>>> with >>>> just normalisation between the samples, e.. using a 48 x 240 object. >>>> >>>> Of course, you may have different preferences, or find out that a >>>> completely different approach is required for this particular data >>>> set. >>>> >>>> \Heidi >>>> >>>>> thanks again for your prompt responses! >>>>> >>>>> best >>>>> >>>>> s >>>>> >>>>> On Jun 27, 2012, at 2:26 PM, Heidi Dvinge wrote: >>>>> >>>>>> Hi Simon, >>>>>> >>>>>>> Thanks for the help Heidi, >>>>>>> but I'm still having troubles, your comments on the plotting helped >>>>>>> me >>>>>>> solve the outputs. But if I want to just display some groups (for >>>>>>> example >>>>>>> the LO group in the example below), how do I associate a group with >>>>>>> multiple samples (ie biological reps)? >>>>>>> >>>>>>> Currently I'm associating genes with samples by reading in the >>>>>>> file >>>>>>> as >>>>>>> below >>>>>>> plate6=read.delim("plate6Sample.txt", header=FALSE) >>>>>>> #this is a file to associate sample ID with the genes in the >>>>>>> biomark >>>>>>> data, >>>>>>> as currently HTqPCR does not seem to associate the sample info in >>>>>>> the >>>>>>> Biomark output to the gene IDs >>>>>>> >>>>>> Erm, no, it doesn't :-/ >>>>>> >>>>>>> samples=as.vector(t(plate6)) >>>>>>> raw6=readCtData(files="Chip6.csv", format="BioMark", n.features=48, >>>>>>> n.data=48, samples=samples) >>>>>>> #now I have samples and genes similar to your example in the guide, >>>>>>> but >>>>>>> I >>>>>>> want to associate samples to groups now. In the guide, you have an >>>>>>> example >>>>>>> where you have entire files as distinct samples, but in our runs, >>>>>>> we >>>>>>> never >>>>>>> have that situation. I have a file which associates samples to >>>>>>> groups, >>>>>>> which I read in... >>>>>>> >>>>>>> groupID=read.csv("plate6key.csv") >>>>>>> >>>>>>> but how do I associate the samples with their appropriate groups >>>>>>> for >>>>>>> biological replicates with any of the functions in HtQPCR? >>>>>> >>>>>> I'm afraid I'm slightly confused here (sorry, long day). Exactly how >>>>>> is >>>>>> your data formatted? I.e. are the columns either individual samples, >>>>>> or >>>>>> from files containing multiple samples? So for example for a single >>>>>> 48.48 >>>>>> arrays, is your qPCRset object 2304 x 1 or 48 x 48? >>>>>> >>>>>> From your readCtData command I'm guessing you have 48 x 48, i.e. all >>>>>> 48 >>>>>> samples from your 1 array are in columns. In that case the 'groups' >>>>>> parameter in plotCtOverview will need to be a vector of length 48, >>>>>> indicating how you want the 48 columns in your qPCRset object to be >>>>>> grouped together. >>>>>> >>>>>> Below is an example of (admittedly ugly) plots. I don't know if >>>>>> that's >>>>>> similar to your situation at all. >>>>>> >>>>>> \Heidi >>>>>> >>>>>>> # Reading in data >>>>>>> exPath <- system.file("exData", package = "HTqPCR") >>>>>>> raw1 <- readCtData(files = "BioMark_sample.csv", path = exPath, >>>>>>> format >>>>>>> = >>>>>> "BioMark", n.features = 48, n.data = 48) >>>>>>> # Check sample names >>>>>>> head(sampleNames(raw1)) >>>>>> [1] "Sample1" "Sample2" "Sample3" "Sample4" "Sample5" "Sample6" >>>>>>> # Associate samples with (randomly chosen) groups >>>>>>> anno <- data.frame(sampleID=sampleNames(raw1), >>>>>>> Group=rep(c("A", "B", >>>>>> "C", "D"), times=c(4,24,5,15))) >>>>>>> head(anno) >>>>>> sampleID Group >>>>>> 1 Sample1 A >>>>>> 2 Sample2 A >>>>>> 3 Sample3 A >>>>>> 4 Sample4 A >>>>>> 5 Sample5 B >>>>>> 6 Sample6 B >>>>>>> # Plot the first gene - for each sample individually >>>>>>> plotCtOverview(raw1, genes=featureNames(raw1)[1], legend=FALSE, >>>>>> col=1:nrow(anno)) >>>>>>> # Plot the first gene - for each group >>>>>>> plotCtOverview(raw1, genes=featureNames(raw1)[1], group=anno$Group, >>>>>> legend=FALSE, col=1:length(unique(anno$Group))) >>>>>>> # Plot the first gene, using group "A" as a control >>>>>>> plotCtOverview(raw1, genes=featureNames(raw1)[1], group=anno$Group, >>>>>> legend=FALSE, col=1:length(unique(anno$Group)), calibrator="A") >>>>>> >>>>>> >>>>>> >>>>>>> You recommend below using a vector, but I dont see how that helps >>>>>>> me >>>>>>> associate the samples in the Expression set. >>>>>>> >>>>>>> thanks again! >>>>>>> >>>>>>> s >>>>>>> >>>>>>> On Jun 26, 2012, at 12:48 PM, Heidi Dvinge wrote: >>>>>>> >>>>>>>>> Hi, >>>>>>>>> I'm having some troubles selectively sub-setting, and graphing up >>>>>>>>> QPCR >>>>>>>>> data within HTqPCR for Biomark plates (both 48.48 and 96.96 >>>>>>>>> plates). >>>>>>>>> I'd >>>>>>>>> like to be able to visualize specific genes, with specific groups >>>>>>>>> we >>>>>>>>> run >>>>>>>>> routinely on our Biomark system. Typical runs are across multiple >>>>>>>>> plates, >>>>>>>>> and have multiple biological replicates, and usually 2 or more >>>>>>>>> technical >>>>>>>>> replicates (although we are moving away from technical reps, as >>>>>>>>> the >>>>>>>>> CVs >>>>>>>>> are so tight). >>>>>>>>> >>>>>>>>> Can anyone help with this? Heidi? >>>>>>>>> >>>>>>>>> raw6=readCtData(files="Chip6.csv", format="BioMark", >>>>>>>>> n.features=48, >>>>>>>>> n.data=48, samples=samples) >>>>>>>>> #Ive read the samples in from a separate file, as when you read >>>>>>>>> it >>>>>>>>> in, >>>>>>>>> it >>>>>>>>> doesnt take the sample names supplied in the biomark output# >>>>>>>>> #Next, I want to plot genes of interest, with samples of >>>>>>>>> interest, >>>>>>>>> and >>>>>>>>> I'm >>>>>>>>> having trouble getting an appropriate output# >>>>>>>>> >>>>>>>>> g=featureNames(raw6)[1:2] >>>>>>>>> plotCtOverview(raw6, genes=g, groups=groupID$Treatment, >>>>>>>>> col=rainbow(5)) >>>>>>>>> >>>>>>>>> #This plots 1 gene across all 48 samples# >>>>>>>>> #but the legend doesnt behave, its placed on top of the plot, and >>>>>>>>> I >>>>>>>>> cant >>>>>>>>> get it to display in a non-overlapping fashion# >>>>>>>>> #I've tried all sorts of things in par, but nothing seems to >>>>>>>>> shift >>>>>>>>> the >>>>>>>>> legend's position# >>>>>>>>> >>>>>>>> As the old saying goes, whenever you want a job done well, you'll >>>>>>>> have >>>>>>>> to >>>>>>>> do it yourself ;). In this case, the easiest thing is probably to >>>>>>>> use >>>>>>>> legend=FALSE in plotCtOverview, and then afterwards add it >>>>>>>> yourself >>>>>>>> in >>>>>>>> the >>>>>>>> desired location using legend(). That way, if you have a lot of >>>>>>>> different >>>>>>>> features or groups to display, you can also use the ncol parameter >>>>>>>> in >>>>>>>> legend to make several columns within the legend, such as 3x4 >>>>>>>> instead >>>>>>>> of >>>>>>>> the default 12x1. >>>>>>>> >>>>>>>> Alternatively, you can use either xlim or ylim in plotCtOverview >>>>>>>> to >>>>>>>> add >>>>>>>> some empty space on the side where there's then room for the >>>>>>>> legend. >>>>>>>> >>>>>>>>> #I now want to plot a subset of the samples for specific genes# >>>>>>>>>> LOY=subset(groupID,groupID$Treatment=="LO" | groupID$Treatment== >>>>>>>>>> "LFY") >>>>>>>>>> LOY >>>>>>>>> Sample Treatment >>>>>>>>> 2 L20 LFY >>>>>>>>> 5 L30 LFY >>>>>>>>> 7 L45 LO >>>>>>>>> 20 L40 LO >>>>>>>>> 27 L43 LO >>>>>>>>> 33 L29 LFY >>>>>>>>> 36 L38 LO >>>>>>>>> 40 L39 LO >>>>>>>>> 43 L23 LFY >>>>>>>>> >>>>>>>>> >>>>>>>>>> plotCtOverview(raw6, genes=g, groups=LOY, col=rainbow(5)) >>>>>>>>> Warning messages: >>>>>>>>> 1: In split.default(t(x), sample.split) : >>>>>>>>> data length is not a multiple of split variable >>>>>>>>> 2: In qt(p, df, lower.tail, log.p) : NaNs produced >>>>>>>>>> >>>>>>>> >>>>>>>> Does it make sense if you split by groups=LOY$Treatment? It looks >>>>>>>> like >>>>>>>> the >>>>>>>> object LOY itself is a data frame, rather than the expected >>>>>>>> vector. >>>>>>>> >>>>>>>> Also, you may have to 'repeat' the col=rainbow() argument to fit >>>>>>>> your >>>>>>>> number of features. >>>>>>>> >>>>>>>>> >>>>>>>>> #it displays the two groups defined by treatment, but doesnt do >>>>>>>>> so >>>>>>>>> nicely, >>>>>>>>> very skinny bars, and the legend doesnt reflect what its >>>>>>>>> displaying# >>>>>>>>> #again, I've tried monkeying around with par, but not sure what >>>>>>>>> HTqPCR >>>>>>>>> is >>>>>>>>> calling to make the plots# >>>>>>>>> >>>>>>>> If the bars are very skinny, it's probably because you're >>>>>>>> displaying >>>>>>>> a >>>>>>>> lot >>>>>>>> of features. Nothing much to do about that, except increasing the >>>>>>>> width >>>>>>>> or >>>>>>>> your plot :(. >>>>>>>> >>>>>>>> \Heidi >>>>>>>> >>>>>>>>> please help! >>>>>>>>> >>>>>>>>> thanks >>>>>>>>> >>>>>>>>> Simon. >>>>>>>>> >>>>>>>>> _______________________________________________ >>>>>>>>> Bioconductor mailing list >>>>>>>>> Bioconductor at r-project.org >>>>>>>>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>>>>>>>> Search the archives: >>>>>>>>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>>>>>>>> >>>>>>>> >>>>>>>> >>>>>>> >>>>>>> >>>>>> >>>>>> >>>>> >>>>> >>>> >>>> >>> >>> >> >> > >
ADD REPLYlink written 7.1 years ago by Heidi Dvinge2.0k
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