Hi Nishant, xyplot( `SSC-H`~`FL1-H`| factor(name, levels = unique(name)), > data=fs.trans[1:5]) Worked like a charm, thanks. And no error message any more for the polygon. Cheers, Anja On Wed, 2009-06-03 at 16:22 -0700, Nishant Gopalakrishnan wrote: > Hi Anja > > data=fs.trans[1:5])) > xyplot( `SSC-H`~`FL1-H`, data=fs.trans[1:5]) obviously works, but again > the shingles are not in the right order. > > To get the xyplots in the right order , you could try > xyplot( `SSC-H`~`FL1-H`| factor(name, levels = unique(name), > data=fs.trans[1:5]) > > So obviously the object 'name' is not present. But I thought I had > defined it with > read.flowSet(name.keyword = 'SAMPLE ID', phenoData = list(name = 'SAMPLE > ID', Filename = '$FIL'))? > > You can check if the names are correct using pData(fs.trans[1:5])) > > > And by the way, I created a polygon gate according to the example > in ?polygonGate with > > qrcut <- matrix(c(400,300,600,600,50,300,400,50),ncol=2,nrow=4) > > colnames(sqrcut) <- c("FSC-H","SSC-H") > > pg <- polygonGate(filterId="nonDebris", boundaries= sqrcut) > and do get a gate but also this error message Warning message: > The 'boundaries' argument is deprecated, please use '.gate' instead. > Where would I find the information on how to correctly define the > boundaries, does this have to do with glines? > > The boundaries argument has been deprecated and will be removed in the > next release. > I will update the example with the .gate argument as shown below > > pg <- polygonGate(filterId="nonDebris", .gate = sqrcut) > > > Nishant > > > Anja Schiel wrote: > > Dear Florian, dear Deepayan, > > > > Thanks a lot for all the help. I have run a few more experiments and > > looked at my data. > > I see the problem with the huge amount of values in the lower > > measurement margin and have decided to take your advice and use the > > densityplot function instead of trying to make individual histograms. > > Obviously I am rather reluctant to remove any data points from the raw > > data due to the reasons you mention, I can not tell for sure if they > > might not be relevant in one of the channels in advance. > > > > And I also realize that I would like to learn a lot more about the > > lattice, so I might endeavour into the depth of lattice during the > > summer! > > > > I have one other small issue. I have now used this to get the > > densityplot to place the plots in the order of the files in the folder > > > >> densityplot(factor(name, levels = unique(name)) ~`FL1-H`, data = > >> > > fs.trans[1:5]) > > Why can't I use this then to do the same with an xyplot? > > > >> xyplot(factor(name, levels = unique(name)), `SSC-H`~`FL1-H`, > >> > > data=fs.trans[1:5])) > > > >> xyplot( `SSC-H`~`FL1-H`, data=fs.trans[1:5]) obviously works, but again > >> > > the shingles are not in the right order. > > > > I get this error message: > > Error in typeof(x) : object 'name' not found > > Error in xyplot(factor(name, levels = unique(name)), `SSC-H` ~ > > `FL1-H`, : > > error in evaluating the argument 'x' in selecting a method for > > function 'xyplot' > > > > So obviously the object 'name' is not present. But I thought I had > > defined it with > > read.flowSet(name.keyword = 'SAMPLE ID', phenoData = list(name = 'SAMPLE > > ID', Filename = '$FIL'))? > > > > I assume I am missing something in respect to the difference in what > > densityplot does compared to xyplot. > > I have to admit that I can't quit follow the logic behind the > > # (factor(name, levels = unique(name)) > > to begin with, but this I should probably try to figure out myself. > > > > Maybe you could point me in the right direction? > > > > And by the way, I created a polygon gate according to the example > > in ?polygonGate with > > > >> sqrcut <- matrix(c(400,300,600,600,50,300,400,50),ncol=2,nrow=4) > >> colnames(sqrcut) <- c("FSC-H","SSC-H") > >> pg <- polygonGate(filterId="nonDebris", boundaries= sqrcut) > >> > > and do get a gate but also this error message > > Warning message: > > The 'boundaries' argument is deprecated, please use '.gate' instead. > > > > Where would I find the information on how to correctly define the > > boundaries, does this have to do with glines? > > > > Many thanks for your help, > > Anja > > > > On Thu, 2009-05-21 at 11:50 -0700, Florian Hahne wrote: > > > >> Anja Schiel wrote: > >> > >>> Dear Deepayan, > >>> > >>> Thanks a lot for your help! I very much appreciate that you take the > >>> time to help me. Bellow some comments and another question, if you have > >>> time..... > >>> > >>> On Wed, 2009-05-20 at 14:13 -0700, Deepayan Sarkar wrote: > >>> > >>> > >>>> On Wed, May 20, 2009 at 6:06 AM, Anja Schiel <a.e.schiel at="" medisin.uio.no=""> wrote: > >>>> > >>>> > >>>>> Hi, > >>>>> > >>>>> I am currently testing flowCore and flowViz and have encountered some > >>>>> problems. > >>>>> > >>>>> I am running : > >>>>> R version 2.9.0 (2009-04-17) > >>>>> i486-pc-linux-gnu > >>>>> > >>>>> attached base packages: > >>>>> [1] stats graphics grDevices utils datasets methods > >>>>> base > >>>>> > >>>>> other attached packages: > >>>>> [1] flowViz_1.8.0 lattice_0.17-25 flowCore_1.10.0 rrcov_0.5-01 > >>>>> [5] pcaPP_1.6 mvtnorm_0.9-5 robustbase_0.4-5 Biobase_2.4.1 > >>>>> > >>>>> loaded via a namespace (and not attached): > >>>>> [1] feature_1.2.3 graph_1.22.2 grid_2.9.0 > >>>>> KernSmooth_2.22-22 > >>>>> [5] ks_1.6.3 latticeExtra_0.5-4 MASS_7.2-47 > >>>>> RColorBrewer_1.0-2 > >>>>> [9] stats4_2.9.0 tools_2.9.0 > >>>>> > >>>>> > >>>>> I have noticed that when I use > >>>>> xyplot(`SSC-H` ~ `FSC-H`, data = fs.trans[[1]], filter = eGate) > >>>>> I get a plot with the Gate defined by eGate plotted, but when I try to > >>>>> do the same with > >>>>> flowPlot(fs.trans[[1]], filter = eGate) > >>>>> the gate is not drawn. Since the default settings seem to be filter = > >>>>> NULL (and I pass eGate to filter) and showFilter = TRUE I am wondering > >>>>> if this is a glinch in the system or if my command is wrong. > >>>>> > >>>>> > >>>> The 'flowPlot' function is not really maintained any more; the method > >>>> for "flowFrame" does have a 'filter' argument, but it is never used in > >>>> the actual function definition. xyplot() should be able to do > >>>> everything flowPlot does. If not, please let us know. > >>>> > >>>> > >>>> > >>>>> Second I am somewhat confused about the plot function. When I transform > >>>>> my FL-H signals with > >>>>> fs.trans <- transform('FL1-H' = asinh, 'FL2-H' = asinh) %on% fs > >>>>> and then run > >>>>> plot (fs.trans[[7]], 'FL1-H', breaks=256) > >>>>> I get a histogram with all my data crammed into the left corner due to > >>>>> the y-axis scale that seems to be extremely large. > >>>>> > >>>>> > >>>> The following seems to work for me: > >>>> > >>>> data(GvHD) > >>>> fs <- GvHD > >>>> fs.trans <- transform('FL1-H' = asinh, 'FL2-H' = asinh) %on% fs > >>>> plot(fs[[7]], "FL1-H") ## most of the data in left-most bin > >>>> plot(fs.trans[[7]], "FL1-H") ## much more spread out > >>>> > >>>> So we need a reproducible example to figure out why you are seeing > >>>> different behavior. > >>>> > >>>> > >>>> > >>> It seems that my original FACS data must be different than the one used > >>> for GvHD. I do see a relatively normal histogram with the GvHD data set, > >>> as you point out. But when I use my own files the y-axis is set to 30 > >>> 000 and not like in the example to 3000. > >>> > >>> By the way, the problem becomes more obvious if I actually set the > >>> breaks. If i set the breaks=256 then my graph gets extremely 'small', if > >>> I do the same with the GvHD the effect is not the same. > >>> > >>> I have attached 3 png files to show you what I get. > >>> > >>> I could sent you some of my original files (like one with no signal in > >>> FL1-H and one with a signal), but I am not sure if I can just attach > >>> those to an e-mail. Maybe I can sent you an zip archive? > >>> > >>> > >> Hi Anja, > >> maybe I can chip in here: > >> It seems that your data has tons of values on the lower measurement > >> margin, quite a common problem for flow data. The more sophisticated > >> plotting functions in flowViz (e.g. densityplot) try to ignore these > >> artefactual values, the simple histogram in the plot function does not. > >> You have a couple of options here: > >> 1.) remove those values before plotting. There is the boundaryFilter > >> function which should help you do that. Please note that margin events > >> might not be particularly informative in one channel, but they might > >> have perfectly fine values in others, so blindly removing them for other > >> purposes than visualization is usually not a good idea. > >> 2.) Play around with ylim once this is fixed. This however will only > >> clip the extremely large bin at around 0 and the picture might not be > >> particularly nice. > >> 3.) If you have multiple FCS files in a flowSet you could use the > >> densityplot function. As mentioned before, this should ignore the margin > >> events, although they are still indicated in the plot by little bars, as > >> far as I remember. I guess there should be a densityplot method for > >> flowFrames as well, and I will talk to Deepayan to add this for the next > >> release. > >> > >>> > >>> > >>>>> Also the axis changes > >>>>> between the files. I have tried to figure out how this function works > >>>>> (checked the normal and lattice information), but I am clearly not > >>>>> understanding what is the underlying set of data points that determines > >>>>> the y-axis scale. I would like to know how to reduce the y-axis scale > >>>>> and keep it constant between different files (at least if this is not > >>>>> something totally stupid to try). > >>>>> > >>>>> > >>>> I'm not sure what you mean. Different calls with different flow frames > >>>> will have different scales, based on the data for that frame. You > >>>> should be able to explicitly specify 'xlim' and 'ylim' to be the same > >>>> in all calls. This doesn't work now, and that's a bug. We will fix it > >>>> soon. > >>>> > >>>> > >>>> > >>> Well that is related to the problem above. I tried to pass xlim and ylim > >>> to the plot but nothing happened. I didn't get an error message either > >>> so I thought I was doing it wrong (but if I define xlim and ylim in > >>> xyplot it does work). So I was a bit confused. But if it is a bug then > >>> in theory what I tried was correct and once it is fixed it should work. > >>> In principal my idea was that I could just force the scale to be smaller > >>> on the plot to make my data look better. It might also be necessaryr if I > >>> need files for presentations or publications as it is usually expected > >>> that all axis are of the same scale. > >>> > >> That is a reason why I prefer density plots. They are already scaled in > >> a way, and having similar axes is much easier. Hard to do that on a > >> frequency histogram when your sample sizes differ a lot... > >> > >>> > >>> > >>> > >>> > >>>> You could always use densityplot() instead to compare multiple FCS files. > >>>> > >>>> densityplot(~`FL1-H` | names, data = fs.trans[1:5]) > >>>> > >>>> > >>>> > >>> And in fact this is probably a better way to combine several 'signals' > >>> in one plot to be honest. > >>> > >>> > >>> > >>>>> Third, I have created densityplots and noticed that the order of files > >>>>> is not like the order in the phenoData info. In phenoData the files are > >>>>> ordered according to their file-names (or more precise by the trailing > >>>>> numbers given by CellQuest), while they are plotted in some kind of > >>>>> alphabetical order in densityplot. Is it possible to pass an argument to > >>>>> densityplot that will plot the files in the file-names order? > >>>>> > >>>>> > >>>> Yes, that's the default for factor levels (see ?factor) when the file > >>>> names get converted to a factor. You can control the order by > >>>> specifying the levels explicitly. For example, compare: > >>>> > >>>> densityplot(factor(name, levels = rev(unique(name))) ~`FL1-H`, data = > >>>> fs.trans[1:5]) > >>>> > >>>> and > >>>> > >>>> densityplot(factor(name, levels = unique(name)) ~`FL1-H`, data = fs.trans[1:5]) > >>>> > >>>> > >>>> > >>> Exactly what I needed! Perfect. > >>> > >>> > >>> > >>>>> And is it also possible to have the plot in black and white and not in color? > >>>>> > >>>>> > >>>> Yes, e.g. > >>>> > >>>> densityplot( ~`FL1-H`, data = fs.trans[1:5], par.settings = > >>>> standard.theme(color = FALSE)) > >>>> > >>>> See ?trellis.device and ?flowViz.par.get for more details. > >>>> > >>>> > >>>> > >>> I admit that the higher plot functions are still a bit of a miracle to > >>> me but I think I start getting how to change some higher level > >>> functions. > >>> > >>> > >> If you are not afraid of a lattice overdose, I strongly recommend > >> Deepayan's Springer book... > >> > >>> > >>> > >>>>> I have also tried different gates and managed to create ellipsoid, > >>>>> rectangular and n2Filter, but failed to produce a polygon gate. Could > >>>>> anyone provide me with an simple example how to do that? > >>>>> > >>>>> > >>>> See the example in ?polygonGate. > >>>> > >>>> > >>>> > >>> Ok, I missed this example, my fault. I have now managed to get an > >>> polygon gate! > >>> > >>> > >>> > >>>>> And a last question, how could I produce a densityplot where I have an > >>>>> overaly instead of shingles for several files in one figure (such as is > >>>>> often used for publications, to show the shift from unstained, isotype > >>>>> control to specific staining). > >>>>> > >>>>> > >>>> Unfortunately that's not yet supported by the "flowSet" densityplot > >>>> method. You could however use the underlying lattice functions > >>>> directly to get what you want: > >>>> > >>>> tmpe <- > >>>> fsApply(fs.trans[1:6], > >>>> function(x) exprs(x)[, "FL1-H"], > >>>> simplify = FALSE) > >>>> > >>>> densityplot(~data, do.call(make.groups, tmpe), groups = which, > >>>> plot.points = FALSE, auto.key = list(columns = 3)) > >>>> > >>>> > >>>> > >>> This works nicely too. Thanks. > >>> > >>> > >>> > >>>> -Deepayan > >>>> > >>>> > >>> I have another question now. I was wondering if I can use four > >>> rectangular gates at the same time. This would be a bit like having > >>> quadrant statistics in other FACS software. I tried this by creating > >>> four gates with > >>> UL <- rectangleGate(filterId='UL', 'SSC-H' = c(400, 1000), 'FSC-H' = > >>> c(0,500)) > >>> LL <- rectangleGate(filterId='LL', 'SSC-H' = c(0, 400), 'FSC-H' = > >>> c(0,500)) > >>> UR <- rectangleGate(filterId='UR', 'SSC-H' = c(400, 1000), 'FSC-H' = > >>> c(500,1000)) > >>> LR <- rectangleGate(filterId='LR', 'SSC-H' = c(0, 400), 'FSC-H' = > >>> c(500,1000)) > >>> Now I can create subsets for each of these gates and get the percentages > >>> gated and create results with > >>> result_LL <- filter(fs.trans[1:4], LL) > >>> Percent.LL <- lapply(result_LL, summary) > >>> Percent.LL > >>> I have used FSC and SSC for this example but obviously this is something > >>> I eventually want to do with two fluorescent channels to identify double > >>> positive populations. > >>> > >>> I was wondering if I can now create a graph in which all 4 gates are > >>> plotted and the percentage in each gate is also plotted? > >>> > >>> > >> In this case you could directly use the quadGate class (see ? quadGate). > >> flowViz knows how to plot those. > >> data(GvHD) > >> foo <- GvHD[[1]] > >> qg <- quadGate("FSC-H"=500, "SSC-H"=400) > >> xyplot(`FSC-H` ~ `SSC-H`, foo, filter=qg) > >> > >> Adding additional gates to a plot is also possible using the glpolygon > >> or glpoints methods for trellis-type plots and the gpoints or glines > >> methods for base graphics plots. For the trellis plots you could either > >> adjust the panel function to deal with multiple gates and call glpolygon > >> in there, or you could use trellis.focus() to get to a particular panel > >> in your plot (by clicking on it, or you get it for free if there is only > >> one...) and now you can interactively add whatever you like. > >> trellis.unfocus() will get rid of the red boundary after you are done. > >> > >> xyplot(`FSC-H` ~ `SSC-H`, foo) > >> trellis.focus() > >> glpolygon(UL, gpar=list(gate=list(col="black", fill="red", alpha=0.2))) > >> glpolygon(LL, gpar=list(gate=list(col="black", fill="blue", alpha=0.2))) > >> glpolygon(UR, gpar=list(gate=list(col="black", fill="green", alpha=0.2))) > >> glpolygon(LR, gpar=list(gate=list(col="black", fill="black", alpha=0.2))) > >> trellis.unfocus() > >> > >> Florian > >> > >> > >>> I have failed in plotting all 4 gates and I looked at the filterSet > >>> function but I am not sure if making a filter set is the right way to do > >>> this? And in fact I am not sure that this is possible at all. But from > >>> what I have figured out about lattice I thought that it is possible to > >>> 'add' further information to a graph after it is created. Maybe you can > >>> point me in the right direction how to do this? > >>> > >>> And I would like to thank you for the time and effort you have put into > >>> making this package for Flow-data. I have been searching for > >>> Flow-software working in Linux some time now and really this is the > >>> first time I have come across something that allows me to get some nice > >>> output and at the same time control over what I am doing with my data. I > >>> have used R mainly for microarray data in the past, so my learning curve > >>> wasn't that steep this time, so this might not be true for first time > >>> users. But I can only recommend taking the time to learn how to use > >>> flowViz to anyone looking for Linux based Flow-software. > >>> > >>> Thanxs, > >>> > >>> Anja > >>> > >>> > >>> > >>> ---------------------------------------------------------------- -------- > >>> > >>> > >>> ---------------------------------------------------------------- -------- > >>> > >>> > >>> ---------------------------------------------------------------- -------- > >>> > >>> ---------------------------------------------------------------- -------- > >>> > >>> _______________________________________________ > >>> Bioconductor mailing list > >>> Bioconductor at stat.math.ethz.ch > >>> https://stat.ethz.ch/mailman/listinfo/bioconductor > >>> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor > >>> > >> > -- Anja Schiel, PhD Department of Anatomy Institute of Basic Medical Sciences University of Oslo Po-Box 1105 Blindern N-0317 Oslo Domus Medica Room 2362 Tel: +47-22851215 http://www.uio.no/sok?person=anjasc