FlowViz graphics problems
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Anja Schiel ▴ 20
@anja-schiel-3466
Last seen 7.3 years ago
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 > > -- <html> <head> <meta http-equiv="Content-Type" content="text/html; CHARSET=UTF-8"> <meta name="GENERATOR" content="GtkHTML/3.26.0"> </head> <body> Anja Schiel, PhD
Department of Anatomy
Institute of Basic Medical Sciences
University of Oslo
Po-Box 1105 Blinderen
N-0317 Oslo

Domus Medica
Room 2362
Tel: +47-22851137

<tt><font color="#737373">http://www.uio.no/sok?person=anjasc</font></tt> </body> </html>
Microarray Visualization graph flowCore flowViz Microarray Visualization graph flowCore • 1.1k views
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@nishant-gopalakrishnan-3253
Last seen 7.3 years ago
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 >>> >>
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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
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