[BioC] Fwd: maSigPro - NA values for coefficient estimates - Is a polynomical fit recommended for my data?
jeremy wilson
jeremy.wilson88 at gmail.com
Wed Nov 11 00:49:05 CET 2009
Dear Ana,
thanks a lot for the very helpful suggestions. You guided me in the
right direction. The analysis makes exact sense of my data.
On Tue, Nov 10, 2009 at 7:20 AM, Ana Conesa <aconesa at cipf.es> wrote:
> Dear Jeremy
>
> My apologies for the late response.
> First of all: maSigPro compares profiles. This means that you must have
> overlapping time points, not necessarily the same time points, but
> overlapping at least. This means that if you have a group with only one time
> point, you cannot do much with this. For the other 2 groups you can compare
> only the segment corresponding to time points 0 and 5. You can add the rest
> of the time points of the last group, but this will not be compared.
>
> In you case I would do the following:
>
> - Compare groups 2 and 3 considering time points 0 and 5 and using degree = 1
> - Compare all groups only at time point 0 using limma
> - Study the profiles changes ONLY for group 3 using a degree = 2 or 3 (if you
> do not have replicates, surely not more than 2)
>
> This, of course if this analysis makes sense for your data, but I guess that
> if you designed it like this, your questions could be answered with these
> analyzes.
>
> Could it be?
> Hope this answer your question. If not, come back to me..
>
> Best regards
>
> Ana
>
>
> On Monday 09 November 2009 19:30:58 you wrote:
>> Hi dear Ana,
>>
>> Please see my question below. I posted this last week on the BioC list
>> but did not get any answer. I would appreciate if you show me a path
>> here.
>> I am sorry for emailing you directly. Thought I should as no body is
>> replying to this question.
>>
>>
>> ---------- Forwarded message ----------
>> From: jeremy wilson <jeremy.wilson88 at gmail.com>
>> Date: Thu, Nov 5, 2009 at 11:05 AM
>> Subject: maSigPro - NA values for coefficient estimates - Is a
>> polynomical fit recommended for my data?
>> To: bioconductor at stat.math.ethz.ch
>>
>>
>> Dear BioConductors,
>>
>> I have unbalanced number of time points in my experiment. In 3 groups
>> I have, one group has only observations at one time point (time 0),
>> the other at two time points (times 0, 5) and the last group with four
>> time points(0, 5, 12, 31). I am wondering if I can use maSigPro for
>> this type of data. In this case, what order of polynomial fit is good?
>> Do you recommend to use degree = 1 to reduce the degree of the model
>> as the best model is the one with the least order? or is it good to
>> model with degree = 3? I have 4 time points in one group.
>>
>> Is it better to do this kind of unbalanced time points data with
>> LIMMA? There are many comparisons to consider if I use LIMMA. What do
>> you suggest to use?
>>
>> I did fit a degree = 3 polynomial model and got NA values. The NA
>> values appeared as below. There were no NA values for the remaining
>> coefficients. Are these due to no values for some experimental groups
>> at some time points? Can I ignore these warnings?
>>
>> Here are my commands and warnings I got..
>> design.init<-read.table("maSigPro_PhenotypeInput.txt", sep="\t",
>> header=TRUE, row.names=1)
>>
>> > design<-make.design.matrix(design.init, deg=3) # Create a maSigPro design
>> > matrix
>> >
>> > design
>>
>> $dis
>> VehiclevsNaive BCGvsNaive Time TimexVehicle TimexBCG
>> Time2 Time2xVehicle Time2xBCG Time3 Time3xVehicle Time3xBCG
>> Veh Day0 A2.CEL 1 0 0 0 0
>> 0 0 0 0 0 0
>> Veh Day0 A4.CEL 1 0 0 0 0
>> 0 0 0 0 0 0
>> Veh Day0 A5.CEL 1 0 0 0 0
>> 0 0 0 0 0 0
>> Veh Day12 A1.CEL 1 0 12 12 0
>> 144 144 0 1728 1728 0
>> Veh Day12 A3.CEL 1 0 12 12 0
>> 144 144 0 1728 1728 0
>> Veh Day0 B1.CEL 1 0 0 0 0
>> 0 0 0 0 0 0
>> Veh Day0 B3.CEL 1 0 0 0 0
>> 0 0 0 0 0 0
>> Veh Day0 B4.CEL 1 0 0 0 0
>> 0 0 0 0 0 0
>> Veh Day12 B2.CEL 1 0 12 12 0
>> 144 144 0 1728 1728 0
>> Veh Day12 B3.CEL 1 0 12 12 0
>> 144 144 0 1728 1728 0
>> Veh Day0 C1.CEL 1 0 0 0 0
>> 0 0 0 0 0 0
>> Veh Day0 C3.CEL 1 0 0 0 0
>> 0 0 0 0 0 0
>> Veh Day0 C4.CEL 1 0 0 0 0
>> 0 0 0 0 0 0
>> Veh Day0 C5.CEL 1 0 0 0 0
>> 0 0 0 0 0 0
>> Veh Day12 C1.CEL 1 0 12 12 0
>> 144 144 0 1728 1728 0
>> Veh Day12 C3.CEL 1 0 12 12 0
>> 144 144 0 1728 1728 0
>> Day 0 A1.CEL 0 1 0 0 0
>> 0 0 0 0 0 0
>> Day 0 A2.CEL 0 1 0 0 0
>> 0 0 0 0 0 0
>> Day 0 A3.CEL 0 1 0 0 0
>> 0 0 0 0 0 0
>> Day 5 A1.CEL 0 1 5 0 5
>> 25 0 25 125 0 125
>> Day 5 A2.CEL 0 1 5 0 5
>> 25 0 25 125 0 125
>> Day 5 A3.CEL 0 1 5 0 5
>> 25 0 25 125 0 125
>> Day 12 A1.CEL 0 1 12 0 12
>> 144 0 144 1728 0 1728
>> Day 12 A2.CEL 0 1 12 0 12
>> 144 0 144 1728 0 1728
>> Day 12 A3.CEL 0 1 12 0 12
>> 144 0 144 1728 0 1728
>> Day 31 A1.CEL 0 1 31 0 31
>> 961 0 961 29791 0 29791
>> Day 31 A2.CEL 0 1 31 0 31
>> 961 0 961 29791 0 29791
>> Day 31 A3.CEL 0 1 31 0 31
>> 961 0 961 29791 0 29791
>> Day 0 B1.CEL 0 1 0 0 0
>> 0 0 0 0 0 0
>> Day 0 B2.CEL 0 1 0 0 0
>> 0 0 0 0 0 0
>> Day 0 B3.CEL 0 1 0 0 0
>> 0 0 0 0 0 0
>> Day 5 B1.CEL 0 1 5 0 5
>> 25 0 25 125 0 125
>> Day 5 B2.CEL 0 1 5 0 5
>> 25 0 25 125 0 125
>> Day 5 B3.CEL 0 1 5 0 5
>> 25 0 25 125 0 125
>> Day 12 B1.CEL 0 1 12 0 12
>> 144 0 144 1728 0 1728
>> Day 12 B2.CEL 0 1 12 0 12
>> 144 0 144 1728 0 1728
>> Day 12 B3.CEL 0 1 12 0 12
>> 144 0 144 1728 0 1728
>> Day 31 B1.CEL 0 1 31 0 31
>> 961 0 961 29791 0 29791
>> Day 31 B2.CEL 0 1 31 0 31
>> 961 0 961 29791 0 29791
>> Day 31 B3.CEL 0 1 31 0 31
>> 961 0 961 29791 0 29791
>> Day 0 C1.CEL 0 1 0 0 0
>> 0 0 0 0 0 0
>> Day 0 C2.CEL 0 1 0 0 0
>> 0 0 0 0 0 0
>> Day 0 C3.CEL 0 1 0 0 0
>> 0 0 0 0 0 0
>> Day 0 C4.CEL 0 1 0 0 0
>> 0 0 0 0 0 0
>> Day 5 C1.CEL 0 1 5 0 5
>> 25 0 25 125 0 125
>> Day 5 C2.CEL 0 1 5 0 5
>> 25 0 25 125 0 125
>> Day 5 C3.CEL 0 1 5 0 5
>> 25 0 25 125 0 125
>> Day 12 C1.CEL 0 1 12 0 12
>> 144 0 144 1728 0 1728
>> Day 12 C2.CEL 0 1 12 0 12
>> 144 0 144 1728 0 1728
>> Day 12 C3.CEL 0 1 12 0 12
>> 144 0 144 1728 0 1728
>> Day 31 C1.CEL 0 1 31 0 31
>> 961 0 961 29791 0 29791
>> Day 31 C2.CEL 0 1 31 0 31
>> 961 0 961 29791 0 29791
>> Day 31 C3.CEL 0 1 31 0 31
>> 961 0 961 29791 0 29791
>> Naive A1.CEL 0 0 0 0 0
>> 0 0 0 0 0 0
>> Naive A2.CEL 0 0 0 0 0
>> 0 0 0 0 0 0
>> Naive B1.CEL 0 0 0 0 0
>> 0 0 0 0 0 0
>> Naive B2.CEL 0 0 0 0 0
>> 0 0 0 0 0 0
>> Naive C1.CEL 0 0 0 0 0
>> 0 0 0 0 0 0
>> Naive C2.CEL 0 0 0 0 0
>> 0 0 0 0 0 0
>> Naive C3.CEL 0 0 0 0 0
>> 0 0 0 0 0 0
>>
>> $groups.vector
>> [1] "VehiclevsNaive" "BCGvsNaive" "Naive"
>> "VehiclevsNaive" "BCGvsNaive" "Naive" "VehiclevsNaive"
>> "BCGvsNaive"
>> [9] "Naive" "VehiclevsNaive" "BCGvsNaive"
>>
>> $edesign
>> Time Replicate Naive Vehicle BCG
>> Veh Day0 A2.CEL 0 1 0 1 0
>> Veh Day0 A4.CEL 0 1 0 1 0
>> Veh Day0 A5.CEL 0 1 0 1 0
>> Veh Day12 A1.CEL 12 2 0 1 0
>> Veh Day12 A3.CEL 12 2 0 1 0
>> Veh Day0 B1.CEL 0 3 0 1 0
>> Veh Day0 B3.CEL 0 3 0 1 0
>> Veh Day0 B4.CEL 0 3 0 1 0
>> Veh Day12 B2.CEL 12 4 0 1 0
>> Veh Day12 B3.CEL 12 4 0 1 0
>> Veh Day0 C1.CEL 0 5 0 1 0
>> Veh Day0 C3.CEL 0 5 0 1 0
>> Veh Day0 C4.CEL 0 5 0 1 0
>> Veh Day0 C5.CEL 0 5 0 1 0
>> Veh Day12 C1.CEL 12 6 0 1 0
>> Veh Day12 C3.CEL 12 6 0 1 0
>> Day 0 A1.CEL 0 7 0 0 1
>> Day 0 A2.CEL 0 7 0 0 1
>> Day 0 A3.CEL 0 7 0 0 1
>> Day 5 A1.CEL 5 8 0 0 1
>> Day 5 A2.CEL 5 8 0 0 1
>> Day 5 A3.CEL 5 8 0 0 1
>> Day 12 A1.CEL 12 9 0 0 1
>> Day 12 A2.CEL 12 9 0 0 1
>> Day 12 A3.CEL 12 9 0 0 1
>> Day 31 A1.CEL 31 10 0 0 1
>> Day 31 A2.CEL 31 10 0 0 1
>> Day 31 A3.CEL 31 10 0 0 1
>> Day 0 B1.CEL 0 11 0 0 1
>> Day 0 B2.CEL 0 11 0 0 1
>> Day 0 B3.CEL 0 11 0 0 1
>> Day 5 B1.CEL 5 12 0 0 1
>> Day 5 B2.CEL 5 12 0 0 1
>> Day 5 B3.CEL 5 12 0 0 1
>> Day 12 B1.CEL 12 13 0 0 1
>> Day 12 B2.CEL 12 13 0 0 1
>> Day 12 B3.CEL 12 13 0 0 1
>> Day 31 B1.CEL 31 14 0 0 1
>> Day 31 B2.CEL 31 14 0 0 1
>> Day 31 B3.CEL 31 14 0 0 1
>> Day 0 C1.CEL 0 15 0 0 1
>> Day 0 C2.CEL 0 15 0 0 1
>> Day 0 C3.CEL 0 15 0 0 1
>> Day 0 C4.CEL 0 15 0 0 1
>> Day 5 C1.CEL 5 16 0 0 1
>> Day 5 C2.CEL 5 16 0 0 1
>> Day 5 C3.CEL 5 16 0 0 1
>> Day 12 C1.CEL 12 17 0 0 1
>> Day 12 C2.CEL 12 17 0 0 1
>> Day 12 C3.CEL 12 17 0 0 1
>> Day 31 C1.CEL 31 18 0 0 1
>> Day 31 C2.CEL 31 18 0 0 1
>> Day 31 C3.CEL 31 18 0 0 1
>> Naive A1.CEL 0 19 1 0 0
>> Naive A2.CEL 0 19 1 0 0
>> Naive B1.CEL 0 20 1 0 0
>> Naive B2.CEL 0 20 1 0 0
>> Naive C1.CEL 0 21 1 0 0
>> Naive C2.CEL 0 21 1 0 0
>> Naive C3.CEL 0 21 1 0 0
>>
>> > fit <- p.vector(efiltered.mat, design, Q = 0.05, MT.adjust = "BH",min.obs
>> > = 20) tstep <- T.fit(fit, step.method = "two.ways.backward", alfa = 0.05)
>>
>> I got the following warnings after the above step
>> warnings:
>> 1: In rbind(sol, as.numeric(c(p.value, result$r.squared, ... :
>> NAs introduced by coercion
>> 2: In rbind(sol, as.numeric(c(p.value, result$r.squared, ... :
>> NAs introduced by coercion
>>
>> >sigs <- get.siggenes(tstep, rsq = 0.6, vars = "groups")
>> > see.genes(sigs$sig.genes$BCGvsNaive, main = "BCGvsNaive", show.fit =
>> > T,dis =design$dis, cluster.method="kmeans" ,cluster.data = 1, k = 9)
>>
>> Warning messages:
>> 1: In rbind(sol, as.numeric(c(p.value, result$r.squared, p.valor))) :
>> NAs introduced by coercion
>> 2: In rbind(sol, as.numeric(c(p.value, result$r.squared, p.valor))) :
>> NAs introduced by coercion
>>
>>
>>
>> betaTimexBCG betaTime2 betaTime2xVehicle
>> betaTime2xBCG betaTime3
>> AB002558_at NA 0.0000000000 NA
>> NA -1.824498e-05
>> AF001953_g_at NA 0.0114056076 NA
>> NA -2.616775e-04
>> AF067795_at NA 0.0011159473 NA
>> NA -3.539580e-05
>> D13120_s_at NA 0.0011509775 NA
>> NA 0.000000e+00
>> E01534cds_s_at NA -0.0029700508 NA
>> NA 8.818569e-05
>> L24896_s_at NA -0.0164766816 NA
>> NA 3.728959e-04
>> M20156_at NA -0.0011509235 NA
>> NA 0.000000e+00
>> M25638_s_at NA 0.0137020816 NA
>> NA -3.174337e-04
>> M58404_at NA -0.0123769559 NA
>> NA 2.928362e-04
>> M60921_at NA 0.0000000000 NA
>> NA -1.465997e-05
>> M91652complete_seq_at NA -0.0020565596 NA
>> NA 0.000000e+00
>> M94918mRNA_f_at NA -0.0256353952 NA
>> NA 6.500460e-04
>> M94919mRNA_f_at NA 0.0000000000 NA
>> NA 3.851441e-05
>> rc_AA859783_at NA 0.0009030979 NA
>> NA 0.000000e+00
>> rc_AA875171_g_at NA -0.0007324529 NA
>> NA 0.000000e+00
>> rc_AA891171_s_at NA 0.0010095092 NA
>> NA 0.000000e+00
>> rc_AA891727_g_at NA -0.0047867258 NA
>> NA 1.441130e-04
>> rc_AA892123_at NA -0.0038206649 NA
>> NA 1.165693e-04
>> rc_AA892248_g_at NA 0.0000000000 NA
>> NA 0.000000e+00
>> rc_AA893246_at NA 0.0010566522 NA
>> NA 0.000000e+00
>> rc_AA924772_at NA -0.0008263709 NA
>> NA 0.000000e+00
>> rc_AA944324_at NA -0.0013202929 NA
>> NA 0.000000e+00
>> rc_AI007824_g_at NA 0.0000000000 NA
>> NA 0.000000e+00
>> rc_AI013194_at NA 0.0000000000 NA
>> NA 0.000000e+00
>> rc_AI102044_at NA 0.0000000000 NA
>> NA 0.000000e+00
>> rc_AI104544_at NA 0.0019113263 NA
>> NA 0.000000e+00
>> rc_AI170268_at NA -0.0012527673 NA
>> NA 0.000000e+00
>> rc_AI176662_s_at NA 0.0036907561 NA
>> NA -1.149829e-04
>> rc_AI179576_s_at NA -0.0275651514 NA
>> NA 6.926836e-04
>> rc_AI235364_at NA -0.0028867693 NA
>> NA 8.997026e-05
>> rc_AI237836_at NA 0.0000000000 NA
>> NA 0.000000e+00
>> S68135_s_at NA 0.0064503716 NA
>> NA -1.943265e-04
>> S74351_s_at NA 0.0066852568 NA
>> NA -2.153613e-04
>> U02553cds_s_at NA 0.0000000000 NA
>> NA -2.903164e-05
>> U12568_at NA -0.0012220408 NA
>> NA 0.000000e+00
>> U19866_at NA -0.0010647292 NA
>> NA 0.000000e+00
>> U39875_at NA -0.0007813637 NA
>> NA 0.000000e+00
>> U84727_at NA 0.0104752791 NA
>> NA -2.460059e-04
>> X03347cds_g_at NA 0.0022984842 NA
>> NA -7.116160e-05
>> X54510_g_at NA 0.0000000000 NA
>> NA 2.572912e-05
>> X56325mRNA_s_at NA -0.0214234307 NA
>> NA 5.057676e-04
>> X58389cds_s_at NA -0.0034907821 NA
>> NA 1.055627e-04
>> X63375exon_at NA 0.0132059943 NA
>> NA -3.082255e-04
>> X66369_at NA 0.0014723516 NA
>> NA 0.000000e+00
>> X94242_at NA 0.0000000000 NA
>> NA 0.000000e+00
>> betaTime3xVehicle betaTime3xBCG
>> AB002558_at NA NA
>> AF001953_g_at NA NA
>> AF067795_at NA NA
>> D13120_s_at NA NA
>> E01534cds_s_at NA NA
>> L24896_s_at NA NA
>> M20156_at NA NA
>> M25638_s_at NA NA
>> M58404_at NA NA
>> M60921_at NA NA
>> M91652complete_seq_at NA NA
>> M94918mRNA_f_at NA NA
>> M94919mRNA_f_at NA NA
>> rc_AA859783_at NA NA
>> rc_AA875171_g_at NA NA
>> rc_AA891171_s_at NA NA
>> rc_AA891727_g_at NA NA
>> rc_AA892123_at NA NA
>> rc_AA892248_g_at NA NA
>> rc_AA893246_at NA NA
>> rc_AA924772_at NA NA
>> rc_AA944324_at NA NA
>> rc_AI007824_g_at NA NA
>> rc_AI013194_at NA NA
>> rc_AI102044_at NA NA
>> rc_AI104544_at NA NA
>> rc_AI170268_at NA NA
>> rc_AI176662_s_at NA NA
>> rc_AI179576_s_at NA NA
>> rc_AI235364_at NA NA
>> rc_AI237836_at NA NA
>> S68135_s_at NA NA
>> S74351_s_at NA NA
>> U02553cds_s_at NA NA
>> U12568_at NA NA
>> U19866_at NA NA
>> U39875_at NA NA
>> U84727_at NA NA
>> X03347cds_g_at NA NA
>> X54510_g_at NA NA
>> X56325mRNA_s_at NA NA
>> X58389cds_s_at NA NA
>> X63375exon_at NA NA
>> X66369_at NA NA
>> X94242_at NA NA
>>
>>
>> The following coefficients had no "NA" values.
>> tstep$coefficients$beta0
>> tstep$coefficients$betaVehiclevsNaive
>> tstep$coefficients$betaBCGvsNaive
>> tstep$coefficients$betaTime tstep$coefficients$betaTimexVehicle
>>
>> > sessionInfo()
>>
>> R version 2.10.0 (2009-10-26)
>> i386-pc-mingw32
>>
>> locale:
>> [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United
>> States.1252
>> [3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
>> [5] LC_TIME=English_United States.1252
>>
>> attached base packages:
>> [1] tcltk stats graphics grDevices utils datasets
>> methods base
>>
>> other attached packages:
>> [1] maSigPro_1.17.5 DynDoc_1.23.1
>> widgetTools_1.23.1 genefilter_1.26.4
>> [5] arrayQualityMetrics_2.4.1 affyPLM_1.22.0
>> preprocessCore_1.7.9 gcrma_2.17.4
>> [9] rgu34acdf_2.5.0 affy_1.23.12 Biobase_2.5.8
>>
>> loaded via a namespace (and not attached):
>> [1] affyio_1.13.5 annotate_1.23.4 AnnotationDbi_1.7.20
>> beadarray_1.13.9
>> [5] Biostrings_2.13.54 DBI_0.2-4 grid_2.10.0
>> hwriter_1.1
>> [9] IRanges_1.3.99 KernSmooth_2.23-3 lattice_0.17-26
>> latticeExtra_0.6-3
>> [13] limma_3.0.3 marray_1.23.0 Mfuzz_2.3.4
>> RColorBrewer_1.0-2
>> [17] RSQLite_0.7-3 simpleaffy_2.21.3 splines_2.10.0
>> stats4_2.10.0
>> [21] survival_2.35-7 tkWidgets_1.23.2 tools_2.10.0
>> vsn_3.13.7
>> [25] xtable_1.5-5
>>
>>
>> I would really appreciate for any suggestions. Thanks in advance..
>
>
>
> --
> Ana Conesa
> Bioinformatics and Genomics Department
> Centro de Investigaciones Principe Felipe
> Avda. Autopista Saler 16,
> 46012 Valencia, Spain
> Phone: +34 96 328 96 80
> Fax: +34 96 328 97 01
> http://bioinfo.cipf.es/aconesa
> http://www.blast2go.org
> ==========================================
> FIRST INTERNATIONAL COURSE IN AUTOMATED
> FUNCTIONAL ANNOTATION AND DATA MINING
> Valencia/Orlando, September/October 2009
> http://bioinfo.cipf.es/blast2gocourse
> ==========================================
>
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