Dear Holger,
It's working fine now ! I understand.... I had a similar problem with
a
bioperl package (Bio::SeqIO::TIGR) that was never tested and I found a
set of bugs... Again, thanks for your help
I will be ready to analyse my set of rice salt-stressed microarray
with
your 'patchs'.
Your package if exactly what I needed !
Best regards
Christophe
Holger Schwender a écrit:
>Set R.fold=FALSE. Again, this will be automatically done in the soon
>published new version of siggenes / Bioconductor.
>
>Maybe I should tell you the reason why the one-class analysis doesn't
work
>that well: The origin version of siggenes only contained two class
analyses.
>Then someone ask how to do an one-class analysis with this package.
Since I
>thought that it was not that much work to do to modify the functions
for an
>one-class analysis (it uses the same test statistic as the two-class
paired
>analysis), I modified the code but didn't test it. And so there are
some
>bugs which as mentioned earlier will be removed in the new version of
>siggenes.
>
>Best,
>Holger
>
>
>
>>Hi,
>>Thanks a lot. Now, sam function is working but (sorry for that !),
there
>>is now a problem with sam.plot. Everything seems ok except for
sam.plot
>>gene list output
>>
>>
>>Here is the code (I tried with golub dataset for this example):
>>
>>golubvf<-golub[,1:4]
>>golubvf.cl<-c(1,1,1,1)
>>a<-median(rs.cal(golubvf,1:4,B=1)$s)
>>sam.output<-sam(golubvf,golubvf.cl,s0=a)
>>sam.final<-sam.plot(sam.output,d=1.4)
>>
>>Error in inherits(x, "data.frame") : subscript out of bounds
>>
>>It draws the sam plot for d=1.4 but nothing in sam.final
>>
>>Christophe
>>
>>
>>
>>>Oh, sorry I've forgot: Please source the attached code to R. Then
it
>>>
>>>
>>should
>>
>>
>>>work. There was another bug in the one-class case which I've fixed
only
>>>
>>>
>>for
>>
>>
>>>the next version of siggenes which will appear soon (when Release
1.4 of
>>>Bioconductor comes out).
>>>
>>>I've also attached the next version of the function fudge which can
>>>
>>>
>>handle
>>
>>
>>>less than 101 genes.
>>>
>>>
>>>
>>>
>>>
>>>>So I did it:
>>>>
>>>>here are the last lines
>>>>
>>>>
>>>>[8347] 0.084854730 0.148227947 0.106908754 0.125831287 0.077950147
>>>>0.211227137
>>>>[8353] 0.152660230 0.045091101 0.096348186 0.044063845 0.108855645
>>>>0.092417888
>>>>[8359] 0.118671164 0.027960521 0.043253745 0.046361978 0.154575781
>>>>0.152634900
>>>>[8365] 0.074517075 0.229593012 0.082204362 0.206090376 0.045857296
>>>>0.094095230
>>>>[8371] 0.103852101 0.233859308 0.258523505 0.103864178 0.122385598
>>>>0.167787555
>>>>[8377] 0.168447995 0.095190885 0.095376051 0.049927168 0.053458959
>>>>0.102979050
>>>>[8383] 0.183686758 0.162928860 0.101409564 0.055026031 0.047275238
>>>>0.299989073
>>>>[8389] 0.048762658 0.114255858 0.213818969 0.065428394 0.012298578
>>>>0.061579019
>>>>[8395] 0.050404750 0.075257078 0.084360630 0.199929845 0.177594221
>>>>0.157630579
>>>>[8401] 0.180217253 0.112000120 0.218196589 0.152459139 0.144681365
>>>>0.073595428
>>>>[8407] 0.108158061 0.013548077 0.149060865 0.123811131 0.308569776
>>>>0.217594634
>>>>[8413] 0.071042281 0.099095831 0.029147566 0.044104928 0.109420584
>>>>0.116411330
>>>>[8419] 0.121903308 0.053624854 0.159189549 0.203203948 0.128026072
>>>>0.142591910
>>>>[8425] 0.140158370 0.095307900 0.230283970 0.182668313 0.159248974
>>>>0.215344233
>>>>[8431] 0.128684507 0.126580411 0.067487608 0.041372752 0.076218630
>>>>0.118931153
>>>>[8437] 0.292739641 0.058138725 0.057913610 0.108836945 0.125646992
>>>>0.194450518
>>>>[8443] 0.092050998 0.926009673 0.164894890 0.304727643 0.415619179
>>>>0.174991675
>>>>
>>>>So standard deviations seems not to be equal to zero....
>>>>
>>>>I tried also with another set of expression data (golub) using the
first
>>>>four columns, five six and 12 and gave a similar result.
>>>>
>>>>It's working (one class) until five replicates and gave a similar
error
>>>>that I got with my data if I worked with four replicates or less ?
>>>>
>>>>Strange ?
>>>>
>>>>Thanks
>>>>
>>>>Best regards
>>>>
>>>>Dr Christophe Perin
>>>>
>>>>
>>>>Holger Schwender a écrit:
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>>It seems that each gene has the same value for each of the four
>>>>>
>>>>>
>>>>>
>>>>>
>>>>replicates
>>>>
>>>>
>>>>
>>>>
>>>>>(because of the error message). Otherwise all the expression
values of
>>>>>
>>>>>
>>>>>
>>>>>
>>>>the
>>>>
>>>>
>>>>
>>>>
>>>>>genes have to be NAs. Please take a look on the standard
deviations by
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>>rs.out<-rs.cal(salt_stress,1:4,B=1)
>>>>>>rs.out$s
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>Are they all zero? If yes, then all genes have the same value for
each
>>>>>
>>>>>
>>of
>>
>>
>>>>>their expression levels.
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>>Hi,
>>>>>>
>>>>>>Thanks.
>>>>>>I download and install the new version of siggenes. I also added
the
>>>>>>
>>>>>>
>>two
>>
>>
>>>>>>lines of code to set s0 as the median of the standard deviations
of
>>>>>>
>>>>>>
>>the
>>
>>
>>>>>>gene and it's working now.
>>>>>>But, I still have a pb: I played with 6 replicates as a test but
I
>>>>>>planned to generate only 4 replicates (2 dye swap) for each
step of
>>>>>>
>>>>>>
>>my
>>
>>
>>>>>>time course experiment. So if I try with only 4 replicates
instead 6 I
>>>>>>gave the output as follow:
>>>>>>
>>>>>>SAM Analysis for the one-class case.
>>>>>>
>>>>>>Warning: There are 8448 genes which have variance Zero or no
>>>>>>
>>>>>>
>>non-missing
>>
>>
>>>>>>values.
>>>>>> The d-value of these genes is set to NA.
>>>>>>
>>>>>>There are 8448 missing d values.
>>>>>>
>>>>>>Error in "[<-"(*tmp*, int, value = numeric(0)) :
>>>>>> nothing to replace with
>>>>>>In addition: Warning messages:
>>>>>>1: no finite arguments to min; returning Inf
>>>>>>2: no finite arguments to max; returning -Inf
>>>>>>
>>>>>>with the following code:
>>>>>>
>>>>>>a<-median(rs.cal(salt_stress,1:4,B=1)$s)
>>>>>>sam.output<-sam(maM(salt_stress,salt_cl,s0=a)
>>>>>>
>>>>>>Again, any idea ? (I tried with only 5 replicates and it's
working
>>>>>>
>>>>>>
>>???)
>>
>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>Holger Schwender a écrit:
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>>Hi,
>>>>>>>
>>>>>>>the warning message says that all the expression values of each
of
>>>>>>>
>>>>>>>
>>the
>>
>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>353
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>>genes are either the same or NAs.
>>>>>>>
>>>>>>>The error message usually occurs in the computation of the
fudge
>>>>>>>
>>>>>>>
>>factor
>>
>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>when
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>>there are less than 101 different d values. This "bug" will be
fixed
>>>>>>>
>>>>>>>
>>in
>>
>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>the
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>>next version of siggenes. You can avoid this error message by
setting
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>s0
>>>>
>>>>
>>>>
>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>in
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>>sam(...) to a reasonable value, or by doing something like
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>>a<-median(rs.cal(salt_stress,1:6,B=1)$s)
>>>>>>>>sam(salt_stress,salt_cl,s0=a)
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>This will specify the fudge factor as the median of the
standard
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>deviations
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>>of the genes.
>>>>>>>
>>>>>>>Since you have >8000 genes this error message shouldn't
actually
>>>>>>>
>>>>>>>
>>occur.
>>
>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>So
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>>another idea is that you might have an old version of siggenes
in
>>>>>>>
>>>>>>>
>>which
>>
>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>the
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>>one-class analysis did not work correctly. If you don't have
the
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>siggenes
>>>>
>>>>
>>>>
>>>>
>>>>>>>version of the developmental section of Bioconductor, you
should
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>download
>>>>
>>>>
>>>>
>>>>
>>>>>>>this version (1.0.6).
>>>>>>>
>>>>>>>Best,
>>>>>>>Holger
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>>Hi,
>>>>>>>>
>>>>>>>>I'am new on this mailing list and I probably have a 'stupid'
>>>>>>>>
>>>>>>>>
>>question.
>>
>>
>>>>>>>>I'am doing SAM analysis for the one-class case on a 6
replicates per
>>>>>>>>8448 gene matrix.
>>>>>>>>I get each time this message
>>>>>>>>
>>>>>>>>SAM Analysis for the one-class case.
>>>>>>>>
>>>>>>>>Warning: There are 353 genes which have variance Zero or no
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>non-missing
>>>>
>>>>
>>>>
>>>>
>>>>>>>>values.
>>>>>>>> The d-value of these genes is set to NA.
>>>>>>>>
>>>>>>>>Error in var(v) : missing observations in cov/cor
>>>>>>>>
>>>>>>>>Any idea ?
>>>>>>>>
>>>>>>>>Here the code:
>>>>>>>>
>>>>>>>>sam.output<-sam(salt_stress,salt.cl)
>>>>>>>>
>>>>>>>>where salt.cl<-c(rep(1,6))
>>>>>>>>and salt_stress a matrix (6 col per 8448 row)
>>>>>>>>
>>>>>>>>
>>>>>>>>thanks
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>Thanks
>>>>>>>>
>>>>>>>>_______________________________________________
>>>>>>>>Bioconductor mailing list
>>>>>>>>Bioconductor@stat.math.ethz.ch
>>>>>>>>
https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>>
>>>>
>>>
>>>
>>>
>>>
>>
>>
>
>
>
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