Quality checks / flagging
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Tan, MinHan ▴ 180
@tan-minhan-431
Last seen 10.3 years ago
Hi, Sorry - this sounds like a rather elementary question - but is there any way I can read in selected columns into the marrayRaw object using read.genepix? (e.g. % > B532+2SD) Instead of performing quality flagging in GenePix, I was wondering how to achieve the same thing in R. Thank you! Regards, Min-Han This email message, including any attachments, is for the sole use of the intended recipient(s) and may contain confidential information. Any unauthorized review, use, disclosure or distribution is prohibited. If you are not the intended recipient(s) please contact the sender by reply email and destroy all copies of the original message. Thank you. [[alternative HTML version deleted]]
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@gordon-smyth
Last seen 1 hour ago
WEHI, Melbourne, Australia
See function read.maimages() and the section QualityWeights in the limma package. You can read in any weight functions or flags that you can calculate from the GenePix files, i.e., RG <- read.maimages(files,source="genepix",wtfun=myweightfun) where myweightfun is any function which you have defined which takes Genepix data from one array as a matrix or data.frame and computes from it a column of flags or quality weights. These weights then become part of the component RG$weights Note that read.maimages() produces an RGList object which is essentially equivalent to a marrayRaw object. You can transform it to an marrayRaw object if you want. Gordon At 11:22 AM 18/09/2003, Tan, MinHan wrote: >Hi, > >Sorry - this sounds like a rather elementary question - but is there any >way I can read in selected columns into the marrayRaw object using >read.genepix? (e.g. % > B532+2SD) > >Instead of performing quality flagging in GenePix, I was wondering how >to achieve the same thing in R. > >Thank you! > >Regards, >Min-Han
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Gordon Smyth wrote: > See function read.maimages() and the section QualityWeights in the limma > package. You can read in any weight functions or flags that you can > calculate from the GenePix files, i.e., > > RG <- read.maimages(files,source="genepix",wtfun=myweightfun) > > where myweightfun is any function which you have defined which takes > Genepix data from one array as a matrix or data.frame and computes from > it a column of flags or quality weights. These weights then become part > of the component > > RG$weights > > Note that read.maimages() produces an RGList object which is essentially > equivalent to a marrayRaw object. You can transform it to an marrayRaw > object if you want. > > Gordon > An even more elementary question (disclaimer: I am not a statistician): how to use these weights to make limma ignore some lines? I naively tried with weights of 0 and 1, but it didn't work as expected. Regards, yves > At 11:22 AM 18/09/2003, Tan, MinHan wrote: > >> Hi, >> >> Sorry - this sounds like a rather elementary question - but is there any >> way I can read in selected columns into the marrayRaw object using >> read.genepix? (e.g. % > B532+2SD) >> >> Instead of performing quality flagging in GenePix, I was wondering how >> to achieve the same thing in R. >> >> Thank you! >> >> Regards, >> Min-Han > > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor > >
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At 05:04 PM 18/09/2003, Yves Bastide wrote: >Gordon Smyth wrote: >>See function read.maimages() and the section QualityWeights in the limma >>package. You can read in any weight functions or flags that you can >>calculate from the GenePix files, i.e., >> RG <- read.maimages(files,source="genepix",wtfun=myweightfun) >>where myweightfun is any function which you have defined which takes >>Genepix data from one array as a matrix or data.frame and computes from >>it a column of flags or quality weights. These weights then become part >>of the component >> RG$weights >>Note that read.maimages() produces an RGList object which is essentially >>equivalent to a marrayRaw object. You can transform it to an marrayRaw >>object if you want. >>Gordon > >An even more elementary question (disclaimer: I am not a statistician): >how to use these weights to make limma ignore some lines? I naively tried >with weights of 0 and 1, but it didn't work as expected. Weights of 0 and 1 do have the effect of ignoring the log-ratios for spots with zero weight. I am not sure what you mean by "ignore some lines". Please explain further what you did and what you expected to happen which didn't. Gordon >Regards, > >yves
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Gordon Smyth wrote: > At 05:04 PM 18/09/2003, Yves Bastide wrote: > >> Gordon Smyth wrote: >> >>> See function read.maimages() and the section QualityWeights in the >>> limma package. You can read in any weight functions or flags that you >>> can calculate from the GenePix files, i.e., >>> RG <- read.maimages(files,source="genepix",wtfun=myweightfun) >>> where myweightfun is any function which you have defined which takes >>> Genepix data from one array as a matrix or data.frame and computes >>> from it a column of flags or quality weights. These weights then >>> become part of the component >>> RG$weights >>> Note that read.maimages() produces an RGList object which is >>> essentially equivalent to a marrayRaw object. You can transform it to >>> an marrayRaw object if you want. >>> Gordon >> >> >> An even more elementary question (disclaimer: I am not a >> statistician): how to use these weights to make limma ignore some >> lines? I naively tried with weights of 0 and 1, but it didn't work as >> expected. > > > Weights of 0 and 1 do have the effect of ignoring the log-ratios for > spots with zero weight. I am not sure what you mean by "ignore some > lines". Please explain further what you did and what you expected to > happen which didn't. Oh. I zero-weighted flagged spots and spots for unwanted genes. This resulted in about 600 genes to consider on each array, out of 4096. Then, the result of normalizeWithinArrays is strange, the final coef vs. lodds more so. (However, P-values and B are much better :) ). Same for weighting-out only flagged genes, keeping 'bout 1/3 of them. As I'm just starting playing with statistic tools, I don't know what is expected and what is clearly wrong. > > Gordon > >> Regards, >> >> yves
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At 05:52 PM 18/09/2003, Yves Bastide wrote: >>>An even more elementary question (disclaimer: I am not a statistician): >>>how to use these weights to make limma ignore some lines? I naively >>>tried with weights of 0 and 1, but it didn't work as expected. >> >>Weights of 0 and 1 do have the effect of ignoring the log-ratios for >>spots with zero weight. I am not sure what you mean by "ignore some >>lines". Please explain further what you did and what you expected to >>happen which didn't. > >Oh. I zero-weighted flagged spots and spots for unwanted genes. This >resulted in about 600 genes to consider on each array, out of 4096. In my honest opinion, throwing out almost all your data from a microarray experiment is crazy unless you have decided that there is something seriously wrong with the array. If you really want to do this, then please use normalizeWithinArrays with method="loess" rather than the default. You've thrown out almost all your data so there really isn't enough left to expect to be able to do normalization by print-tip groups. Gordon > Then, the result of normalizeWithinArrays is strange, the final coef vs. > lodds more so. (However, P-values and B are much better :) ). >Same for weighting-out only flagged genes, keeping 'bout 1/3 of them. As >I'm just starting playing with statistic tools, I don't know what is >expected and what is clearly wrong. > >>Gordon >> >>>Regards, >>> >>>yves
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Gordon Smyth wrote: > At 05:52 PM 18/09/2003, Yves Bastide wrote: > >>>> An even more elementary question (disclaimer: I am not a >>>> statistician): how to use these weights to make limma ignore some >>>> lines? I naively tried with weights of 0 and 1, but it didn't work >>>> as expected. >>> >>> >>> Weights of 0 and 1 do have the effect of ignoring the log-ratios for >>> spots with zero weight. I am not sure what you mean by "ignore some >>> lines". Please explain further what you did and what you expected to >>> happen which didn't. >> >> >> Oh. I zero-weighted flagged spots and spots for unwanted genes. This >> resulted in about 600 genes to consider on each array, out of 4096. > > > In my honest opinion, throwing out almost all your data from a > microarray experiment is crazy unless you have decided that there is > something seriously wrong with the array. If you really want to do this, > then please use normalizeWithinArrays with method="loess" rather than > the default. You've thrown out almost all your data so there really > isn't enough left to expect to be able to do normalization by print- tip > groups. > Yep, that's the conclusion I arrived to. My basic flawed methodology was to find differentially expressed genes in one group, then re-read the arrays for another group, lather, rince, repeat. Gee. > Gordon > Thanks, yves PS: one question on limma.R (1.2.1, 1.2.2) toptable, line 511: does the else apply to the right if? if(is.null(A)) ifsort.by=="A") stop("Cannot sort by A-values as these have not been given") else (and yes, I'm a Python user)
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> PS: one question on limma.R (1.2.1, 1.2.2) > toptable, line 511: does the else apply to the right if? > if(is.null(A)) > ifsort.by=="A") stop("Cannot sort by A-values as these > > have not been given") > else > (and yes, I'm a Python user) Thanks for pointing this out - it's a bug. Fixed in limma 1.2.3. Gordon
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