Thanks a lot.

Chen

2012/4/5 James W. MacDonald <jmacdon@uw.edu>

> Hi Chen,
>
>
> On 4/5/2012 10:03 AM, yao chen wrote:
>
>> Thanks, James.
>>
>> You are right. I find expression of some genes are significant between
>> treatment and control: I use lmFit(x,design), in which design only include
>> treatment vs control
>>
>>  and the case vs control contrast is no longer significant when I add
>> cholesterol level to the model. In this case, cholesterol level is in the
>> design matrix, as
>>              case  control   cholesterol
>>                 1        0           130
>>                  0       1           120
>>                  1       0           110
>> My question is, the cholesterol explain gene expression more may be the
>> reflection of treatment . That is treatment change the expression of some
>> genes and then cause the cholesterol difference. Therefore it's not the
>> confounding factor. But I don't know how to distinguish them.
>>
>
> That is the point I tried to make in my last response. All you know at
> this point is that the difference in gene expression correlates with
> cholesterol level. The model only tells you correlation, not causation.
>
> If you want to know if the gene causes differences in cholesterol levels
> or if cholesterol levels cause differential gene expression, you will have
> to design and perform an experiment to test for the causal factor.
>
> Best,
>
> Jim
>
>
>
>> Any suggestion?
>>
>>
>> Chen
>>
>>
>>
>> 2012/4/5 James W. MacDonald <jmacdon@uw.edu <mailto:jmacdon@uw.edu>>
>>
>>
>>    Hi Chen,
>>
>>
>>    On 4/5/2012 9:12 AM, yao chen wrote:
>>
>>        Thanks, Moshe and James
>>
>>        Any question about confounding factor if you or anybody can
>>        help me.
>>
>>        Actually, I have many different phenotype of samples here,
>>        such as weight, age, cholesterol. I want to find the
>>        differential expressed genes between case and control
>>        (say:high blood pressure vs health ). I found some significant
>>        genes when I adjust the age, weight. However, when I adjust
>>        the cholesterol level, these genes became not significant (/P/
>>        values larger). So either cholesterol is confounding factor
>>        which I should control , or these differential expressed genes
>>        control the cholesterol level which I should not adjust. I am
>>        not sure which assumption is right. I am thinking it should be
>>        the last one.
>>
>>
>>    I'm not sure I completely understand what you are saying here. It
>>    is conventional in this field to state that 'genes are
>>    significant', but that is an incomplete statement. The real
>>    statement is that the gene expression appears to be significantly
>>    differentially expressed between (e.g., treatment and control). So
>>    are you saying that the case vs control contrast is no longer
>>    significant if you add cholesterol level to the model?
>>
>>    If you add cholesterol level to the model, you have moved from an
>>    ANOVA model to an ANCOVA model in which you are fitting a linear
>>    model between the gene expression and cholesterol level, and
>>    allowing different intercepts between the different factor levels,
>>    but assuming equal slopes. If the contrasts between factor levels
>>    are no longer significant, that implies that the intercepts aren't
>>    different.
>>
>>    In this case, the cholesterol level 'explains' more about gene
>>    expression than the treatment. Whether or not the genes control
>>    the cholesterol level or the cholesterol level is affecting gene
>>    expression cannot be determined. But what can be said is that once
>>    you control for cholesterol level, the differences between the
>>    factor levels are no longer significant.
>>
>>    Also note that fitting a linear model (as compared to ANOVA) is
>>    trickier. For ANOVA, you really just assume that the distribution
>>    of the data for the different factor levels is relatively close to
>>    normal (or at least 'hump shaped'). With linear regression you can
>>    run into problems if your residuals are not correctly distributed,
>>    if you have high leverage data points, etc, which are not things
>>    you can check for thousands of models. In addition, you are making
>>    the assumption that the slopes of the regression lines are
>>    parallel, which might not always be valid. You may need to
>>    introduce an interaction term to allow for diverging slopes.
>>
>>    Best,
>>
>>    Jim
>>
>>
>>
>>        2012/4/4 Moshe Olshansky <olshansky@wehi.edu.au
>>        <mailto:olshansky@wehi.edu.au> <mailto:olshansky@wehi.edu.au
>>
>>        <mailto:olshansky@wehi.edu.au>**>>
>>
>>
>>           You are right.  My mistake. Sorry...
>>
>>        > Hi, Moshe,
>>        >
>>        > I still did not get it. How to use your method?
>>        >
>>        > lmFit<-(x,design) which x allow missing values, but design
>>        didn't.
>>        >
>>        > usually, I want to adjust batch effects, I will include it
>>        in design
>>        > matrix, i.e.
>>        >                case  control  batch
>>        >                  1        0           1
>>        >                   0       1           1
>>        >                   1       0           2
>>        > which x is expression matrix, and then I will get the
>>        differential
>>        > expressed gene between case and control with adjusting batch.
>>        >
>>        > In this case, I just want to simply use "weight" instead of
>>            "batch" in
>>        > design matrix. But unfortunately, you can't have missing values
>>           in design
>>        > matrix which I think it's very often if we have many samples.
>>        >
>>        > 2012/4/4 Moshe Olshansky <olshansky@wehi.edu.au
>>        <mailto:olshansky@wehi.edu.au>
>>        <mailto:olshansky@wehi.edu.au <mailto:olshansky@wehi.edu.au>**>>
>>
>>
>>        >
>>        >> Why do you want to include weight in the design matrix?
>>        >> It may be more reasonable to include weight in the linear
>>           model, i.e.
>>        >> expression ~ condition + weight and then your design matrix
>>           will have
>>        >> two
>>        >> columns (if there are two conditions): the first column will
>>           contain 0's
>>        >> and 1's depending on the condition (group) of the patient
>>        while the
>>        >> second
>>        >> one will be identically 1. And then the weights of the patients
>>           will be
>>        >> in
>>        >> the values (i.e. the second argument to lmFit) which I believe
>>           allows
>>        >> missing values.
>>        >> But this implicitly assumes that the weight (or some function
>>           of it -
>>        >> you
>>        >> can use any transformation you like) contributes additively
>>        (and
>>        >> linearly)
>>        >> to the expression (or it's logarithm).
>>        >>
>>        >> Moshe.
>>        >>
>>        >> > Hi,
>>        >> >
>>        >> > I am using Limma to get differential expressed genes between
>>           case and
>>        >> > control samples. In addition, I want to adjust the
>>        "weight" of
>>        >> patients
>>        >> by
>>        >> > including it in the "design" matrix. However, one weight
>>           value of a
>>        >> > patient
>>        >> > is missing, so I have a "NA" in design matrix. When I run
>>           lmFit, I got
>>        >> the
>>        >> > error message :Error in qr.default(x) : NA/NaN/Inf in foreign
>>           function
>>        >> > call
>>        >> > (arg 1).
>>        >> >
>>        >> > Can limma handle the missing value in design matrix or I
>>        have to
>>        >> remove
>>        >> > this sample ?
>>        >> >
>>        >> > Thanks,
>>        >> >
>>        >> > Chen
>>        >> >
>>        >> >       [[alternative HTML version deleted]]
>>        >> >
>>        >> > ______________________________**_________________
>>        >> > Bioconductor mailing list
>>        >> > Bioconductor@r-project.org
>>        <mailto:Bioconductor@r-**project.org <Bioconductor@r-project.org>>
>>        <mailto:Bioconductor@r-**project.org <Bioconductor@r-project.org>
>>
>>        <mailto:Bioconductor@r-**project.org <Bioconductor@r-project.org>
>> >>
>>
>>        >> > https://stat.ethz.ch/mailman/**listinfo/bioconductor<https://stat.ethz.ch/mailman/listinfo/bioconductor>
>>        >> > Search the archives:
>>        >> >
>>        http://news.gmane.org/gmane.**science.biology.informatics.**
>> conductor<http://news.gmane.org/gmane.science.biology.informatics.conductor>
>>        >> >
>>        >>
>>        >>
>>        >>
>>        >>
>>                  ______________________________**
>> ______________________________**__________
>>        >> The information in this email is confidential and intended
>>           solely for
>>        >> the
>>        >> addressee.
>>        >> You must not disclose, forward, print or use it without the
>>           permission
>>        >> of
>>        >> the sender.
>>        >>
>>                  ______________________________**
>> ______________________________**__________
>>        >>
>>        >
>>
>>
>>
>>                  ______________________________**
>> ______________________________**__________
>>           The information in this email is confidential and intended
>>        solely
>>           for the addressee.
>>           You must not disclose, forward, print or use it without the
>>           permission of the sender.
>>                  ______________________________**
>> ______________________________**__________
>>
>>
>>
>>    --     James W. MacDonald, M.S.
>>    Biostatistician
>>    University of Washington
>>    Environmental and Occupational Health Sciences
>>    4225 Roosevelt Way NE, # 100
>>    Seattle WA 98105-6099
>>
>>
>>
> --
> James W. MacDonald, M.S.
> Biostatistician
> University of Washington
> Environmental and Occupational Health Sciences
> 4225 Roosevelt Way NE, # 100
> Seattle WA 98105-6099
>
>

	[[alternative HTML version deleted]]

