发信人: feir (菲儿), 信区: Biology
标 题: Re: Also microarray questions
发信站: Unknown Space - 未名空间 (Tue Nov 23 16:30:39 2004) WWW-POST

First of all, if you could find a biostatistician in your department or your
school you can let him/her do the analysis for you. All that you are asking
is standard microarray data analysis.

You should use the p-values instead of fold changes in defining differentially
expressed genes. What puppeteer mentioned (0.05/N) is called Bonferroni
adjustment for multiple comparison, which is the most conservative way
possible. Other common and less-conservative way is FDR (false-discovery
rate) adjustment. Based on my experience, if you have any gene that's
significant using Bonferroni you'll have dozens of significant genes using
FDR.

Another important thing is what algorithm you used to pre-process your data.
If you used Affymetrix's MAS 5 you probably want to get rid of all the genes
whose average expression are too low (say 16 or 32) because MAS 5 is not great
at processing the low-expressed genes so they are likely to be an artifact
when they are significant. RMA is a little better on that issue but RMA tends
to underestimate the fold changes.

One more thing you can try is to log-transform your data before you do the T
test comparison, which is almost a standard in microarray analysis. You may
get more signficant genes by doing that because your data would be less
skewed.

After you get the gene list you can put the probe-set names into EASE (very
simple, just copy-paste), choose the correct chip name in EASE and run it.
EASE is a freeware in recgonizing gene themes. Other things you could do
include making some pretty clustering pictures, building a prediction model,
matching your list with other lists people have generated before, etc.


【 在 dentsu (xixi) 的大作中提到: 】
: I had done microarray and gotten the data in excel sheet. My design is
simple:
: comparison of differential gene expression in the infected cell line and
: uninfected cell line. I replicate four times for each of them. The number of
: genes shown difference is as following:
:
: 1. Based on P value:
: 10-9 ~ 10-5: 9 genes
: 10-5 ~ 10-4: 32 genes
: 10-4 ~ 10-3: 207 genes
: 10-2 ~ 0.05: 631 genes
:
: 2. Based on the fold:
: >2: 8 genes
: 1.5 ~ 2: 40 genes
: 1.0 ~ 1.5: 507 genes
:
: My questions are:
: 1.Should I use fold or P value to define the differential expression? If use
P
: value, usually what value can be used as threshold? I found someone use
10-4.
: Is P<0.05 too unstrict for microarray data?
:
: 2.How to classify those differential genes by their function? Should I use
: software to do it or just classify them one by one based on its description
in
: the excel spread sheet? If software, can I input those data directly from
the
: excel? What is the simple and most popular software to do it?
:
: 3.How can I write a paper for publication only based on the microarray data?
:
: I am new for microarray although I know how it works. Since there is no
expert
: to get instruction in my department, I make little progress after I had
spend
: lots of time on those data. Hope here I can get good advises. Thank you.
:
:




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※ 修改:·feir 於 Nov 23 16:30:39 修改本文·[FROM: 140.163.]
※ 来源:．Unknown Space - 未名空间 mitbbs.com．[FROM: 140.163.]