If a variable y has (approximately) constant CV, then log(y) has
(approximately) constant variance. So, use the standard deviation of
the data.
begin included message ---
Hi, I have a microarray dataset from Agilent chips. The data were really
log ratio between test samples and a universal r
Thank you Peter.
John
From: Peter Langfelder
To: Bert Gunter
Sent: Tuesday, February 21, 2012 2:51 PM
Subject: Re: [R] "CV" for log normal data
>
> Good advice. But perhaps ?mad or some other perhaps robust plain old
> measure of spre
On Feb 21, 2012, at 22:44 , array chip wrote:
> Hi, I have a microarray dataset from Agilent chips. The data were really log
> ratio between test samples and a universal reference RNA. Because of the
> nature of log ratios, coefficient of variation (CV) doesn't really apply to
> this kind of d
>
> Good advice. But perhaps ?mad or some other perhaps robust plain old
> measure of spread?
The problem is not (lack of) robustness to outliers, the problem is to
find genes whose expression variation is small compared to (mean)
expression. Trouble is, Agilent throws the mean expression informat
Inline below.
On Tue, Feb 21, 2012 at 2:07 PM, Peter Langfelder
wrote:
> On Tue, Feb 21, 2012 at 1:44 PM, array chip wrote:
>> Hi, I have a microarray dataset from Agilent chips. The data were really log
>> ratio between test samples and a universal reference RNA. Because of the
>> nature of l
On Tue, Feb 21, 2012 at 1:44 PM, array chip wrote:
> Hi, I have a microarray dataset from Agilent chips. The data were really log
> ratio between test samples and a universal reference RNA. Because of the
> nature of log ratios, coefficient of variation (CV) doesn't really apply to
> this kind
6 matches
Mail list logo
