[R] calculate Euclidean distances between populations in R with this data structure

[Ding, Yuan Chun]

Hi Sarah,

Thank you very  much for your quick response.

I checked the dist() function. It calculate distance between two samples with a number of variables.

   Variable1 variable 2 variable 3 variable4 ....
X      3       5           6            7 
Y      4       8           9            10

So it is easy to calculate distance between x and y.

But in my study, X is a group with 20 samples and y is another group with 30 samples, so I need to calculate distance between x group and y group.

I think I need to calculate a mean for each group, then use dist() function.  I tried to find a R package to do it.

Thanks,

Ding

-----Original Message-----
From: Sarah Goslee [mailto:sarah.goslee at gmail.com] 
Sent: Thursday, September 04, 2014 4:49 PM
To: Ding, Yuan Chun
Cc: r-help at R-project.org
Subject: Re: [R] calculate Euclidean distances between populations in R with this data structure

I'd probably start with ?dist

Sarah

On Thu, Sep 4, 2014 at 4:10 PM, Ding, Yuan Chun <ycding at coh.org> wrote:
>
>
>
> I want to calculate Euclidean distance between 12 populations, in each population there are 20 samples and each sample is measured for 100 genes (these are microarray data; the numbers here are just examples).
> The equation I found is:
> distance = sqrt{[sum(Average of xi -average of yi)^2] /n }, i=1 to n; 
> where xi and yi are the expression of gene i over two populations with p and q samples (x1, x2,...,xp), (y1, y2,...,yq), n is the number of genes.
> part of data are pasted below
> row.names pop1.1    pop1.2  pop1.3  pop1.4  pop2.1  pop2.2  pop2.3  pop2.4
> 7A5     5.38194 4.06191 4.88044 5.60383 6.23101 6.53738 4.80336 5.86136
> A1BG    5.15155 4.29441 4.59131 4.90026 4.62908 4.48712 4.73039 4.46208
> A1CF    4.22396 4.14451 4.41465 3.93179 4.89638 4.66109 4.20918 4.48107
> A26C3   12.1969 12.4179 10.9786 11.7659 11.405  11.7594 11.1757 11.8128
> How might one calculate these distances in R with this data structure?
>
>
> Thanks,
>
> Ding
>

--
Sarah Goslee
http://www.functionaldiversity.org


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