finding most highly transcribed genes - ranking, sorting and subsets?

Thanks so much Martin,

This method is definitely more straightforward.  And you are right I don't
think I was doing anything wrong before. However, I thought that rank, would
rank the highest 1st, however after looking at the results using your
methods, I realized it ranks the lowest number 1.  So I modified it for
rank>18500.  And now I'm getting 300 rows for which the intensity is
consistenly high.

However, I am still laking some information.  For the results I can get a
matrix of 300 rows and the corresponding intensities (from m) or rank (from
h), but what I really want is the name of the original row, which
corresponds to a specific spot on the array).

I did msubset<-m[hrows,] and as mentioned I just get the rows numbered
1-300, while I want to essentially pickout the 300 rows from the original
19,000 rows maintaing the original row designation as it corresponds to a
specific gene.

Thanks again for any suggestions,

Alison

-----Original Message-----
From: Martin Morgan [mailto:mtmorgan at fhcrc.org] 
Sent: Thursday, December 06, 2007 4:06 PM
To: alison waller
Subject: Re: [R] finding most highly transcribed genes - ranking, sorting
and subsets?

Hi Alison --

I'm not sure where your problem is coming from, but R can help you to
more efficiently do your task. Skipping the bioc terminology and data
structures, you have a matrix

m <- matrix(runif(100000), ncol=10)

you'd like to determine the rank of values in each column

r <- apply(m, 2, rank)

identfiy those with high rank

h <- r < 500

and find the rows for which the rank is always high

hrows <- apply(h, 1, all)

you can then use hrows to subset your original matrix (m[hrows,]) or
otherwise, e.g., how many rows with high rank

sum(hrows)

[1] 0

or perhaps the distribution of the number of columns in which high
ranking genes occur.

table(apply(h, 1, sum))

   0    1    2    3    4 
5996 3132  765  100    7 

Martin

"alison waller" <alison.waller at utoronto.ca> writes:

Hello,

 

I am not only interested in finding out which genes are the most highly

up-

or down-regulated (which I have done using the linear models and Bayesian
statistics in Limma), but I also want to know which genes are consistently
highly transcribed (ie. they have a high intensity in the channel of
interest eg. Cy5 or Cy3 across the set of experiments).  I might have

missed

a straight forward way to do this, or a valuable function, but I've been
using my own methods and going around in circles.

 

So far I've normalized within and between arrays, then returned the RG
values using RG<-RG.MA, then I ranked each R and G values for each array

as

below.

rankRG<-RG

rankRG$R[,1]<-rank(rankRG$R[,1])

rankRG$R[,2]<-rank(rankRG$R[,2]) .. and so on for 6 columns(ie. arrays, as
well as the G's)

 

then I thought I could pull out a subset of rankRG using something like;

topRG<-rankRG

topRG$R<-subset(topRG$R,topRG$R[,1]<500&topRG$R[,2]<500&topRG$R[,5]<500)

 

However, this just returned me a matrix with one row of $R (the ranks were
<500 for columns 1,2, and 5 and greater than 500 for 3,4,and 6).  However,

I

can't believe that there is only one gene that is in the top 500 for R
intensitiy among those three arrays.

 

Am I doing something wrong?  Can someone think of a better way of doing
this?

 

Thanks

 

Alison

 

 

******************************************
Alison S. Waller  M.A.Sc.
Doctoral Candidate
awaller at chem-eng.utoronto.ca
416-978-4222 (lab)
Department of Chemical Engineering
Wallberg Building
200 College st.
Toronto, ON
M5S 3E5

  

 


	[[alternative HTML version deleted]]

Thanks so much Martin,

This method is definitely more straightforward.  And you are right I don't
think I was doing anything wrong before. However, I thought that rank,

rank the highest 1st, however after looking at the results using your
methods, I realized it ranks the lowest number 1.  So I modified it for
rank>18500.  And now I'm getting 300 rows for which the intensity is
consistenly high.

However, I am still laking some information.  For the results I can get a
matrix of 300 rows and the corresponding intensities (from m) or rank

h), but what I really want is the name of the original row, which
corresponds to a specific spot on the array).

I did msubset<-m[hrows,] and as mentioned I just get the rows numbered
1-300, while I want to essentially pickout the 300 rows from the original
19,000 rows maintaing the original row designation as it corresponds to a
specific gene.

Thanks again for any suggestions,

Alison

-----Original Message-----
From: Martin Morgan [mailto:mtmorgan at fhcrc.org] 
Sent: Thursday, December 06, 2007 4:06 PM
To: alison waller
Subject: Re: [R] finding most highly transcribed genes - ranking, sorting
and subsets?

Hi Alison --

I'm not sure where your problem is coming from, but R can help you to
more efficiently do your task. Skipping the bioc terminology and data
structures, you have a matrix

m <- matrix(runif(100000), ncol=10)

you'd like to determine the rank of values in each column

r <- apply(m, 2, rank)

identfiy those with high rank

h <- r < 500

and find the rows for which the rank is always high

hrows <- apply(h, 1, all)

you can then use hrows to subset your original matrix (m[hrows,]) or
otherwise, e.g., how many rows with high rank

sum(hrows)

[1] 0

or perhaps the distribution of the number of columns in which high
ranking genes occur.

table(apply(h, 1, sum))

   0    1    2    3    4 
5996 3132  765  100    7 

Martin

"alison waller" <alison.waller at utoronto.ca> writes:

Hello,

 

I am not only interested in finding out which genes are the most highly

up-

or down-regulated (which I have done using the linear models and Bayesian
statistics in Limma), but I also want to know which genes are

highly transcribed (ie. they have a high intensity in the channel of
interest eg. Cy5 or Cy3 across the set of experiments).  I might have

missed

a straight forward way to do this, or a valuable function, but I've been
using my own methods and going around in circles.

 

So far I've normalized within and between arrays, then returned the RG
values using RG<-RG.MA, then I ranked each R and G values for each array

as

below.

rankRG<-RG

rankRG$R[,1]<-rank(rankRG$R[,1])

rankRG$R[,2]<-rank(rankRG$R[,2]) .. and so on for 6 columns(ie. arrays,

well as the G's)

 

then I thought I could pull out a subset of rankRG using something like;

topRG<-rankRG

topRG$R<-subset(topRG$R,topRG$R[,1]<500&topRG$R[,2]<500&topRG$R[,5]<500)

 

However, this just returned me a matrix with one row of $R (the ranks

<500 for columns 1,2, and 5 and greater than 500 for 3,4,and 6).

I

can't believe that there is only one gene that is in the top 500 for R
intensitiy among those three arrays.

 

Am I doing something wrong?  Can someone think of a better way of doing
this?

 

Thanks

 

Alison

 

 

******************************************
Alison S. Waller  M.A.Sc.
Doctoral Candidate
awaller at chem-eng.utoronto.ca
416-978-4222 (lab)
Department of Chemical Engineering
Wallberg Building
200 College st.
Toronto, ON
M5S 3E5

  

 


	[[alternative HTML version deleted]]