Installed postfix, but no qshape? (CC)

Here’s a simple one…
I installed a new FC12, and postfix, using “yum install postfix”. Afterward, after configuring main.cf, postfix ran fine through our simple testing, so I put it in service on a limited basis. However, once we started using it, qshape failed, with an error indicating it was not found.
It took me a couple of hours of my poor google skills to find the answer, so hopefully, if you find yourself in the same pickle, you can use this and it will help you.
First, separately install all the perl packages with this command:
“yum groupinstall perl development”  once you have that all done, (and here’s the magic) run:
“yum install postfix-perl-scripts”.
I know, qshape is *supposed* to be installed with postfix.  Only it wasn’t. and it took me all morning to figure out how to get it in there….
…..It worked for me.

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Process NIH GEO GSE data by geoQuery

How to get an Expression value table of a GSE* file from GEO website.

for example: GSE33147

>g = getGEO("GSE33147")

......

it may download a series data matrix file: GSE33147_series_matrix.txt.gz
then load the dat again:
>g = getGEO(filename="GSE33147_series_matrix.txt.gz")

check the data

>class(g)

get the ExpressionSet:

> e = as(g, "ExpressionSet")

get the data table
> f = exprs(e)

save:
> write.csv(f, file="***")

load the group gene names that you want: (assume you only want part of them)
the names are stored in file "top60.csv"
>genes = read.csv("top60.csv",header=T)

the genes are factors, we need change them to character,
> cgenes = as.character(genes[,1])               //the first column.

>

Processing Microarray data by using R and Bioinductor

Load all the .CEL.gz file from a folder:

>library(affy)
>data <- ReadAffy()

## For Affymetrix data, there is no concept of RAW data.
See page 72 of DNA microarray data analysis using Bioconductor.

Now get the RMA data
> data.rma <- rma(data)

or you can use the following if the size of data is too large.

>data.rma <- justRMA(data)


change the RMA result to expression values
data.e <- exprs(data.rma)

save the data.e
write.csv(data.e,file="data.csv")

##now process the data and map probe ID to gene ID
##by any script language ....such as python


now load the processed data to R again:
d <- read.table("processed_data.csv",header=T,sep=",")

now d is a string matrix, make it to float

df <- data.frame(d,row.names=1)    ## use the first column as name

calculate the mean and append to df

df$mean <- rowMeans(df)

ranking

dfr <- df[order(-df$mean),]

save the highest 60 to a .csv file

write.csv(dfs[1:60,],file="d60.csv",sep=",")