Hello, I have a question about reading in data for use with the Limma package by using standard R functions like read.table as mentioned in chapter 4.1. The data I'm trying to use is an existing dataset from the DeRisi lab and will be used for educational purposses only. I've been working on a script to preprocess/ normalize the data using standard R functions after which I have a data frame containing the following columns (9 columns and 5969 rows): ORF (Identifier) Name (Gene name (optional)) T1 - T7 (normalized log intensities for each of the 7 arrays) Other then the above I have no information about the data so I cannot create a targets.txt file etc. Is there any way that I can use Limma to do the normalization and analysis on this data? I'm trying to write a short tutorial on how to use Limma with this data which I hope to finish on friday, so any help to point in the right direction is greatly appreciated. M. Kempenaar Bioinformatics Hanze University Groningen, the Netherlands [[alternative HTML version deleted]]

What are the actual data? Raw intensities or log2 ratios? You can either create an RGList object (if the data are raw intensities) or an MAList object (if they are log ratios). You could also create log ratios from the data and then create an MAList object. The problems comes if you only have data for one channel (red or green?!) or you only have log ratios (where would you get A from? We need more information Mick -----Original Message----- From: bioconductor-bounces@stat.math.ethz.ch [mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of m_kempenaar at planet.nl Sent: 01 October 2008 09:17 To: bioconductor at stat.math.ethz.ch Subject: [BioC] Need help using read.table to read in non-standard data Hello, I have a question about reading in data for use with the Limma package by using standard R functions like read.table as mentioned in chapter 4.1. The data I'm trying to use is an existing dataset from the DeRisi lab and will be used for educational purposses only. I've been working on a script to preprocess/ normalize the data using standard R functions after which I have a data frame containing the following columns (9 columns and 5969 rows): ORF (Identifier) Name (Gene name (optional)) T1 - T7 (normalized log intensities for each of the 7 arrays) Other then the above I have no information about the data so I cannot create a targets.txt file etc. Is there any way that I can use Limma to do the normalization and analysis on this data? I'm trying to write a short tutorial on how to use Limma with this data which I hope to finish on friday, so any help to point in the right direction is greatly appreciated. M. Kempenaar Bioinformatics Hanze University Groningen, the Netherlands [[alternative HTML version deleted]] _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor

You should take a look at the limma analysis of affymetrix and other single-channel designs, as this is what you have now basically. The examples in the userguide on this type of data do not need a targets.txt file. Jan > I have a question about reading in data for use with the Limma package by > using standard R functions like read.table as mentioned in chapter 4.1. > The data I'm trying to use is an existing dataset from the DeRisi lab and > will be used for educational purposses only. I've been working on a script > to preprocess/ normalize the data using standard R functions after which I > have a data frame containing the following columns (9 columns and 5969 > rows): > > ORF (Identifier) > Name (Gene name (optional)) > T1 - T7 (normalized log intensities for each of the 7 arrays) > > Other then the above I have no information about the data so I cannot > create a targets.txt file etc. Is there any way that I can use Limma to do > the normalization and analysis on this data? > > I'm trying to write a short tutorial on how to use Limma with this data > which I hope to finish on friday, so any help to point in the right > direction is greatly appreciated. >