Please check out the new Shiny App R-package called BatchQC, which will let you easily do what you want. You can adjust for Batch using ComBat or SVA and compare the results, all with a click of a few buttons.
Please check out the following application note in Bioinformatics journal that we just published:
“BatchQC: interactive software for evaluating sample and batch effects in genomic data” Solaiappan Manimaran, Heather Marie Selby, Kwame Okrah, Claire Ruberman, Jeffrey T. Leek, John Quackenbush, Benjamin Haibe-Kains, Hector Corrada Bravo and W. Evan Johnson
BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. BatchQC can also apply existing adjustment tools and allow users to evaluate their benefits interactively.
BatchQC is available from Bioconductor at the following link:
From: Ewelina Dratkiewicz [bioc] [mailto:email@example.com]
Sent: Monday, September 5, 2016 8:27 AM
Subject: [bioc] What type of data normalization for multiple microarrays?
Activity on a post you are following on support.bioconductor.org<https: support.bioconductor.org="">
User Ewelina Dratkiewicz<https: support.bioconductor.org="" u="" 11421=""/> wrote Question: What type of data normalization for multiple microarrays?<https: support.bioconductor.org="" p="" 86786=""/>:
I'm new to R and I have a little problem with data analysis. I was asked to create a correlation plot for 2 genes expressed in melanoma cells. I downloaded data from GEO (for 14 data sets, 2 types of similar microaarays), made Expression Sets, normalized with RMA, substracted data for 2 genes and compiled it into one matrix. To every sample I assigned two traits - cell type (normal, primary, metastasis and so on) and number of data set it was substracted from. Then I created simple plot to observe how my data looks like (without multiple sets normalization Spearman's correlation coefficient is above 0,5 with really low p-value). Now I would like to remove any differences between data sets - if I understood it correctly I should remove batch effect with e.g. ComBat. And here's my question - should I assume one batch equals one data set (or one data set contains more batches (differences in data collection dates and so on))? Is ComBat or SVA the best method for this particular case? And should I perform this normalization on whole data matrices (how?) and extract data for my 2 genes of interest?
I'm sorry if my post is a little chaotic but I'm still learning how to use R. I will be really greatful for your advice.
Post tags: normalization, microarray, combat
You may reply via email or visit What type of data normalization for multiple microarrays?