Hi,
I have normalized microarray data with me. For this normalized matrix, I have information file also which contains information about each sample, i.e. ethnicity, sex and age of each sample or subject. Now, I have been asked to provide result tables based on parameters, mentioned in infomation file.
Here, I am just seeking for help in context of result tables based on parameters. So, what results should I provide based on parameters? Below is the sample information file:
Sample Ethnicity Age Sex Condition
C1A Asian 50 M Malaria
C2A Asian 70 M Dengue
P1A Africa 60 F Malaria
P2B Australia 50 M Dengue
E5C American 38 F Malaria
Thanks
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written
by
mandar.bobade60 •
1 answer
What you are describing is a differential expression analysis. However, since you have multiple "factors" or groupings of samples, you will need a software package that is designed to cope with these multiple factors. The bioconductor limma package comes to mind, so I would suggest reading the fantastic limma User Guide. If this is your first time working with bioconductor or doing analysis, I highly recommend you find a local collaborator or bioinformatics person to help you as you work through your dataset.
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Here, what I am thinking is, analysis between multiple combinations, such as Male Vs Female, age above 60 Vs below 60, ethnicity Asian Vs rest or Ameriacal Vs rest. Is this right?
I hope you have more samples than that, having only 5 with so many combinations would make life difficult.
Firstly, you should just ask whomever asked you to provide the results what he/she wants rather than asking a bunch of people who can only infer it. Secondly, yes, presumably the person wants the interaction effect of disease and gender/age/ethnicity (separately, I should add). You can do that in limma. I assume that effects such as Male vs Female aren't important, since you're looking at disease.
Yeah, you r right. I have likewise 70 samples, I have just mentioned first 5. So in interaction studies, any hint how I can make groups for analysis.
Ah, 70 should be enough to look at some interesting effects :) If you're not familiar with limma, have a look through the vignette, particularly on how contrasts work, since you'll be making heavy use of them.