I am an absolute novice when it comes to analyzing microarray data. I want to troll through the NCBI microarray database and look at a multitude of experiments where my gene of interest was knocked-out/down or inhibited and see its downstream effects and compare them across experiments. All I want is to see is which genes were up and down regulated downstream.
However, my excursion through the database has been an epic failure. I am looking for software to analyze the data and more than that, I just want to figure out how to OPEN the NCBI files. I know nothing about programming and am just looking for a "for Dummies" version of how to do this. Thank you.
I really just want to see which genes papers' authors concluded were up and down regulated.
I don't think you can easily do that. You can get the raw or normalised data and than will need to decide for yourself what cut-off indIcates differential expression (different studies will use different criteria; common criteria might be p-value =2).
Remember that experiments may not be truly comparable due to different experimental conditions, technical variation and normalisation.
MeV (Multiexperiment Viewer) is a nice, user-friendly piece of software you could try: http://www.decodon.com/files/doc/MeV_Manual_4_0.pdf
Its not that straight forward, its takes a degree of bioinformatic knowledge to make sense of the raw expression data. When you would want to look for the same gene in different datasets, you need to keep in mind that these datasets come from different conditions and even different arrays. You will need to normalise them even before you could use the data. My suggestion is that you get in touch with a bioinformatician.
can any bone teach me how to extract files from ncbi geo, please suggest me easy way for getting files and normalization of files.
- Hi all,
- You can follow this paper where they use Python to do such a meta-analysis.
- "A Kinetic Analysis of the Auxin Transcriptome Reveals Cell Wall Remodeling Proteins That Modulate Lateral Root Development in Arabidopsis".
- See section- "Comparison of These Results with Previously Published Microarray Results".
- They have not explicitly mentioned in the methods but if you mail them I think they will share it with you. Take help of your bioinformatician to understand and make sense of the analysis.
- Good luck,
- Debatosh Das.
- Graduate student
- Utrecht University,
- The Netherlands.
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- Science method
- Similar topics
- Mathematics
- Statistics
- Statistical Techniques
- Meta-Analysis