Please go through following link. I hope you will get some clue for how to use R for microarray data analysis.
http://jura.wi.mit.edu/bio/education/bioinfo2007/arrays/array_exercises_1R.html
http://jura.wi.mit.edu/bio/education/bioinfo2007/arrays/array_exercises_2R.html
Please check this two links
http://www.bioconductor.org/help/workflows/oligo-arrays/
http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual
Also you can download specific userguide for each package from bioconductor website
Hi Shuchi,
Bioconductor is a right choice. Probably is not your case, but If you have to analyse microarray data that have been published on GEO, I also suggest you to visit GEO2R.
http://www.ncbi.nlm.nih.gov/geo/geo2r/
GEO2R is an interesting tool that can help you to compare two or more groups of samples for differentially expressed genes indentification. Moreover GEO2R gives you the possibility to produce in real time an R script that you can import and execute in R, to obtain expression data sets, pvalues from ANOVA analysis, of all the groups that you selected from the GSE.
Also if the produced R script is oriented to published GSE data in GEO, you can modify the R script to adapt it to your personal data. I found it an interesting exercise to do in R environment, for microarray analysis.
Go to this link. this will definitely help you. go to the BIOCONDUCTOR PART in the manual that is what you looking for microarray data analysis ..
http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual
Hi Luca,
Let me if in any GSE only one sample is present means no replicates are there. then how can I comaper 2 sample using GEO2R.
Hi Sanchita,
Unfortunately, independently from GEO2R, there are no possibilities to obtain a p-value, when you have non replicates, so, you can compare 2 sample only considering their Fold Change. This has importance in terms of biological significativity, but at least in my experience, is highly not-recommended select other Gene Expression Profles with more replicates, to statistically filter your data.
Dear Luca,
I was wondering if you could help me. My question is how many controls can I compare to my samples in GEO2R? I mean if I have 10 samples should I compare them with just 10 controls or It is possible to add more samples, if the study provided. Another thing is, for increasing the significancy (adjusted p-Value) , is it possible to select controls or samples that are more alike, or I should select them randomly?
I would appreciate if you could answer my questions.
Many thanks
The best way to learn how to analyze microarray data, dna sequence data, or any biological data by using R Program or any other software is to practicing using the software scripts. It might take you a few hours or even a few days. The more you practice, the better you will understand the software and understand the data. Then you will be able to make the process and data your own. I hope this helps and best of luck in your professional endeavors.
Dear Dr. Shuchi Smita,
You can use either GEO2R analysis tool directly from the geo accession link of the specific experimental microarray data or siggenes package from the RStudio. Both are good for the identification of the differentially expressed genes in microarray data.
Hello,
Does it depend on what type of data you are using?. I think based on your affiliation, you are going to use microarray related to plants. First, you need to check whether your dataset or obtained public datasets are having enough info for differential gene analysis or not.
If everything accessible you can perform normalization and DEG analysis using R Script (“Limma”) or GEO2R. If not accessible you can request from the respective authors. Scripts are available respective package tutorial.
You can double-check the packages for DEGs analysis because “limma” and GEO2R mostly expressing gene sets related to the pathology of diseases. There is a chance of misinterpretation if you are using different datasets.
- Badges
- Science topic
- Similar topics
- Filing