Now my question is: are there any alternative packages to limma for analysis of differentially expressed genesin microarray datasets? I would like to compare results for the differentially expressed genes I found with limma. I did some research but only found alternative packages to limma for RNA-seq data and not for microarray data.
By my understanding, the alternatives to limma for RNA-seq, such as edgeR and DESeq, were created because limma's statistical model was not (at that time) a good fit for count data. Do you have concerns about limma's statistical model for your data, or is there a specific statistical method you're looking to apply and you're hoping to find a package to do it?
thank you for your answer. No, I don't have concerns about limma' statistical model for my data, the results for the DEGs with limma are in accord with the gene expression described in the original publication Prediction of antibiotic resistance by gene expression profiles available under https://www.nature.com/articles/ncomms6792.
However, the analysis with limma revealed some additional, possibly interesting genes in this dataset and I thought about using a different package/software/method to see if these results are reproducible.
personally i would not consider anything better than limma for the analysis of microarray data (not only-)-this has been proven in numerous scientific published papers and pipelines-rather than your approach of using alternative R packages, you could focus on the interpretation of your DE list. Does functional enrichment of your DE genes provides "sensible results" regarding your biological question ? Or for instance, have you performed any literature mining to search for any similar transcriptomic analyses that have answered "similar experimental designs ? Also in parallel, you could create some additional plots, like a heatmap to inspect the expression pattern of your DE genes (and lots of more...)
Nevertheless, i could for instance mention the samr R package and cyberT test as alternatives, but i clearly think that limma provides the best choise.
thank you for your answer. As I said in my answer to Ryan, the results for the DEGs with limma are in accord with the gene expression described in the original publication. However, there were some additional, possibly interesting genes in the results and I wanted to see if these results are reprducible with other packages/software/methods. Thank you for your suggestions samr and cyberT.
if you are interested in two group comparisons only, the st CRAN package implements various alternative variance shrinkage estimators, the CAT score even takes correlations between the genes into account:
By my understanding, the alternatives to limma for RNA-seq, such as edgeR and DESeq, were created because limma's statistical model was not (at that time) a good fit for count data. Do you have concerns about limma's statistical model for your data, or is there a specific statistical method you're looking to apply and you're hoping to find a package to do it?
Hello Ryan,
thank you for your answer. No, I don't have concerns about limma' statistical model for my data, the results for the DEGs with limma are in accord with the gene expression described in the original publication Prediction of antibiotic resistance by gene expression profiles available under https://www.nature.com/articles/ncomms6792.
However, the analysis with limma revealed some additional, possibly interesting genes in this dataset and I thought about using a different package/software/method to see if these results are reproducible.