Does anyone have experience using machine learning algorithms to analyze RNA sequence data?
have a few questions:
1. format of sequence data. Can raw read counts be used, or is log normalized data preferred (or another form of data processing)?
2. my data set has about 26,000 genes, so wondering what % of the most variable genes I should include in the analysis. For example, should I start with the top 200 most variable? or top 1000? etc.
It is typical to use log normalized data, so if you already have it in that format that would be a good place to start.
A dataset will typically support a certain *number* of predictors rather than a fixed percentage. That, in turn, will depend on the size of your dataset (number of observations), the nature of the target variable and the method you will be using. So unfortunately no easy answers. If your dataset is sufficiently large, take advantage of dividing your data into three portions so you can try various numbers of genes without compromising your ability to assess the final performance of the models.
