Many ask us "Is this normalization correct?", but they are making a wrong question.
Normalization is the means (tools) to cancel artificial effects on variations. They are tools, so you can ask "Is the sequence of normalization/pre-processing steps suitable for my data and purpose?"
To answer to such a question, we need to know
- Purpose (What information do you want to extract from the data?)
- Assumption (How do you objectively expect the data distribute?)
- Observation (How the data distribute actually?)
The following movie only shows how to set on the software, but it does not for answering the intrinsic question. Please request an online support for this type of question.
- What are Processed Signals? Why do you turn signals into log ratios?
- How to interpret microarray data?
- Details of the preset normalization scenario for the RNA-Seq data.
- Why you shouldn't use Z-score Normalization on the gene expression data.
Finding a proper sequence of normalization and pre-processing.
Normalization is actually very tricky, because it heavily depends on characteristics of experimental data. That's why we think it can't be automated. Users must make decision to apply suitable normalization and pre-processing to each series. Subio Platform is designed to help your decision making by visual aid, which you can see what happens on data at each step.
Going through trials and errors is the best way to learn about omics data. You can recall pre-set, your saved or currently-applied scenario by one click. So you don't need to be afraid of collapsing data. Try as much as you can, and we're happy to help you via online support. It makes your understanding the data in depth.
Please take an Online Training for a full instruction of the data analysis.