Though many might not notice, normalization and pre-processing are the most critical parts of data analysis because this process makes hidden things in complex data readable.
The following affect the omics data significantly.
- System/Platform, solution kit, laboratory, experiment date
- Total number of reads for RNA-Seq, quantity and quality of input RNA
- Cell type, organ, cell sorting
- Biopsy or fixed sample or culture cell
- Growth protocol, infection, and contamination
- Data quantification method
Thus, the characteristics and quality of omics data vary so much that it is practically hard to define a fixed analysis process for automation, even if it is technically possible. The analysts must make decisions case by case.
We designed the Subio Platform so that users can visually understand how each process worked on the data. Users can tentatively adjust the options; such trials and errors give you a much deeper understanding of the data. That works in the subsequent analysis in choosing methods and interpreting results. Without such notions, the automatic analysis tends to mislead conclusions that can bring drawbacks.
Finding a proper sequence of normalization and pre-processing.
Subio Platform offers the following normalization scenarios. You can pick one as a template and adjust the options to make it fit the characteristics of the data.
- Expression Microarray
- RNA-Seq (Count)
- RNA-Seq (FPKM, TPM, RPKM)
- Methylation Beta Values
- Pre-normalized Log2 Data - Select this if you have pre-normalized log2 ratio data.
- Nothing - Select this to clear all blocks.
Novice users might feel unconfident about the settings, but we will support them through online training so that they can learn the whole analysis process for the data they want to analyze.
Related Topics
- Details of the preset normalization scenario for the RNA-Seq data.
- Don’t trust TPM/FPKM/RPKM too much. They don’t promise to cancel the systematic error of RNA-Seq data.
- What are Processed Signals? Why do you turn signals into log ratios?
- Why you shouldn't use Z-score Normalization on the gene expression data.
- How to interpret microarray data?