This tutorial illustrates the entire workflow of microarray data analysis, from data import to biological interpretation, for wet researchers in life science fields. Those who don't know well about the microarray data analysis tend to believe that there is a right way and want to get the result as the output of the proper method. So, we think GSE97918 is a good example to learn that data analysis is not like that.
The way of data analysis is always arguable. There must be multiple ways because the analyst must make assumptions on the data subjectively. And without knowing the premises, the interpretation must go wrong.
This is why you worth re-analyze other researchers' data sets by yourself. You can get much more knowledge and insights, or even disputable conclusion from the reanalysis on your own rather than merely reading the paper.
Please learn how to get data files that you need for analysis and to integrate different types of information by importing them into Subio Platform.Microarray Data Analysis Tutorial (01) - The Preparation
In the next movie, you will see the quality issue in the real experimental data. The way shown in the movie is not only the right way. You can differently analyze this data on different assumptions.
Please learn the point on the decisions and think about how you will handle this data set.
Microarray Data Analysis Tutorial (02) - The Normalization & Pre-processing
Please learn what's the point of decisions on the normalization and pre-processing. And understand the effect of those operations for proper usage.
Microarray Data Analysis Tutorial (03) - Filtering & Extracting Differentially Expressed Genes (DEGs)
Applying analytical methods without considerations can lead to erroneous conclusions. Please learn how to grab the characteristics of the data and choose adequate methods.
Microarray Data Analysis Tutorial (04) - Multivariate Analysis & Biological Interpretations
PCA and clustering are useful to summarize the data set. But such statistical methods won't give you the biological answer. In order to make biological discoveries, you have to dive into the sea of data. This is the most difficult part of microarray data analysis. You would't have time to waste time on other phases like statistical analysis.