An Introduction to Options.
As a machine learning engineer, I have been learning and playing with deep learning for quite some time. Now, after finishing all Andrew NG newest deep learning courses in Coursera, I decided to put some of my understanding of this field into a blog post. I found writing things down is an efficient way in subduing a topic. In addition, I hope that this post might be useful to those who want to get started into Deep Learning.
What is a meta-analysis? As the name implies, a meta-analysis is an analysis of other people’s analyses o_O! when used correctly (in the context of a systematic review, for instance) meta-analysis is a powerful technique for understanding experimental effects. The great thing about meta-analysis is that it gets at the true effects that underlie probabilistic experiments (i.e., pretty much every experiment that isn’t physics). I think I have used the term “true” effect before, but all that I mean by that is the magnitude of the effect that we would be able to measure if we were able to collect data from an entire population. The gods might know what this effect is… but we mere mortals can only estimate it based on samples… and the better data we have, the more data we have, the better our estimates will be
In this blog we would use some of those techniques to reproduce a graphic from the Economist ( Most of the part of this blog has been taken from the Harvard Labs class of Introduction to R Graphics )
Below shows a general workflow for carrying out a GWAS prephasing and imputation using 1000GP phase3. In this guide, I will focus on the processing of GWAS imputation in a detailed manner.
I had been working on strand-specific paired-end reads from HiSeq lately and I had trouble mapping reads back to assembled transcripts using STAR as well as using RSEM to estimate transcript abundance.
Below shows a general workflow for carrying out a RNA-Seq experiment. In this guide, I will focus on the pre-processing of NGS raw reads, mapping, quantification and identification of differentially expressed genes and transcripts.
Linear models in practice