Intro

DNA Microarray

Affymetrix GeneChip and Terminologies

Workflow

What does the intensities on Affymetrix GeneChip Array mean?

Normalization methods

Fetching data: download .CEL files

Read .CEL files

RMA normalization

Retrieve normalized expression data

Map probe IDs to gene symbols

Downloading the data

Getting started

Installation of packages

Loading libraries in R

Setting working directory

Copy and normalize data

Write normalized expression values into a text file

Annotation of probes

Combining all data into one file

Conclusion

Intro

Data set

Annotation file

Setting up working directory

Normalization

Write expression data

Annotating the data

Combine annotation and provided data

Combining data

Conclusion

Data download

Import the data

Edit sample information

Edit parameters

Select Flag Information

Scale options

Preparation for Analysis

Create a genome annotation table

Import the Genome

Organize the chromosomes

Microarray Analysis

Install the Package from Github

Description of the study

Differential Expression Analysis

Fva Labels Function

Indices To Extract the Zeros and Ones from

Dummy Coding

Design Matrix

Empirical Base

Summarize

Welcome back

Lecture overview

A few words about project planning

Common questions in a gene expression experiment

What is a microarray

The microarray workflow and basic data structure

Common issues with microarrays

The microarray workflow and bioinformatics

Normalization of microarrays due to noisy data

microarray normalization (Mean centering, Standard score and Quantile normalization)

Light intensity ratio data and Log2 transformation

Hypothesis testing on microarray data

Difference between likelihood and probability

Single and double sided Student's t-test

Two-sample t-test, differences between groups

Intro

Dataset overview

Creating groups

Filtering the data

Working out quantiles

What is GEO?

Quick tour of GEO

Retrieving the data

Download GEO data

Summary function

Sample info file

Differential expression analysis

How to define a gene as expressed?

Statistical analysis

Volcano plot

1. GEO のトップページから口腔粘膜のトランスクリプトームを測定したデータセットを探す

2. 各サンプルを比較したい 2 群に分ける

3. データのクオリティを調べる

4. 2 群間で異なる発現パターンを示した遺伝子を確認する

5. P 値の計算方法を変更する

6. プローブ ID から任意の遺伝子の発現を確認する

7. これまでの操作を R のスクリプトで表示する.togotv#DBCLS#bioinformatics #togotv#DBCLS#bioinformatics

Heatmap demonstration

Loading data

Summary statistics

Mapping ids to genes

MA plot

Exporting data as PDF

Differential gene expression analysis

P-value filtering

Filtering genes

Plotting the gene expression map

Intro

Requirements

Set up RStudio for Analysis

Read data in R

Get metadata using GEOquery package

dplyr: select(), rename(), mutate()

Reshape data: gather()

Join data: left_join()

dplyr: filter(), group_by(), summarize(), arrange()

Advantages of the limma package for differential expression

Empirical Bayes method

Getting started

Design matrices

Standard limma pipeline

Building and testing a model

Intro

What information can be derived from gene expression data?

Mapping and assembly

Normalization methods

Multiple testing issues

Correcting p-values for multiple testing

Comparing most popular Differential Expression Analysis tools

Data exploration

Preliminary analysis tools

Conclusion

Pre-requisites

Bioconductor landing pages

Series records

Setting a download path

Understanding a bioconductor object

Creating a summarized experiment object

What is column metadata?

Bioconductor classes

Accessing metadata

Getting more information about the assay matrix