PART I - From Data import to Normalization in Microarray Analysis using in R (Part I)

PART II - From Genefiltering to Results storgae in Microarray Analysis using in R (Part II) - FARMS

I prepared the data from PART II. I think that data presentation is important for understanding the results in large data. I display clustering results through MeV software (free).

From PART II, I selected 212 genes using FARMS and genefiltering (Standard Deviation) at GSE4536 data.

# Open MeV software (version 4.9 - http://www.tm4.org/mev.html)
# File -> Load Data -> Single-color Array select -> mydata_GSE4536_xxx_xxx.txt. text file upload
# Load Annotation Data -> Automatically download -> Homo sapiens -> hgu133plus2
# Expression Table -> first row x first column values click -> Load Click!
# Clustering Click -> Hierarchial Clustering (HC) -> Tree Selection (check Gene Tree and Sample Tree) ->
Distance Metric Selection (Current Metric -> Euclidean Distance) -> Linkage Method (Complete linkage clustering) -> OK click!!

# For identifying significant genes between groups, I perform statistics, SAM analysis.
# Statistics click -> Significance Analysis of Microarrays (SAMs) click -> Two-class unpaired -> Cluster Selection (green - Group 1, blue - Group 2) -> check HC -> Tree Selection (check Gene Tree and Sample Tree) ->
Distance Metric Selection (Current Metric -> Euclidean Distance) -> Linkage Method (Complete linkage clustering) -> OK click!!
# We will show SAM graph and HC as below

# Next, I usually organize top gene list through Excel.