Something about Microarray-based expression analysis of specifical genes

Question

[Ginsea Chen]

Hello everyone.

I have about 600 genes of a superfamily, Now I tried to analyze their expression pattern through bioinformation method, many papers told me that Microarray data can be used to find genes expression characteristics in different tissues but the methods which they described were not clear and I don't know how to do it.

I pasted the correspondent method of one paper here:

Microarray-based expression analysis of ZmPK genes

To analyze the expression pattern of the ZmPK genefamily among different organs and development stages,transcriptome data of the genome-wide gene expressionatlas of maize inbred line B73 were obtained from the publicly available database PLEXdb with the accessionnumber ZM37 (BarleyBase/PLEXdb). This microarraywas made by NimbleGen, containing 80,301 probe setsto profile transcription patterns in 60 distinct tissuesrepresenting 11 major organ systems of inbred line B73.The maize tissues/organs and developmental stagesselected for microarray analysis include germinatingseed, root, whole seedling, stem and shoot apicalmeristem, internodes, cob, tassel and anthers, silk, leaf,husk and seed. The data were normalized using a robustmulti-chip average (RMA) algorithm. Log2-trans-formed expression values were loaded into R (v15.2)and Bioconductor for expression analysis (http://www.bioconductor.org/). Limma, a software used to analyze gene expression data, was used for data processing, and heat maps representing log2-transformed probe intensities were generated with the gplots package.

If you some suggestion, please tell me .

I am a newer of bioinformatics, I don't need detail pathway, I just need crude flows such as what packages and parameters used.

Thank you!

Answer 1

[Manvendra Singh]

I can understand the pain of being newer, We need to read lot.

You can visit this site to get how to install bioconductor packages in R

90% of all studies using Affy chips will use RMA for normalization. RMA (Robust Multi-array Average) was developed in the Speed Lab at UC Berkeley (Irizarry et al.). Variants such as gcRMA are also available.

Three prime Affymetrix Arrays (older ones)

Normalizing older Affymetrix arrays is EASY!!! This methods and technology are very mature.

once you have data downloaded and packages installed then

# load the affy library
library(affy)

# Read in the CEL files in the directory, then normalize the data
data <- ReadAffy()
eset <- rma(data)

# Finally, save the data to an output file to be used by other programs, etc (Data will be log2 transformed and normalized)
write.exprs(eset,file="data.txt")

#### Now once data is normalized then you can work on the normalized matrix or you can go for log2Fold change (which is in last sentence in your question)

which is very easy and given here

HTH

Answer 2

[orzech_mag]

Hello!

If need just a R codes for such an analysis you can find it here or in Limma

User Guide available @ Bioconductor page.

Similar study was performed here