RNA-seq

In this project, I elaborate an analysis pipeline for RNA-seq data through the creation of appropriate functions called from a single main script .

The aim is therefore not to use functions from the most famous NGS data analysis packages but to create custom ones in order to work at a low level on the primitives.

Each step of the pipeline will be represented by a different function.

Pipeline scheme:

• Data normalization
• Selection of differentially expressed genes
• Biological interpretation with functional annotation
• Clustering Expression Profiles (k-means)

DATA

The data reproduce an experimental design in which the dynamics of beta cells gene expression is monitored under two conditions:

• activation of insulin secretion ("INS" samples)
• inhibition of insulin secretion ("CTRL" samples)

The data are stored in the file matcount_1095199.RData, matrix 10000x78:

N = 10000 genes monitored

M = 13 time instants

3 biological replicates for each temporal instant

where:

rownames(matcount) = EntrezID of the monitored genes (http://www.ncbi.nlm.nih.gov/gene)

colnames(matcount) = CONDITION.TIME.REPLICATES

CONDITION = {"CTRL","INS"}

TIME = {"T0","T1",...,"T12"}

REPLICATES = {"REPL1","REPL2","REPL3"}

It is possible to use this viewer for a better visualization of the notebook: https://nbviewer.jupyter.org/github/lorrandal/RNA_seq/blob/master/RNA_seq.ipynb