Molecular data analysis using R

TABLE OF CONTENTS

1 Introduction to R statistical environment

2 Simple sequence analysis

3 Annotating gene groups

4 Next-generation sequencing: introduction and genomic applications

5 Quantitative transcriptomics: qRT-PCR

6 Advanced transcriptomics: gene expression microarrays

7 Next-generation sequencing in transcriptomics: RNA-seq experiments

8 Deciphering the regulome: from ChIP to ChIP-seq

9 Inferring regulatory and other networks from gene expression data

10 Analysis of biological networks

11 Proteomics: mass spectrometry

12 Measuring protein abundance with ELISA

13 Flow cytometry: counting and sorting stained cells

This book addresses the difficulties experienced by wet lab researchers with the statistical analysis of molecular biology related data. The authors explain how to use R and Bioconductor for the analysis of experimental data in the field of molecular biology. The content is based upon two university courses for bioinformatics and experimental biology students (Biological Data Analysis with R and High-throughput Data Analysis with R). The material is divided into chapters based upon the experimental methods used in the laboratories.
Key features include:
• Broad appeal--the authors target their material to researchers in several levels, ensuring that the basics are always covered.
• First book to explain how to use R and Bioconductor for the analysis of several types of experimental data in the field of molecular biology.
• Focuses on R and Bioconductor, which are widely used for data analysis. One great benefit of R and Bioconductor is that there is a vast user community and very active discussion in place, in addition to the practice of sharing codes. Further, R is the platform for implementing new analysis approaches, therefore novel methods are available early for R users.

https://www.wiley.com/en-us/Molecular+Data+Analysis+Using+R-p-9781119165026