Molecular Data Analysis Using R 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 labs.
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.
About the Companion Website, xxi
1 Introduction to R statistical environment, 1
2 Simple sequence analysis, 17
3 Annotating gene groups, 41
4 Next-generation sequencing: introduction and genomic applications, 65
5 Quantitative transcriptomics: qRT-PCR, 99
6 Advanced transcriptomics: gene expression microarrays, 125
7 Next-generation sequencing in transcriptomics: RNA-seq experiments, 145
8 Deciphering the regulome: from ChIP to ChIP-seq, 167
9 Inferring regulatory and other networks from gene expression data, 191
10 Analysis of biological networks, 215
11 Proteomics: mass spectrometry, 245
12 Measuring protein abundance with ELISA, 261
13 Flow cytometry: counting and sorting stained cells, 279