Teaching plan for the course unit

(Short version)


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General information


Course unit name: Statistics for Biosciences

Course unit code: 361237

Academic year: 2021-2022

Coordinator: Esteban Vegas Lozano

Department: Faculty of Economics and Business

Credits: 6

Single program: S



Estimated learning time

Total number of hours 150


Face-to-face and/or online activities



-  Lecture with practical component

Face-to-face and online




-  Problem-solving class

Face-to-face and online




-  IT-based class

Face-to-face and online



Supervised project


Independent learning




Learning objectives


Referring to knowledge

[A] Identify and use correctly the basic terminology of the biosciences: biology, biomedicine and bioinformatics, ecology, genetics and biodiversity.

[B] Know the most relevant statistical techniques in the biosciences.

[C] Apply statistical techniques in the biosciences and interpret the results correctly.

[D] Know the most relevant medical specialties and their most common types of study and their associated variables.

[E] Know some terms and concepts of molecular biology, as well as some of the technologies used in biology and biomedicine research.

[F] Learn the usual processes for high-performance data analysis.

[G] Know and interpret general statistical measures of diversity and their relationship with biodiversity in ecology and genetics.

[H] Apply the statistical environment R to data analysis in the biosciences.

[I] Enhance the capacity for reflection and critical analysis by working with data sets from the biosciences.

[J] Learn to write a report containing the objectives, methods and results, as well as a critical assessment of the limitations found.

[K] Be aware of the ethical issues inherent in studies in the biosciences.

[L] Know the working environments of the biosciences that offer work to statisticians and their requirements in terms of knowledge and skills.



Teaching blocks


1. Statistics and bioinformatics

1.1. Biomolecules, biomedicine and disease

— The molecules of life: DNA and proteins, central dogma, gene expression
— Biomedicine and molecular basis of some diseases (cancer and immune diseases)
— Examples and a case study: personalized medicine

1.2. Introduction to some biotechnology tools

— Bioinformatics
— Gene expression analysis: microarrays
— Other high-performance data acquisition techniques: sequencing and proteomics
— Examples and case studies: bioinformatic tools for exploiting biological databases

1.3. High-performance data analysis: analysis of gene expression arrays

— Pre-processing and quality control
— Normalization
— Selection of differentially expressed genes
— Classification and prediction with high-performance data
— Examples and a case study: selection of genes associated with breast cancer

2. Statistics and biodiversity

2.1. Introduction to diversity

— Statistical measures of diversity. The Simpson and Shannon indices

2.2. Biodiversity in ecology

— Basic concepts of ecology: species, ecosystem, niche, habitat, species richness, diversity index, abundance, etc.
— Graphs for representation of diversity data, frequency distributions, and rank-abundance diagram
— Statistical models for species diversity: Fisher’s log-series, the log-normal model, the geometric series, McArthur’s broken stick model
— Species diversity measures: species richness, the Simpson index, the Shannon index
— Species richness estimates: the species accumulation curve, parametric and non-parametric richness estimators
— Uncertainty in estimating diversity. The jackknife
— Examples

2.3. Biodiversity in genetics

— Basic concepts of genetics: chromosomes, loci, genes and alleles, genotypes, haplotypes, dominant, codominant and recessive markers, microsatellites and SNPs, polymorphisms, allelic and genotypic frequencies, observed and expected heterozygosity, genetic balance (Hardy-Weinberg and binding imbalance). Statistics to measure imbalance
— Measures of genetic diversity. Percentage of polymorphic loci, effective number of alleles, richness of alleles, expected heterozygosity. The Simpson and Shannon indices
— Analysis of genetic diversity between and within populations, Nei’s indices, and Wright’s F statistics
— Examples





Reading and study resources

Consulteu la disponibilitat a CERCABIB


COHEN, William W. A computer Scientist’s guide to cell biology: a travelogue from a stranger in a strange land. Pittsburgh: Springer, 2007

  Recommended for block 1.

Catāleg UB  Enllaç

GASTON, Kevin J. et al. Biodiversity: an introduction. 2nd ed. Oxford: Blackwell Science, 2004

  Recommended for block 2.

Catāleg UB  Enllaç

GIBSON, Greg et al. A primer of genome science. 3rd ed. Sunderland, Mass.: Sinauer Associates, 2009

  Recommended for block 1.

Catāleg UB  Enllaç

KJRIJNEN, H. Applied Statistics for Bioinformatics (pdf)

  Recommended for block 1.

Catāleg UB  Enllaç

LOWE, Andrew et al. Ecological genetics, design, analysis and application. Malden (Mass.): Blackwell, 2004

  Recommended for block 2.

Catāleg UB  Enllaç

PEVSNER, Jonathan. Bioinformatics and Functional Genomics. Hoboken, N.J.: Wiley-Blackwell, 2009

  Recommended for block 1.

Catāleg UB  Enllaç

MAGURRAN, Anne E. Measuring biological diversity. Malden: Blackwell, 2004

  Recommended for block 2.

Catāleg UB  Enllaç

Rafael A Irizarry and Michael I. Love. Data Analysis for the Life Sciences with R. Chapman and Hall/CRC, 2016

CCUC: Accés restringit als usuaris de la UOC   Enllaç