Chapter 12 Conclusions

In this course, we have consolidated our skills in data analysis and visualisation using R. In particular, this course has focused on interpretation and understanding of the outputs.

We have also learned and applied new tools, in particular how to manipulate sequence data using Biostrings and how to use statistical learning tools to explore data and identify patterns of biological interest. In terms of statistical testing and machine learning, it can be useful to summarise the different classes of techniques we have touch on using the figures below. The grid on each of these figures represents a matrix with quantitative values with features (genes, proteins, transcripts, …) along the rows and samples along the columns. Annotations of features or samples are presented as coloured boxes on the right or top if the matrices.

In hypothesis test, we start with quantitative data and an experimental design, i.e. sample annotation that group samples in biologically relevant groups. The output of hypothesis testing is a set of metrics for each features (a p-value, an adjusted p-value, a fold-change, …) that informs whether that feature shows any difference between the biological groups of interest.

Figure 12.1: Hypothesis testing.

Hypothesis testing.

When performing clustering (unsupervised machine learning), we only have our quantitative data as input, and the clustering algorithm (whether k-means, hierarchical clustering among many others) suggests a set of groups to cluster the features (as shown below) or samples.

Figure 12.2: Clustering of features.

Clustering of features.

When performing dimensionality reduction with, for example PCA, one starts with a n by m data set as input to reduce the number of dimensions in either direction. On the figure below, the number of features n was reduced to 2 to, typically, visualise the sample along a scatter plot.

Figure 12.3: Dimensionality reduction.

Dimensionality reduction.

In classification (supervised machine learning), we need labelled data, i.e. a set of sample (top on the figures below) or features (bottom on the figure below). The classifier uses the data to learn from assigned labels and applies that learnt model to infer the most likely label of the unlabelled data.

Figure 12.4: Classification of samples (top) or features (bottom).

Classification of samples (top) or features (bottom).

The next steps of the curriculum (course WSBIM2122) will build upon the skills gained in this course to fully analyse complete datasets from omics technologies, using state-of-the-art statistical and machine learning methods and software. The course will be project based: each experiment and associated technologies will be introduced, the analysis pipeline will be explained, and the students will then implement and present the data analysis and critical interpretation of the results.

12.1 Additional exercise

These final exercises integrate several techniques seen throughout this course.

Exercise 1

► Question

The the RNA-Seq data and sample annotation in the files returned by the rWSBIM1207::kem2.tsv() function. How many genes have been assayed? Describe the experimental design at hand.

► Question

Print and describe the experimental design.

► Question

Before performing log-transformation of the data, check if there are any 0-expression values. Indeed, these would be converted to -Inf after log-transformation. Are there any 0-values in the data. If so, how many are there.

► Question

Add 1 to all expression values, then log-2 transform the expression data. Visualise the distributions of the expression values in each samples before and after transformation.

Continue with log-transformed data, without preforming any normalisation.

► Question

Compute, for each gene, the number of 0-expression values and visualise these as a table showing the number of genes with 0, 1, 2, … zero values.

► Question

Visualise and interpret the experiment design (cell type and treatment) using a principal component analyis.

► Question

Use a t test to identify the genes that are differentially expressed between the stimulated and non-stimulated samples of cell type A. Visualise the results on a volcano plot.

► Question

Considering that a gene is called differentially expressed if it has an absolute log2 fold-change > 1 and an adjusted p-value < 0.001, how many differentially genes are there?

► Question

Visualise the expression of these differentially expressed genes in all sampes using a heatmap and interpret the figure.

► Question

Select the 3 genes with the smallest p-values and visualise their expression in all samples (cell types A and B, both stimulated and un-stimulated) using boxplots.

► Question

Perform a k-means clustering of all genes and samples using k = 5 and visualise these clusters on a PCA plot. Interprete the figure.

► Question

Repeat the volcano plot above, colouring the genes based on their respective clusters. Refine your interpretation of the gene-level PCA above.

Exercise 2

► Question

Load the recapSE1 data available in the rWSBIM1322 package (version >= 0.3.1) with data(recapSE1). The data contains quantitative proteomics data. You are asked to:

  • Familiarise yourself with the experimental design.
  • Verify if the data need to be transformed and or normalised.
  • Perform an principal component analyse and interpret it.
  • Identify differentially abundant proteins for each condition compared to the reference group CTRL.
  • Are there any significantly up- or down-regulated proteins shared between all treatments?

Exercise 3

► Question

Load the recapSE2 data available in the rWSBIM1322 package (version >= 0.3.2) with data(recapSE2). The data contains quantitative proteomics data. You are asked to:

  • Familiarise yourself with the experimental design.
  • Verify if the data need to be transformed and or normalised.
  • Perform an principal component analyse and interpret it.
  • Identify differentially abundant proteins for each condition compared to the reference group CTRL.
  • Repeat the analyses above after removing the outlier. What is the effect of that outlier of the differential analysis.

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