Independent Study to prepare for workshop

Transcriptomics 2: Statistical Analysis

Emma Rand

17 December, 2024

Overview

In these slides we will:

  • Check where you are following week 3

  • learn some concepts in differential expression

    • log2 fold changes
    • Multiple correction
    • normalisation
    • statistical model

  • Find out what packages we will use

Where should you be?

What we did in Transcriptomics 1: 👋 Hello data!

  • Discovered how many rows and columns we had in our datasets and what these were.
  • Examined the distribution of values
    • across the whole dataset
    • across the samples/cells (i.e., averaged over genes) to see variation between samples/cells
    • across the genes (i.e., averaged over samples/cells) to see variation between genes
  • Filtered data for quality control and wrote to file (except 🐭)

Where should you be?

After the Transcriptomics 1: 👋 Hello data! Workshop including:

🐸 Frog development

  • An RStudio Project called frogs-88H which contains:

    • data-raw: xlaevis_counts_S14.csv, xlaevis_counts_S20.csv, xlaevis_counts_S30.csv
    • data-processed: s30_filtered.csv, s20_filtered.csv
    • Two scripts: cont-fgf-s30.R, cont-fgf-s20.R

🎄 Arabidopsis

  • An RStudio Project called arab-88H which contains:

    • data-raw: arabidopsis-wild.csv, arabidopsis-spl7.csv
    • data-processed: wild_filtered.csv, spl7_filtered.csv
    • Two scripts: suff-def-wild.R, suff-def-spl7.R

💉 Leishmania

  • An RStudio Project called leish-88H which contains:

    • data-raw: leishmania-mex-ama.csv, leishmania-mex-pro.csv, leishmania-mex-meta.csv
    • data-processed: pro_meta_filtered.csv, pro_ama_filtered.csv
    • Two scripts: pro_meta.R, pro_ama.R

🐭 Stem cells

  • An RStudio Project called mice-88H which contains

    • data-raw: surfaceome_hspc.csv, surfaceome_prog.csv, surfaceome_lthsc.csv
    • data-processed: hspc_prog.csv, hspc_lthsc.csv
    • Two scripts: hspc-prog.R, hspc-lthsc.R

Additionally…

Files should be organised into folders. Code should well commented and easy to read. You should have curated your code to remove unnecessary commands that were useful to troubleshoot or understand objects in your environment but which are not needed for the final analysis.

If you are missing files, go through:

Differential expression

Differential expression

  • The goal of differential expression is to test whether there is a significant difference in gene expression between groups.

  • A large number of computational methods have been developed for differential expression analysis

  • R is the leading language for differential expression analysis

Differential expression

  • the statistical concepts are very similar to those you have already encountered in stages 1 and 2

  • you are essentially doing paired- or independent-samples tests

  • but you are doing a lot of them! One for every gene

  • data need normalisation before comparison

Statistical concepts

Like familiar tests:

  • the type of test (the function) you use depends on the type of data you have and the type of assumptions you want to make

  • the tests work by comparing the variation between groups to the variation within groups.

  • you will get: the difference between groups, a test statistic, and a p-value

  • you also get an adjusted p-value which is the ‘correction’ for multiple testing

The difference between groups

  • The difference between groups is given as the log2 fold change in expression between groups

  • A fold change is the expression in one group divided by the expression in the other group: \(\frac{A}{B}\)

  • we use fold changes because the absolute expression values may not be accurate and relative changes are what matters

  • we use log2 fold changes because they are symmetrical around 0

Why log2 fold change?

  • log2 means log to the base 2

  • Suppose the expression in group A is 5 and the expression in group B is 8

  • \(\frac{A}{B} = \frac{5}{8}\) = 0.625 and \(\frac{B}{A} = \frac{8}{5}\) = 1.6

  • If B > A the range of \(\frac{A}{B}\) is 0 - 1 but the range of \(\frac{B}{A}\) is 1 - \(\infty\)

  • However, if we take the log2 of \(\frac{A}{B}\) we get -0.678 and the log2 of \(\frac{B}{A}\) is 0.678.

Adjusted p-value

  • The p-value has to be adjusted because of the number of tested being done

  • In stage 1, we used Tukey’s HSD to adjust for multiple testing following an ANOVA

  • Here the Benjamini-Hochberg procedure (Benjamini and Hochberg 1995) is used to adjust for multiple testing

  • BH controls the False Discovery Rate (FDR)

  • The FDR is the proportion of false positives among the genes called significant

Normalisation

  • Normalisation adjusts raw counts to account for factors that prevent direct comparisons

  • Normalisation usually influences the experimental design as well as the analysis

Normalisation

  • 🐭 mice data are normalised

  • 🐸 frog, 🎄 Arabidopisis and 💉 Leishmania data are raw counts (not normalised) because the differential expression method will do this.

  • Normalisation is a big topic. See Düren, Lederer, and Qin (2022); Bullard et al. (2010); Lytal, Ran, and An (2020); Abrams et al. (2019); Vallejos et al. (2017); Evans, Hardin, and Stoebel (2017)

Type of DE tests

Type of test (the function)

  • DESeq2 and edgeR
    • both require raw counts as input
    • both assume that most genes are not DE
    • both use a negative binomial distribution1 to model the data
    • use slightly different normalisation methods: DESeq2 uses the median of ratios method; edgeR uses the trimmed mean of M values (TMM) method

Type of test (the function)

  • scran
    • works on normalized log-expression values
    • performs Welch t-tests

Meta data

  • DE methods require two types of data: the expression data and the meta data

  • The meta data gives the information about the samples

  • It says which samples (which columns of data) are in which treatment group (s)

  • Meta data is usually stored in a separate file

🐸 Frog development

🎄 Arabidopisis

💉 Leishmania

🐭 Stem cells

  • Expression for a subset of the transcriptome, the surfaceome

  • Values are log2 normalised values

  • The statistical analysis method we will use scran (Lun, McCarthy, and Marioni 2016) requires normalised values

Adding gene information

Adding gene information

  • The gene id is difficult to interpret

  • Therefore we need to add information such as the gene name and a description to the results

🐸 Xenbase

xenbase logo

Xenbase is a model organism database that provides genomic, molecular, and developmental biology information about Xenopus laevis and Xenopus tropicalis.

It took me some time to find the information you need.

🐸 Xenbase

  • I got the information from the Xenbase information pages under Data Reports | Gene Information

  • This is listed: Xenbase Gene Product Information [readme] gzipped gpi (tab separated)

  • Click on the readme link to see the file format and columns

  • I downloaded xenbase.gpi.gz, unzipped it, removed header lines and the Xenopus tropicalis (taxon:8364) entries and saved it as xenbase_info.xlsx

  • In the workshop you will import this file and merge the information with the results file

🎄 TAIR10 through Ensembl

  • Ensembl creates, integrates and distributes reference datasets and analysis tools that enable genomics

  • BioMart (Smedley et al. 2009) provides uniform access to these large datasets

  • biomaRt (Durinck et al. 2009, 2005) is a Bioconductor package gives you programmatic access to BioMart.

  • In the workshop you use this package to get information you can merge with the results file

💉 TriTrypDB

🐭 Ensembl

  • Ensembl creates, integrates and distributes reference datasets and analysis tools that enable genomics

  • BioMart (Smedley et al. 2009) provides uniform access to these large datasets

  • biomaRt (Durinck et al. 2009, 2005) is a Bioconductor package gives you programmatic access to BioMart.

  • In the workshop you use this package to get information you can merge with the results file

Packages

These packages are all on the University computers which you can access on campus or remotely using the VDS

If you want to use your own machine you will need to install the packages.

Install BiocManager from CRAN in the the normal way and set the version of Bioconductor packages to install:

install.packages("BiocManager")
BiocManager::install(version = "3.19")

Install DESeq2 from Bioconductor using BiocManager:

BiocManager::install("DESeq2")

Install scran from Bioconductor using BiocManager:

BiocManager::install("scran")

Install biomaRt from Bioconductor using BiocManager:

BiocManager::install("biomaRt")

Workshops

Workshops

  • Transcriptomics 1: Hello data. Getting to know the data. Checking the distributions of values overall, across rows and columns to check things are as we expect and detect rows/columns that need to be removed

  • Transcriptomics 2: Statistical Analysis. Identifying which genes are differentially expressed between treatments. This is the main analysis step. We will use different methods for bulk and single cell data.

  • Transcriptomics 3: Visualising. Principal Component Analysis (PCA) volcano plots to visualise the results of the

References

Pages made with R (R Core Team 2024), Quarto (Allaire et al. 2024), knitr (Xie 2024, 2015, 2014), kableExtra (Zhu 2021)

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