Evolutionary Transcriptomics with R
Today, phenotypic phenomena such as morphological mutations, diseases or developmental processes are primarily investigated on the molecular level using transcriptomics approaches. Transcriptomes denote the total number of quantifiable transcripts present at a specific stage in a biological process. In disease or developmental (defect) studies transcriptomes are usually measured over several time points. In treatment studies aiming to quantify differences in the transcriptome due to biotic stimuli, abiotic stimuli, or diseases usually treatment / disease versus non-treatment / non-disease transcriptomes are being compared. In either case, comparing changes in transcriptomes over time or between treatments allows us to identify genes and gene regulatory mechanisms that might be involved in governing the biological process of investigation. Although transcriptomics studies are based on a powerful methodology little is known about the evolution of such transcriptomes. Understanding the evolutionary mechanism that change transcriptomes over time, however, might give us a new perspective on how diseases emerge in the first place or how morphological changes are triggered by changes of developmental transcriptomes.
Evolutionary transcriptomics aims to capture and quantify the evolutionary conservation of genes that contribute to the transcriptome during a specific stage of the biological process of interest. This quantification on the highest level is achieved through transcriptome indices (Domazet-Lošo and Tautz, 2010; Drost et al., 2016) which denote weighted means of gene age or rate of protein substitutions. In general, evolutionary transcriptomics can be used as a method to quantify the evolutionary conservation of transcriptomes to investigate how transcriptomes underlying biological processes are constrained or channeled due to evolutionary history (Dollow's law) (Drost et al., 2017).
In principle, any transcriptome dataset published so far can be combined with evolutionary information. Thus,
myTAI in combination with evolutionary information can be used to study corresponding transcriptomes with any available transcriptome dataset.
For the purpose of performing large scale evolutionary transcriptomics studies, the
myTAI package implements frameworks to allow researchers to study the evolution of biological processes and to detect stages or periods of evolutionary conservation or variability.
I hope that
myTAI will become the community standard tool to perform evolutionary transcriptomics studies and I am happy to add required functionality upon request.
The following tutorials will provide use cases and detailed explainations of how to quantify transcriptome onservation with
myTAI and how to interpret the results generated with this software tool.
Please cite the following paper when using
myTAI for your own research. This will allow me to continue working on this software tool and will motivate me to extend its functionality and usability in the next years. Many thanks in advance :)
Drost et al. myTAI: evolutionary transcriptomics with R . Bioinformatics 2018, 34 (9), 1589-1590. doi:10.1093
Users can download
myTAI from CRAN :
# install myTAI 0.8.0 from CRAN source("http://bioconductor.org/biocLite.R") biocLite('myTAI')
Install Developer Version
Some bug fixes or new functionality will not be available on CRAN yet, but in
the developer version here on GitHub. To download and install the most recent
# install the developer version of myTAI on your system source("http://bioconductor.org/biocLite.R") biocLite("HajkD/myTAI")
The current status of the package as well as a detailed history of the
functionality of each version of
myTAI can be found in the NEWS section.
These tutorials introduce users to
- Introduction to the myTAI Package
- Intermediate Concepts of Phylotranscriptomics
- Advanced Topics of Phylotranscriptomics
- Perform Age Enrichment Analyses
- Gene Expression Analysis with myTAI
- Taxonomic Information Retrieval
library(myTAI) # example dataset covering 7 stages of A thaliana embryo development data("PhyloExpressionSetExample") # transform absolute expression levels to log2 expression levels ExprExample <- tf(PhyloExpressionSetExample, log2) # visualize global Transcriptome Age Index pattern PlotSignature(ExprExample) # plot expression level distributions for each age (=PS) category # and each developmental stage PlotCategoryExpr(ExprExample, "PS") # plot median expression of each age category seperated by old (PS1-3) # versus young (PS4-12) genes PlotMedians(ExprExample, Groups = list(1:3, 4:12)) # plot mean expression of each age category seperated by old (PS1-3) # versus young (PS4-12) genes PlotMeans(ExprExample, Groups = list(1:3, 4:12)) # plot relative mean expression of each age category seperated by old (PS1-3) # versus young (PS4-12) genes PlotRE(ExprExample, Groups = list(1:3, 4:12)) # plot the significant differences between gene expression distributions # of old (=group1) versus young (=group2) genes PlotGroupDiffs(ExpressionSet = ExprExample, Groups = list(group_1 = 1:3, group_2 = 4:12), legendName = "PS", plot.type = "boxplot")
# to perform differential gene expression analyses with myTAI # please install the edgeR package # install edgeR source("http://bioconductor.org/biocLite.R") biocLite("edgeR")
Getting started with
Users can also read the tutorials within (RStudio) :
# source the myTAI package library(myTAI) # look for all tutorials (vignettes) available in the myTAI package # this will open your web browser browseVignettes("myTAI") # or as single tutorials # open tutorial: Introduction to Phylotranscriptomics and myTAI vignette("Introduction", package = "myTAI") # open tutorial: Intermediate Concepts of Phylotranscriptomics vignette("Intermediate", package = "myTAI") # open tutorial: Advanced Concepts of Phylotranscriptomics vignette("Advanced", package = "myTAI") # open tutorial: Age Enrichment Analyses vignette("Enrichment", package = "myTAI") # open tutorial: Gene Expression Analysis with myTAI vignette("Expression", package = "myTAI") # open tutorial: Taxonomic Information Retrieval with myTAI vignette("Taxonomy", package = "myTAI")
myTAI framework users can find:
TAI(): Function to compute the Transcriptome Age Index (TAI)
TDI(): Function to compute the Transcriptome Divergence Index (TDI)
TPI(): Function to compute the Transcriptome Polymorphism Index (TPI)
REMatrix(): Function to compute the relative expression profiles of all phylostrata or divergence-strata
RE(): Function to transform mean expression levels to relative expression levels
pTAI(): Compute the Phylostratum Contribution to the global TAI
pTDI(): Compute the Divergence Stratum Contribution to the global TDI
pMatrix(): Compute Partial TAI or TDI Values
pStrata(): Compute Partial Strata Values
Visualization and Analytics Tools:
PlotSignature(): Main visualization function to plot evolutionary signatures across transcriptomes
PlotPattern(): Base graphics function to plot evolutionary signatures across transcriptomes
PlotContribution(): Plot Cumuative Transcriptome Index
PlotCorrelation(): Function to plot the correlation between phylostratum values and divergence-stratum values
PlotRE(): Function to plot the relative expression profiles
PlotBarRE(): Function to plot the mean relative expression levels of phylostratum or divergence-stratum classes as barplot
PlotMeans(): Function to plot the mean expression profiles of age categories
PlotMedians(): Function to plot the median expression profiles of age categories
PlotVars(): Function to plot the expression variance profiles of age categories
PlotDistribution(): Function to plot the frequency distribution of genes within the corresponding age categories
PlotCategoryExpr(): Plot the Expression Levels of each Age or Divergence Category as Barplot or Violinplot
PlotEnrichment(): Plot the Phylostratum or Divergence Stratum Enrichment of a given Gene Set
PlotGeneSet(): Plot the Expression Profiles of a Gene Set
PlotGroupDiffs(): Plot the significant differences between gene expression distributions of PS or DS groups
PlotSelectedAgeDistr(): Plot the PS or DS distribution of a selected set of genes
A Statistical Framework and Test Statistics:
FlatLineTest(): Function to perform the Flat Line Test that quantifies the statistical significance of an observed phylotranscriptomics pattern (significant deviation from a frat line = evolutionary signal)
ReductiveHourglassTest(): Function to perform the Reductive Hourglass Test that statistically evaluates the existence of a phylotranscriptomic hourglass pattern (hourglass model)
EarlyConservationTest(): Function to perform the Reductive Early Conservation Test that statistically evaluates the existence of a monotonically increasing phylotranscriptomic pattern (early conservation model)
EnrichmentTest(): Phylostratum or Divergence Stratum Enrichment of a given Gene Set based on Fisher's Test
bootMatrix(): Compute a Permutation Matrix for Test Statistics
All functions also include visual analytics tools to quantify the goodness of test statistics.
Differential Gene Expression Analysis
DiffGenes(): Implements Popular Methods for Differential Gene Expression Analysis
CollapseReplicates(): Combine Replicates in an ExpressionSet
CombinatorialSignificance(): Compute the Statistical Significance of Each Replicate Combination
Expressed(): Filter Expression Levels in Gene Expression Matrices (define expressed genes)
SelectGeneSet(): Select a Subset of Genes in an ExpressionSet
PlotReplicateQuality(): Plot the Quality of Biological Replicates
GroupDiffs(): Quantify the significant differences between gene expression distributions of PS or DS groups
Taxonomic Information Retrieval
taxonomy(): Retrieve Taxonomic Information for any Organism of Interest
Minor Functions for Better Usibility and Additional Analyses
MatchMap(): Match a Phylostratigraphic Map or Divergence Map with a ExpressionMatrix
tf(): Transform Gene Expression Levels
age.apply(): Age Category Specific apply Function
ecScore(): Compute the Hourglass Score for the EarlyConservationTest
geom.mean(): Geometric Mean
harm.mean(): Harmonic Mean
omitMatrix(): Compute TAI or TDI Profiles Omitting a Given Gene
rhScore(): Compute the Hourglass Score for the Reductive Hourglass Test
Developer Version of
The developer version of
myTAI might include more functionality than the stable version on CRAN.
Hence users can download the current developer version of
myTAI by typing:
# The developer version can be installed directly from github: # install.packages("devtools") # install developer version of myTAI library(devtools) install_github("HajkD/myTAI", build_vignettes = TRUE, dependencies = TRUE) # On Windows, this won't work - see ?build_github_devtools # install_github("HajkD/myTAI", build_vignettes = TRUE, dependencies = TRUE) # When working with Windows, first you need to install the # R package: rtools # or consult: http://www.rstudio.com/products/rpackages/devtools/ # Afterwards you can install devtools -> install.packages("devtools") # and then you can run: devtools::install_github("HajkD/myTAI", build_vignettes = TRUE, dependencies = TRUE) # and then call it from the library library("myTAI", lib.loc = "C:/Program Files/R/R-3.1.1/library")
Domazet-Lošo T. and Tautz D. A phylogenetically based transcriptome age index mirrors ontogenetic divergence patterns. Nature (2010) 468: 815-8.
Quint M, Drost HG, et al. A transcriptomic hourglass in plant embryogenesis. Nature (2012) 490: 98-101.
Drost HG, Gabel A, Grosse I, Quint M. Evidence for Active Maintenance of Phylotranscriptomic Hourglass Patterns in Animal and Plant Embryogenesis. Mol. Biol. Evol. (2015) 32 (5): 1221-1231.
Drost HG, Bellstädt J, Ó'Maoiléidigh DS, Silva AT, Gabel A, Weinholdt C, Ryan PT, Dekkers BJW, Bentsink L, Hilhorst H, Ligterink W, Wellmer F, Grosse I, and Quint M. Post-embryonic hourglass patterns mark ontogenetic transitions in plant development. Mol. Biol. Evol. (2016) doi:10.1093/molbev/msw039
Discussions and Bug Reports
I would be very happy to learn more about potential improvements of the concepts and functions provided in this package.
Furthermore, in case you find some bugs or need additional (more flexible) functionality of parts of this package, please let me know:
I would like to thank several individuals for making this project possible.
First I would like to thank Ivo Grosse and Marcel Quint for providing me a place and the environment to be able to work on fascinating topics of Evo-Devo research and for the fruitful discussions that led to projects like this one.
Furthermore, I would like to thank Alexander Gabel and Jan Grau for valuable discussions on how to improve some methodological concepts of some analyses present in this package.
I would also like to thank Master Students: Sarah Scharfenberg, Anne Hoffmann, and Sebastian Wussow who worked intensively with this package and helped me to improve the usability and logic of the package environment.