The myTAI
packages provides a statistical framework and a collection of functions that can be used to perform phylotranscriptomics analyses and visualization to investigate phenomena within the field of Evolutionary Developmental Biology.
The goal of this package is to provide a broad range of users (as well as the phylotranscriptomics community) with an easy to use and computationally optimized framework for reproducible research in the fields referred to as Evolutionary Developmental Biology and Phylotranscriptomics and to allow a better comparability between molecular phenomena resulting from in silico data analyses.
A series of tutorials aim to introduce you to the myTAI
package:
Internally, computationally intensive functions have been written in C++ using the Rcpp package to allow fast analytics in phylotranscriptomics research.
All functions implemented in the myTAI
package expect a specific data format as input data. These standardized data formats are referred to as PhyloExpressionSet and DivergenceExpressionSet.
The simplest way to get introduced into the standard formats referred to as PhyloExpressionSet and DivergenceExpressionSet is to look at the example data sets that come with the myTAI
package.
library(myTAI)
# load an example PhyloExpressionSet stored in the myTAI package
data(PhyloExpressionSetExample)
# load an example DivergenceExpressionSet stored in the myTAI package
data(DivergenceExpressionSetExample)
Now, we want to have a look at the data sets and their standard format.
head(PhyloExpressionSetExample, 3) # head of an example standard PhyloExpressionSet
Phylostratum GeneID Zygote Quadrant Globular Heart Torpedo Bent Mature
1 1 at1g01040.2 2173.635 1911.200 1152.555 1291.4224 1000.253 962.9772 1696.4274
2 1 at1g01050.1 1501.014 1817.309 1665.309 1564.7612 1496.321 1114.6435 1071.6555
3 1 at1g01070.1 1212.793 1233.002 939.200 929.6195 864.218 877.2060 894.8189
As you can see, a standard PhyloExpressionSet is a data.frame
storing the Phylostratum assignment of a given gene in the first column, the gene id of the corresponding gene in the second column, and the entire gene expression set (time series or treatments) starting with the third column. This format is crucial for all functions that are implemented in the myTAI
package.
Each function checks, whether the PhyloExpressionSet standard is fulfilled.
# used by all myTAI functions to check the validity of the PhyloExpressionSet standard
is.ExpressionSet(PhyloExpressionSetExample)
[1] TRUE
In case the PhyloExpressionSet standard is violated, the is.ExpressionSet()
function will return FALSE
and the corresponding function within the myTAI
package will return an error message.
# used a non standard PhyloExpressionSet
head(PhyloExpressionSetExample[ , 2:5], 2)
GeneID Zygote Quadrant Globular
1 at1g01040.2 2173.635 1911.200 1152.555
2 at1g01050.1 1501.014 1817.309 1665.309
is.ExpressionSet(PhyloExpressionSetExample[ , 2:5])
Error in is.ExpressionSet(PhyloExpressionSetExample[, 2:5]) :
The present input object does not fulfill the ExpressionSet standard.
This PhyloExpressionSetExample
data set stored within the myTAI
package stores the time course (seven stages) of Arabidopsis thaliana embryo development. Again, the first column represents the Phylostratum assignment of a given gene, the second column stores the gene id of the corresponding gene, and columns 3 to 9 store the gene expression set of A. thaliana embryo development.
head(DivergenceExpressionSetExample, 3) # head of an example standard DivergenceExpressionSet
Divergence.stratum GeneID Zygote Quadrant Globular Heart Torpedo Bent Mature
1 1 at1g01050.1 1501.0141 1817.3086 1665.3089 1564.761 1496.3207 1114.6435 1071.6555
2 1 at1g01120.1 844.0414 787.5929 859.6267 931.618 942.8453 870.2625 792.7542
3 1 at1g01140.3 1041.4291 908.3929 1068.8832 967.749 1055.1901 1109.4662 825.4633
Analogous to a standard PhyloExpressionSet, a standard DivergenceExpressionSet is a data.frame
storing the Divergence-Stratum assignment of a given gene in the first column, the gene id of the corresponding gene in the second column, and the entire gene expression set (time series or treatments) starting with the third column. This format is crucial for all functions that are implemented in the myTAI
package.
Keeping these standard data formats in mind will provide you with the most important requirements to get started with the myTAI
package.
Note, that within the code of each function, the argument ExpressionSet
always refers to either a PhyloExpressionSet or a DivergenceExpressionSet, whereas in specialized functions some arguments are specified as PhyloExpressionSet when they take an PhyloExpressionSet as input data set, or specified as DivergenceExpressionSet when they take an DivergenceExpressionSet as input data set.
Before starting any computation, sometimes it is good to visualize the Phylostratum distribution of genes stored within a given PhyloExpressionSet.
For this purpose, the PlotDistribution()
function was implemented:
# displaying the phylostratum distribution (gene frequency distribution)
# of a PhyloExpressionSet as absolute frequency distribution
PlotDistribution(PhyloExpressionSetExample, xlab = "Phylostratum")
or display it as relative frequencies:
# as relative frequency distribution
PlotDistribution(PhyloExpressionSetExample, as.ratio = TRUE, xlab = "Phylostratum", cex = 0.7)
Another important feature to check is, whether the Phylostratum assignment and Divergence-Stratum assignment of the genes stored within the PhyloExpressionSet and DivergenceExpressionSet are correlated (linear dependent). This is important to be able to assume the independence of TAI and TDI measures.
For this purpose the PlotCorrelation()
function was implemented:
# visualizing the correlation between Phylostratum and Divergence-Stratum assignments
# of the intersecting set of genes that are stored within the PhyloExpressionSet
# and DivergenceExpressionSet
PlotCorrelation(PhyloExpressionSetExample, DivergenceExpressionSetExample,
method = "kendall", linearModel = TRUE)
In this case Phylostratum and Divergence-Stratum assignments of the intersecting set of genes that are stored within the PhyloExpressionSet and DivergenceExpressionSet are weakly correlated, but can be assumed to be independent.
Note: The PlotCorrelation()
function always takes a PhyloExpressionSet as first argument and a DivergenceExpressionSet as second argument.
Mathematically the Transcriptome Age Index (TAI) introduced by Domazet-Loso and Tautz (2010) represents a weighted arithmetic mean of the transcriptome age during a corresponding developmental stage s.
Analogous to the TAI measure, the Transcriptome Divergence Index (TDI) was introduced by Quint et al. (2012) as weighted arithmetic mean of the degree of sequence divergence of the transcriptome being expressed during a corresponding developmental stage s.
The PlotPattern()
function first computes the TAI
(given a PhyloExpressionSet) or the TDI
(given a DivergenceExpressionSet) to visualize the TAI or TDI profile, their standard deviation and statistical significance.
# simple use of the PlotPattern function
# to plot the TAI of a given PhyloExpressionSet
PlotPattern(PhyloExpressionSetExample, type = "l", lwd = 6, xlab = "Ontogeny", ylab = "TAI")
The p-value (p_flt
) above the TAI curve is returned by the FlatLineTest. As described in the documentation of PlotPattern()
(?PlotPattern
or ?FlatLineTest
), the FlatLineTest
is the default statistical test to quantify the statistical significance of the observed phylotranscriptomic pattern. In detail, the test quantifies any statistically significant deviation of the phylotranscriptomic pattern from a flat line.
In case the observed phylotranscriptomic pattern not only significantly deviates from a flat line but also visually resembles and hourglass shape, one can obtain a p-value corresponding to the statistical significance of a visual hourglass pattern referred to as ReductiveHourglassTest
(?ReductiveHourglassTest
).
Since the ReductiveHourglassTest
has been defined for a priori biological knowledge (Drost et al., 2014), the modules
argument within the ReductiveHourglassTest()
function needs to be specified.
Three modules need to be specified: an early-module, phylotypic module (mid), and a late-module.
For this example we divide A. thaliana embryo development stored within the PhyloExpressionSetExample into the following three modules:
# using the PlotPattern function
# to plot the TAI of a given PhyloExpressionSet
# and to perform the ReductiveHourglassTest to quantify
# the statisical significance of the visual hourglass pattern
PlotPattern(PhyloExpressionSetExample, TestStatistic = "ReductiveHourglassTest",
modules = list(early = 1:2, mid = 3:5, late = 6:7),
type = "l", lwd = 6, xlab = "Ontogeny", ylab = "TAI")
The corresponding p-value p_rht
now denotes the p-value returned by the ReductiveHourglassTest
which is different from the p-value returned by the FlatLineTest
(p_flt
).
To make sure that you selected the correct modules you can use the shaded.area
argument to visualize the chosen modules:
# using the shaded.area argument to visualize the chosen modules
PlotPattern(PhyloExpressionSetExample, TestStatistic = "ReductiveHourglassTest",
modules = list(early = 1:2, mid = 3:5, late = 6:7),
shaded.area = TRUE, type = "l", lwd = 6, xlab = "Ontogeny", ylab = "TAI")
Note, that defining a priori knowledge for the ReductiveHourglassTest
using the modules
argument, modules need to start at stage 1, …, N and do not correspond to the column position in the PhyloExpressionSet/DivergenceExpressionSet which in contrast would start at position 3, … N.
The third test statistic that is implemented in the myTAI
package is the EarlyConservationTest
.
The EarlyConservationTest
tests whether an observed phylotranscriptomic pattern follows a low-high-high pattern (monotonically increasing function) supporting the Early Conservation Model of embryogenesis.
Analogous to the ReductiveHourglassTest
, the EarlyConservationTest
needs a priori biological knowledge (Drost et al., 2014). So again three modules
have to be specified for the EarlyConservationTest()
function.
Three modules need to be specified: an early-module, phylotypic module (mid), and a late-module.
For this example we divide A. thaliana embryo development stored within the PhyloExpressionSetExample into the following three modules:
# using the PlotPattern function
# to plot the TAI of a given PhyloExpressionSet
# and to perform the EarlyConservationTest to test the
# Early Conservation Hypothesis for the observed phylotranscriptomic pattern
PlotPattern(PhyloExpressionSetExample, TestStatistic = "EarlyConservationTest",
modules = list(early = 1:2, mid = 3:5, late = 6:7),
type = "l", lwd = 6, xlab = "Ontogeny", ylab = "TAI")
The corresponding p-value p_ect
now denotes the p-value returned by the EarlyConservationTest
which is different from the p-value returned by the FlatLineTest
(p_flt
) and ReductiveHourglassTest
(p_rht
).
Since the present TAI pattern of the PhyloExpressionSetExample doesn’t support the Early Conservation Hypothesis, the p-value p_ect
= 1.
Also note here, that defining a priori knowledge for the EarlyConservationTest
using the modules
argument, modules need to start at stage 1, …, N and do not correspond to the column position in the PhyloExpressionSet/DivergenceExpressionSet which in contrast would start at position 3, … N.
To obtain the numerical TAI values, the TAI()
function can be used:
# simply getting the TAI values of a given PhyloExpressionSet
TAI(PhyloExpressionSetExample)
Zygote Quadrant Globular Heart Torpedo Bent Mature
3.229942 3.225614 3.107135 3.116693 3.073993 3.176511 3.390334
Analogous to the TAI computations and visualization, the TDI computations can be performed in a similar fashion:
# simple use of the PlotPattern function
# to plot the TDI of a given DivergenceExpressionSet
PlotPattern(DivergenceExpressionSetExample, type = "l", lwd = 6,
xlab = "Ontogeny", ylab = "TDI")
Again, for the ReductiveHourglassTest we divide A. thaliana embryo development into the three modules:
# using the PlotPattern function
# to plot the TDI of a given DivergenceExpressionSet
# and to perform the ReductiveHourglassTest to quantify
# the statisical significance of the visual hourglass pattern
PlotPattern(DivergenceExpressionSetExample, TestStatistic = "ReductiveHourglassTest",
modules = list(early = 1:2, mid = 3:5, late = 6:7),
type = "l", lwd = 6, xlab = "Ontogeny", ylab = "TDI")
And for the EarlyConservationTest we again divide A. thaliana embryo development into the three modules:
# using the PlotPattern function
# to plot the TDI of a given DivergenceExpressionSet
# and to perform the EarlyConservationTest
# test the Early Conservation Hypothesis
PlotPattern(DivergenceExpressionSetExample, TestStatistic = "EarlyConservationTest",
modules = list(early = 1:2, mid = 3:5, late = 6:7),
type = "l", lwd = 6, xlab = "Ontogeny", ylab = "TDI")
To obtain the numerical TDI values for a given DivergenceExpressionSet simply run:
# getting the TDI values of a given DivergenceExpressionSet
TDI(DivergenceExpressionSetExample)
Zygote Quadrant Globular Heart Torpedo Bent Mature
4.532029 4.563200 4.485705 4.500868 4.466477 4.530704 4.690292
The TAI or TDI pattern are very useful to gain a first insight into the mean transcriptome age or mean sequence divergence of genes being most active during the corresponding developmental stage or experiment.
To further investigate the origins of the global TAI or TDI pattern it is useful to visualize the mean gene expression of each Phylostratum or Divergence-Stratum class.
Visualizing the mean gene expression of genes corresponding to the same Phylostratum or Divergence-Stratum class allows to detect development specific groups of Phylostrata or Divergence-Strata that are most expressed during the underlying developmental process. This might lead to correlating specific groups of Phylostrata or Divergence-Strata sharing similar evolutionary origins with common functions or functional contributions to a specific developmental process.
# visualizing the mean gene expression of each Phylostratum class
PlotMeans(PhyloExpressionSetExample, Groups = list(1:12), legendName = "PS",
xlab = "Ontogeny", lty = 1, cex = 0.7, lwd = 5)
Here we see that the mean gene expression of Phylostratum group: PS1-3 (genes evolved before the establishment of embryogenesis in plants) are more expressed during A. thaliana embryogenesis than PS4-12 (genes evolved during or after the establishment of embryogenesis in plants).
In different developmental processes different Phylostratum groups or combination of groups might resemble the majority of expressed genes.
So the PlotMeans()
function takes an PhyloExpressionSet or DivergenceExpressionSet and plots for each Phylostratum the mean expression levels of all genes corresponding to this Phylostratum. The Groups
argument takes a list storing the Phylostrata (classified into the same group) that shall be visualized on the same plot.
For this example we separate groups of Phylostrata into evolutionary old Phylostrata (PS1-3) in one plot versus evolutionary younger Phylostrata (PS4-12) into another plot:
# visualizing the mean gene expression of each Phylostratum class
PlotMeans(PhyloExpressionSetExample, Groups = list(1:3, 4:12), legendName = "PS",
xlab = "Ontogeny", lty = 1, cex = 0.7, lwd = 5)
To obtain the numerical values (mean expression levels for all Phylostrata) run:
# using the age.apply() function implemented in the myTAI package
# to compute the mean expression levels of all Phylostrata
age.apply(PhyloExpressionSetExample, colMeans)
Zygote Quadrant Globular Heart Torpedo Bent Mature
1 2607.882 2579.372 2604.856 2525.704 2554.825 2622.757 2696.331
2 2597.258 2574.745 2467.679 2388.045 2296.410 2243.716 2321.709
3 2528.272 2363.159 2019.436 2099.079 2155.642 2196.875 2855.866
4 1925.320 1887.078 1771.399 1787.175 1740.823 1867.981 2358.893
5 2378.883 2368.593 2061.729 2077.087 2076.693 2564.904 3157.761
6 1658.253 1697.242 1485.401 1462.613 1492.861 1631.741 2304.683
7 1993.321 1717.659 1480.525 1590.009 1545.078 1600.264 2385.409
8 1781.653 1670.106 1452.180 1414.052 1359.376 1816.718 2364.070
9 1758.119 1764.748 1708.815 1575.727 1388.920 1687.314 2193.930
10 2414.456 2501.390 2163.810 1938.060 1770.039 1993.032 2127.015
11 1999.163 2071.456 1702.779 1710.290 1662.099 1726.865 2501.443
12 2126.189 2036.804 1896.964 1909.578 1859.485 1995.732 2387.343
Here the age.apply()
function (?age.apply
) takes a function as argument that itself receives a data.frame
as argument (e.g. colMeans()
).
For a DivergenceExpressionSet run:
# visualizing the mean gene expression of each Divergence-Stratum class
PlotMeans(DivergenceExpressionSetExample, Groups = list(1:10), legendName = "DS",
xlab = "Ontogeny", lty = 1, cex = 0.7, lwd = 5)
To obtain the numerical values (mean expression levels for all Divergence-Strata) run:
# using the age.apply() function to compute the mean expression levels
# of all Divergence-Strata
age.apply(DivergenceExpressionSetExample, colMeans)
Zygote Quadrant Globular Heart Torpedo Bent Mature
1 5222.189 5230.547 5254.464 4911.494 4807.936 4654.683 4277.490
2 3146.510 3020.156 2852.072 2807.367 2845.025 3002.967 3237.315
3 2356.008 2239.344 2257.539 2272.270 2360.816 2529.276 2912.164
4 2230.350 2180.706 2050.895 2049.035 2001.043 2127.165 2608.903
5 2014.600 1994.640 1884.899 1851.554 1858.913 1920.185 2210.391
6 2096.593 2018.440 1938.765 1961.828 1905.246 2005.523 2339.767
7 1836.290 1832.815 1734.319 1719.186 1659.044 1736.141 2201.981
8 1784.470 1762.151 1635.529 1624.682 1590.489 1711.439 1983.607
9 1649.254 1659.455 1522.214 1485.560 1453.689 1584.176 1767.276
10 1660.750 1735.086 1605.275 1473.854 1398.067 1438.258 1541.633
Introduced by Domazet-Loso and Tautz (2010), relative expression levels are defined as a linear transformation of the mean expression levels (of each Phylostratum or Divergence-Stratum) into the interval [0,1] (Quint et al. (2012)). This allows the comparability between mean expression patterns between Phylostrata or Divergence-Strata independent from their actual magnitude.
The PlotRE()
function can be used (analogous to the PlotMeans()
function) to visualize the relative expression levels of a given PhyloExpressionSet and DivergenceExpressionSet:
# visualizing the mean gene expression of each Phylostratum class
PlotRE(PhyloExpressionSetExample, Groups = list(1:10), legendName = "PS",
xlab = "Ontogeny", lty = 1, cex = 0.7, lwd = 5)
# visualizing the mean gene expression of each Divergence-Stratum class
PlotRE(DivergenceExpressionSetExample, Groups = list(1:10), legendName = "DS",
xlab = "Ontogeny", lty = 1, cex = 0.7, lwd = 5)
or again by assigning Phylostratum or Divergence-Stratum groups that shall be visualized in different plots:
# visualizing the mean gene expression of each Phylostratum class
PlotRE(PhyloExpressionSetExample, Groups = list(1:3, 4:12), legendName = "PS",
xlab = "Ontogeny", lty = 1, cex = 0.7, lwd = 5)
The relative expression levels can be obtained using the REMatrix()
function:
# getting the relative expression levels for all Phylostrata
REMatrix(PhyloExpressionSetExample)
Zygote Quadrant Globular Heart Torpedo Bent Mature
1 0.4816246 0.3145330 0.46389184 0.00000000 0.17067495 0.56880234 1.0000000
2 1.0000000 0.9363209 0.63348381 0.40823711 0.14904726 0.00000000 0.2206063
3 0.6083424 0.4109402 0.00000000 0.09521758 0.16284114 0.21213845 1.0000000
4 0.2985050 0.2366309 0.04946941 0.07499453 0.00000000 0.20573325 1.0000000
5 0.2893657 0.2799777 0.00000000 0.01401191 0.01365328 0.45908792 1.0000000
6 0.2323316 0.2786335 0.02706119 0.00000000 0.03592044 0.20084761 1.0000000
7 0.5666979 0.2620602 0.00000000 0.12099252 0.07133814 0.13232551 1.0000000
8 0.4203039 0.3092784 0.09237036 0.05442042 0.00000000 0.45520558 1.0000000
9 0.4586261 0.4668613 0.39738003 0.23205534 0.00000000 0.37067096 1.0000000
10 0.8811321 1.0000000 0.53841500 0.22974016 0.00000000 0.30490542 0.4881046
11 0.4015809 0.4877111 0.04846721 0.05741594 0.00000000 0.07716367 1.0000000
12 0.5052572 0.3359211 0.07100055 0.09489782 0.00000000 0.25811214 1.0000000
# getting the relative expression levels for all Divergence-Strata
REMatrix(DivergenceExpressionSetExample)
Zygote Quadrant Globular Heart Torpedo Bent Mature
1 0.9669643 0.9755188 1.00000000 0.64894653 0.54294759 0.3860827 0.0000000
2 0.7888009 0.4949178 0.10397567 0.00000000 0.08758660 0.4549387 1.0000000
3 0.1733953 0.0000000 0.02704324 0.04893726 0.18054185 0.4309208 1.0000000
4 0.3772372 0.2955661 0.08201140 0.07895260 0.00000000 0.2074848 1.0000000
5 0.4543752 0.3987496 0.09292474 0.00000000 0.02050713 0.1912595 1.0000000
6 0.4403615 0.2605017 0.07713944 0.13021586 0.00000000 0.2307754 1.0000000
7 0.3264585 0.3200581 0.13864386 0.11077270 0.00000000 0.1420009 1.0000000
8 0.4934416 0.4366671 0.11457069 0.08697865 0.00000000 0.3076689 1.0000000
9 0.6236387 0.6561674 0.21851855 0.10163374 0.00000000 0.4161087 1.0000000
10 0.7794318 1.0000000 0.61482564 0.22487531 0.00000000 0.1192539 0.4259882
The same could also be done using the age.apply()
function in combination with the RE()
function:
# getting the relative expression levels for all Phylostrata
age.apply(PhyloExpressionSetExample, RE)
Zygote Quadrant Globular Heart Torpedo Bent Mature
1 0.4816246 0.3145330 0.46389184 0.00000000 0.17067495 0.56880234 1.0000000
2 1.0000000 0.9363209 0.63348381 0.40823711 0.14904726 0.00000000 0.2206063
3 0.6083424 0.4109402 0.00000000 0.09521758 0.16284114 0.21213845 1.0000000
4 0.2985050 0.2366309 0.04946941 0.07499453 0.00000000 0.20573325 1.0000000
5 0.2893657 0.2799777 0.00000000 0.01401191 0.01365328 0.45908792 1.0000000
6 0.2323316 0.2786335 0.02706119 0.00000000 0.03592044 0.20084761 1.0000000
7 0.5666979 0.2620602 0.00000000 0.12099252 0.07133814 0.13232551 1.0000000
8 0.4203039 0.3092784 0.09237036 0.05442042 0.00000000 0.45520558 1.0000000
9 0.4586261 0.4668613 0.39738003 0.23205534 0.00000000 0.37067096 1.0000000
10 0.8811321 1.0000000 0.53841500 0.22974016 0.00000000 0.30490542 0.4881046
11 0.4015809 0.4877111 0.04846721 0.05741594 0.00000000 0.07716367 1.0000000
12 0.5052572 0.3359211 0.07100055 0.09489782 0.00000000 0.25811214 1.0000000
Quint et al. (2012) introduced an additional way of visualizing the difference of relative expression levels between groups of Phylostrata/Divergence-Strata.
This bar plot comparing the mean relative expression levels of one Phylostratum/Divergence-Stratum group with all other groups can be plotted analogous to the PlotMeans()
and PlotRE()
functions:
# visualizing the mean relative expression of two Phylostratum groups
PlotBarRE(PhyloExpressionSetExample, Groups = list(1:3, 4:12),
xlab = "Ontogeny", ylab = "Mean Relative Expression", cex = 2)
Here the argument Groups = list(1:3, 4:12)
corresponds to dividing Phylostrata 1-12 into Phylostratum groups defined as origin before embryogenesis (group one: PS1-3) and origin during or after embryogenesis (group two: PS4-12). A Kruskal-Wallis Rank Sum Test is then performed to test the statistical significance of the different bars that are compared. The ’*’ corresponds to a statistically significant difference.
Additionally the ratio between both values represented by the bars to be compared can be visualized as function within the bar plot using the ratio = TRUE
argument:
# visualizing the mean relative expression of two Phylostratum groups
PlotBarRE(PhyloExpressionSetExample, Groups = list(1:3, 4:12), ratio = TRUE,
xlab = "Ontogeny", ylab = "Mean Relative Expression", cex = 2)
It is also possible to compare more than two groups:
# visualizing the mean relative expression of three Phylostratum groups
PlotBarRE(PhyloExpressionSetExample, Groups = list(1:3, 4:6, 7:12), wLength = 0.05,
xlab = "Ontogeny", ylab = "Mean Relative Expression", cex = 2)
For the corresponding statistically significant stages, a Posthoc test can be performed to detect the combinations of differing bars that cause the global statistical significance.
Most visualization functions in the myTAI
package have an ellipsis argument (?dotsMethods
) which allows you to modify the cex.lab
, cex.axis
, and cex
arguments within the plot.
# cex = 0.5, cex.lab = 0.5, cex.axis = 0.5
PlotPattern(PhyloExpressionSetExample, type = "l", lwd = 6, xlab = "Ontogeny", ylab = "TAI",
cex = 0.5, cex.lab = 0.5, cex.axis = 0.5)
The cex
argument modifies the the p-value font size, the cex.lab
argument modifies the font size of the x-axis and y-axis labels, and the cex.axis
argument modifies the font size of the axis labels.
More examples:
# cex = 2, cex.lab = 1, cex.axis = 0.5
PlotPattern(PhyloExpressionSetExample, type = "l", lwd = 6, xlab = "Ontogeny", ylab = "TAI",
cex = 2, cex.lab = 1, cex.axis = 0.5)
# cex = 0.5, cex.lab = 0.7, cex.axis = 1.5
PlotPattern(PhyloExpressionSetExample, type = "l", lwd = 6, xlab = "Ontogeny", ylab = "TAI",
cex = 0.5, cex.lab = 0.7, cex.axis = 1.5)
These arguments can analogously be used for all other plot functions, e.g. PlotMeans()
, PlotRE()
, etc.