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‎doc/Advanced-Tutorial.md‎

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‎doc/Analyzing-Big-Data.md‎

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---
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layout: userdoc
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title: "Analyzing Big Data"
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author: _AUTHOR_
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date: _DATE_
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docid: 31
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icon: info-circle
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doctype: manual
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tags:
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- tutorial
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description: "Hints and strategies to analyze big alignments with >= 1000 sequences or >= 10,000 sites."
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sections:
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---
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Analyzing big data
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==================
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Hints and strategies to analyze big alignments with >= 1000 sequences or >= 10,000 sites.
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<!--more-->
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TODO
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‎doc/Assessing-Phylogenetic-Assumptions.md‎

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---
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layout: userdoc
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title: "Assessing Phylogenetic Assumptions"
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author: _AUTHOR_
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date: _DATE_
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docid: 5
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icon: info-circle
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doctype: tutorial
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tags:
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- tutorial
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description: This guide is about evaluating the suitability of the data for phylogenetic analysis.
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sections:
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- name: Tests of symmetry
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url: tests-of-symmetry
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- name: Likelihood mapping
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url: likelihood-mapping
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---
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Assessing phylogenetic assumptions
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==================================
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It is important to know that phylogenetic models rely on various simplifying assumptions to
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ease computations. If your data severely violate these assumptions, it might
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cause bias in phylogenetic estimates of tree topologies and other model
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parameters. Some common assumptions include _treelikeness_ (all sites
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in the alignment have evolved under the same tree), _stationarity_ (nucleotide/amino-acid
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frequencies remain constant over time), _reversibility_ (substitutions are equally
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likely in both directions), and _homogeneity_ (substitution rates remain constant over time).
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This document shows several ways to check some of these assumptions that you
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should perform before doing phylogenetic analysis.
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Tests of symmetry
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-----------------
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IQ-TREE provides three matched-pairs tests of symmetry ([Naser-Khdour et al., 2019]) to
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test the two assumptions of _stationarity_ and _homogeneity_.
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A simple analysis:
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iqtree3 -s example.phy -p example.nex --symtest-only
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will perform the three tests of symmetry on every partition of the alignment
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and print the result into a `.symtest.csv` file. `--symtest-only` option tells
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IQ-TREE to only perform the tests of symmetry and then exit.
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In this example the content of `example.nex.symtest.csv` looks like this:
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```
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# Matched-pair tests of symmetry
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# This file can be read in MS Excel or in R with command:
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# dat=read.csv('example.nex.symtest.csv',comment.char='#')
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# Columns are comma-separated with following meanings:
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# Name: Partition name
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# SymSig: Number of significant sequence pairs by test of symmetry
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# SymNon: Number of non-significant sequence pairs by test of symmetry
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# SymPval: P-value for maximum test of symmetry
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# MarSig: Number of significant sequence pairs by test of marginal symmetry
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# MarNon: Number of non-significant sequence pairs by test of marginal symmetry
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# MarPval: P-value for maximum test of marginal symmetry
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# IntSig: Number of significant sequence pairs by test of internal symmetry
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# IntNon: Number of non-significant sequence pairs by test of internal symmetry
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# IntPval: P-value for maximum test of internal symmetry
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Name,SymSig,SymNon,SymPval,MarSig,MarNon,MarPval,IntSig,IntNon,IntPval
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part1,44,92,0.475639,50,86,0.722371,4,132,0.23869
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part2,43,93,0.142052,49,87,0.205232,5,131,0.169618
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part3,53,83,0.00499855,58,78,0.00164132,6,130,0.343127
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```
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The three important columns are:
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* SymPval: a small p-value (say < 0.05) indicates that the assumptions of stationarity
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or homogeneity or both is rejected. In this case, partition `part3` does not comply with these
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two assumptions (p-value = 0.00499855), whereas the other two partitions are "good".
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* MarPval: a small p-value means that the assumption of stationarity is rejected. In
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this case, only partition `part3` does not comply with the stationary condition (p-value = 0.00164132).
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* IntPval: a small p-value means that the homogeneity assumption is reject. In
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this case, no partitions are "bad" according to this test, i.e., they all comply with
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the homogeneity assumption.
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This little example shows that only `part3` is problematic by not complying with the
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stationary assumption.
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Now you may want to perform the phylogenetic analysis excluding all "bad" partitions by:
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iqtree3 -s example.phy -p example.nex --symtest-remove-bad
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that will remove all "bad" partitions where SymPval < 0.05 and continue the analysis with the
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remaining "good" partitions. You may then compare the trees from "all" partitions
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and from "good" only partitions to see if there is significant difference between them
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with [tree topology tests](Advanced-Tutorial#tree-topology-tests).
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Other options can be seen when running `iqtree3 -h`:
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```
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TEST OF SYMMETRY:
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--symtest Perform three tests of symmetry
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--symtest-only Do --symtest then exist
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--symtest-remove-bad Do --symtest and remove bad partitions
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--symtest-remove-good Do --symtest and remove good partitions
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--symtest-type MAR|INT Use MARginal/INTernal test when removing partitions
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--symtest-pval NUMER P-value cutoff (default: 0.05)
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--symtest-keep-zero Keep NAs in the tests
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```
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Likelihood mapping
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------------------
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<div class="hline"></div>
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Likelihood mapping ([Strimmer and von Haeseler, 1997]) is a visualisation method
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to display the phylogenetic information of an alignment. It visualises the _treelikeness_
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of all quartets in a single triangular graph and therefore renders a quick
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interpretation of the phylogenetic content.
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A simple likelihood mapping analysis can be conducted with:
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iqtree -s example.phy -lmap 2000 -n 0
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where `-lmap` option specify the number of quartets of taxa that will be drawn randomly
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from the alignment. `-n 0` tells IQ-TREE to stop the analysis right after running the
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likelihood mapping. IQ-TREE will print the result in the `.iqtree` report file as well
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as the likelihood mapping plot `.lmap.svg` (in SVG format) and `.lmap.eps` file (in EPS
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figure format).
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You can now view the likelihood mapping plot file `example.phy.lmap.svg`, which looks like this:
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![Likelihood mapping plot.](images/example.phy.lmap.pdf)
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It shows phylogenetic information of the alignment `example.phy`.
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* Top sub-figure: distribution of quartets depicted by dots on the likelihood mapping plot.
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* Left sub-figure: percentages of quartets falling in each of the three areas. The
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three areas show support for one of the different groupings like (a,b)-(c,d).
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* Right sub-figure: percentages of quartets falling in each of the seven areas.
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Quartets falling into the three corners are informative and called fully-resolved quartets.
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Those in three rectangles are partly informative (partly resolved quartets) and those in the center are uninformative
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(unresolved quartets). A good data set should have high number of fully resolved quartets
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and low number of unresolved quartets.
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The meanings can also be found in the `LIKELIHOOD MAPPING STATISTICS` section of the
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report file `example.phy.iqtree`:
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LIKELIHOOD MAPPING STATISTICS
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-----------------------------
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(a,b)-(c,d) (a,b)-(c,d)
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/\ /\
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/ \ / \
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/ \ / 1 \
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/ a1 \ / \ / \
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/\ /\ / \/ \
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/ \ / \ / /\ \
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/ \ / \ / 6 / \ 4 \
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/ \/ \ /\ / 7 \ /\
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/ | \ / \ /______\ / \
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/ a3 | a2 \ / 3 | 5 | 2 \
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/__________|_________\ /_____|________|_____\
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(a,d)-(b,c) (a,c)-(b,d) (a,d)-(b,c) (a,c)-(b,d)
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Division of the likelihood mapping plots into 3 or 7 areas.
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On the left the areas show support for one of the different groupings
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like (a,b|c,d).
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On the right the right quartets falling into the areas 1, 2 and 3 are
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informative. Those in the rectangles 4, 5 and 6 are partly informative
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and those in the center (7) are not informative.
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.....
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The [command reference](Command-Reference#likelihood-mapping-analysis) will provide
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more options and how to perform 2-, 3-, or 4-cluster likelihood mapping analysis.
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[Strimmer and von Haeseler, 1997]: http://www.pnas.org/content/94/13/6815.long
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[Naser-Khdour et al., 2019]: https://doi.org/10.1093/gbe/evz193
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