Monday, December 25, 2017

Christmas tree networks


Greetings for the season.

Mathematicians live in a world of their own — individually, as well as collectively. Therefore, it is inevitable that among all of the graphs with names like pancake graphs, butterfly networks, star graphs, spider web networks, and brother trees, there should be a thing called a Christmas tree.


It was introduced by Chun-Nan Hung, Lih-Hsing Hsu and Ting-Yi Sung (1999) Christmas tree: a versatile 1-fault-tolerant design for token rings. Information Processing Letters 72: 55-63.

As you can see, it is a network, as well as a tree. The authors describe it as "a 3-regular, planar, [optimal] 1-Hamiltonian, and Hamiltonian-connected graph." The Hamiltonian characteristic refers to the existence of a path through the network that connects all of the nodes and ends up back at the start node (ie. a cycle). The Christmas tree is created by joining two Slim trees together, believe it or not. (The Fat tree is a Slim tree with an extra edge connecting the left and right nodes.)

The authors' particular interest was in communications networks (eg. computer networks); but to me it looks like a (historical) phylogenetic tree at the top with an extra network added at the bottom showing (contemporary) ecological connections. It could thus summarize all of the biology in one diagram!

You can read about all of the above-mentioned graph types in the book by Lih-Hsing Hsu and Cheng-Kuan Lin (2009) Graph Theory and Interconnection Networks; CRC Press. You need to be a mathematician to make sense of it, though.

Thanks to Bradly Alicea for alerting me to this graph type.

PS. The above diagram is actually take from: Jeng-Jung Wang, Chun-Nan Hung, Jimmy JM Tan, Lih-Hsing Hsu, Ting-Yi Sung (2000) Construction schemes for fault-tolerant Hamiltonian graphs. Networks 35: 233-245.

Tuesday, December 19, 2017

The art of doing science: alignments in historical linguistics


In the past two years, during which I have been writing for this blog, I have often tried to emphasize the importance of alignments in historical linguistics — alignment involves explicit decisions about which characters / states are cognate (and can thus be aligned in a data table). I have also often mentioned that explicit alignments are still rarely used in the field.

To some degree, this situation is frustrating, since it seems so obvious that scholars align data in their head, for example, whenever they write etymological dictionaries and label parts of a word as irregular, not fulfilling their expectations when assuming regular sound change (in the sense in which I have described it before). It is also obvious that linguists have been trying to use alignments before (even before biologists, as I tried to show in this earlier post), but for some reason, they never became systematized.

As an example for the complexity of alignment analyses in historical linguistics, consider the following figure, which depicts both an early version of an alignment (following Dixon and Kroeber 1919), and a "modern" version of the dame data. For the latter, I used the EDICTOR (http://edictor.digling.org), a software tool that I have been developing during recent years, and which helps linguists to edit alignments in a consistent way (List 2017). The old version on the left has been modified in such a way that it becomes clearer what kind of information the authors tried to convey (for the original, see my older post), while the EDICTOR version contains some markup that is important for linguistics, which I will discuss in more detail below.

Figure 1: Alignments from Dixon and Kroeber (1919) in two flavors

If we carefully inspect the first alignment, it becomes evident that the scholars did not align the data sound by sound, but rather morpheme by morpheme. Morphemes are those parts in words that are supposed to bear a clear-cut meaning, even when taken in isolation, or when abstracting from multiple words. The plural-ending -s in English, for example, is a morpheme that has the function to indicate the plural (compare horse vs. horses, etc.). In order to save space, the authors used abbreviations for the language group names and the names for the languages themselves.

The authors have further tried to save space by listing identical words only once, but putting two entries, separated by a comma, in the column that I have labelled "varieties". If you further compare the entries for NW (=North-Western Maidu) and NE/S (=North-Eastern Maidu and Southern Maidu), you can see that the first entry has been swapped: the tsi’ in tsi’-bi in NW is obviously better compared with the tsi in NE/S bi-tsi rather than comparing bi in NE with tsi in NE/S. This could be a typographical error, of course, but I think it is more likely that the authors did not quite know how to handle swapped instances in their alignment.

In the EDICTOR representation of the alignment, I have tried to align the sounds in addition to aligning the morphemes. My approach here is rather crude. In order to show which sounds most likely share a common origin, I extracted all homologous morphemes, aligned them in such a way that they occur in the same column, and then stripped off the remaining sounds by putting a check-mark in the IGNORE column on the bottom of the EDICTOR representation. When further analyzing these sound correspondences with some software, like the LingPy library (List et al. 2017), all sounds that occur in the IGNORE column will be ignored. Correspondences will then only be calculated for the core part of this alignment, namely the two columns that are left over, in the center of the alignment.

In many cases, this treatment of sound correspondences and homologous words in alignments is sufficient, and also justified. If we want to compare the homologous (cognate) parts across words in different languages, we can't align the words entirely. Consider, for example, the German verb gehen [geːən] and its English counterpart go [gɔu]. German regularly adds the infinitive ending -en to each verb, but English has long ago dropped all endings on verbs apart from the -s in the third person singular (compare go vs. goes). Comparing the whole of the verbs would force us to insert gaps for the verb ending in German, which would be linguistically not meaningful, as those have not been "gapped" in English, but lost in a morphological process by which all endings of English verbs were lost.

There are, however, also cases that are more complicated to model, especially when dealing with instances of partial cognacy (or partial homology). Compare, for example, the following alignment for words for bark (of a tree) in several dialects of the Bai language, a Sino-Tibetan language spoken in China, whose affiliation with other Sino-Tibetan languages is still unclear (data taken from Wang 2006).

Figure 2: Alignment for words for "bark" in Bai dialects

In this example, the superscript numbers represent tones, and they are placed at the end of each syllable. Each syllable in these languages usually also represents a morpheme in the sense mentioned above. That means, that each of the words is a compound of two original meanings. Comparison with other words in the languages reveals that most dialects, apart from Mazhelong, express bark as tree-skin, which is a very well-known expression that we can find in many languages of the world. If we want to analyze those words in alignments, we could follow the same strategy as shown above, and just decide for one core part of the words (probably the skin part) and ignore the rest. However, for our calculations of sound correspondences, we would loose important information, as the tree part is also cognate in most instances and therefore rather interesting. But ignoring only the unalignable part of the first syllable in Mazhelong would also not be satisfying, since we would again have gaps for this word in the tree part in Mazhelong which do not result from sound change.

The only consistent solution to handle these cases is to split the words into their morphemes, and then to align all sets of homologous morphemes separately. This can also be done in the EDICTOR tool (but it requires more effort from the scholar and the algorithms). An example is shown above, where you can see how the tool breaks the linear order in the representation of the words as we find them in the languages, in order to cluster them into sets of homologous "word-parts".

Figure 3: Alignments of partial cognates in the Bai dialects

But if we only look at the tree part of those alignments, namely the third cognate set from the left, with the ID 8, we can see a further complication, as the gaps introduced in some of the words look a little bit unsatisfying. The reason is that the j in Enqi and Tuolo may just as well be treated as a part of the initial of the syllable, and we could re-write it as dj in one segment instead of using two. In this way, we might capture the correspondence much more properly, as it is well known that those affricate initials in the other dialects ([ts, tʂ, dʐ, dʑ]) often correspond to [dj]. We could thus rewrite the alignment as shown in the next figure, and simply decide that in this situation (and similar ones in our data), we treat the d and the j as just one main sound (namely the initial of the syllables).

Figure 4: Revised alignment of "tree" in the sample

Summary and conclusions

Before I start boring those of the readers of this blog who are not linguists, and not particularly interested in details of sound change or language change, let me just quickly summarize what I wanted to illustrate with these examples. I think that the reason why linguists never really formalized alignments as a tool of analysis is that there are so many ways to come up with possible alignments of words, which may all be reasonable for any given analysis. In light of this multitude of possibilities for analysis, not to speak of historical linguistics as a discipline that often prides itself by being based on hard manual labor that would be impossible to achieve by machines, I can in part understand why linguists were reluctant to use alignments more often in their research.

Judging from my discussions with colleagues, there are still many misunderstandings regarding the purpose and the power of alignment analyses in historical linguistics. Scholars often think that alignments directly reflect sound change. But how could they, given that we do not have any ancestral words in our sample? Alignments are a tool for analysis, and they can help to identify sound change processes or to reconstruct proto-forms in unattested ancestral languages; but they are by no means the true reflection of what happened and how things changed. The are the starting point, not the end point of the analysis. Furthermore, given that there are many different ways in which we can analyze how languages changed over time, there are also many different ways in which we can analyze language data with the help of alignments. Often, when comparing different alignment analyses for the same languages, there is no simple right and wrong, just a different emphasis on the initial analysis and its purpose.

As David wrote in an email to me:

"An alignment represents the historical events that have occurred. The alignment is thus a static representation of a dynamic set of processes. This is ultimately what causes all of the representational problems, because there is no necessary and sufficient way to achieve this."

This also nicely explains why alignments in biology as well, with respect to the goal of representing homology, "may be more art than science" (Morrison 2015), and I admit that I find it a bit comforting that biology has similar problems, when it comes to the question of how to interpret an alignment analysis. However, in contrast to linguists, who have never really given alignments a chance, biologists not only use alignments frequently, but also try to improve them.

If I am allowed to have an early New Year wish for the upcoming year, I hope that along with the tools that facilitate the labor of creating alignments for language data, we will also have a more vivid discussion about alignments, their shortcomings, and potential improvements in our field.

References
  • Dixon, R. and A. Kroeber (1919) Linguistic families of California. University of California Press: Berkeley.
  • List, J.-M. (2017) A web-based interactive tool for creating, inspecting, editing, and publishing etymological datasets. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. System Demonstrations, pp. 9-12.
  • LingPy: A Python library for historical linguistics. Version 2.6. Max Planck Institute for the Science of Human History: Jena.
  • Morrison, D. (2015) Molecular homology and multiple-sequence alignment: an analysis of concepts and practice. Australian Systematic Botany 28: 46-62.
  • Wang, W.-Y. (2006) Yǔyán, yǔyīn yǔ jìshù \hana 語言,語音與技術 [Language, phonology and technology]. Xiānggǎng Chéngshì Dàxué: Shànghǎi 上海.

Tuesday, December 12, 2017

Using consensus networks to understand poor roots


The standard way to root a tree is by outgroup rooting. Comprehensive outgroup sampling should minimize bias, such as ingroup-outgroup long-branch attraction (IO-LBA); but this may be impossible to avoid, when ingroup and all possible outgroups are very distant from each other. Using a recent all-Santalales up-to-7-gene dataset (Su et al. 2015) as an example, I will show how bootstrap consensus networks can illuminate topological issues associated with poorly supported roots.

These analyses should be obligatory whenever critical branches lack unambiguous support.

Some background: nothing to see, all is solved

The reason, why I had to re-analyse some of the data of Su et al., was that we dared to discuss alternatives to the accepted Loranthaceae (hereafter "loranth") root in our paper on loranth pollen (Grímsson, Grimm & Zetter 2017). Mainly for the artwork, I harvested gene banks for data on loranths in 2014 (re-checked in 2016, but no-one is studying this highly interesting group anymore). Earlier studies had used different taxon and gene sets, and produced cladograms (trees without branch lengths).

To assess the systematic value of loranth pollen, we needed to know how well resolved are intra-family relationships. Many deeper branches seen in the trees were not supported. What would be the competing alternatives? Unfortunately, one of our reviewers (#1), a veteran expert on plant parasites including loranths but not phylogenetics, was tremendously alienated by our tree, notably, the non-supported parts. He refused to understand the difference between a simple tree topology and branch support. Matrices producing ambiguous signals not-rarely lead to optimized trees that do not show the best-supported topological alternatives. Hence, for mapping our pollen, we did not use our tree, but the ML bootstrap consensus network (Fig. 1; which #1 refused even to look at). Fig. 2 shows the first plot focusing on the deepest, ambiguous relationships.

Fig. 1 The basis for our paper: a ML bootstrap consensus network, rooted under the commonly accepted assumption that the monotypic Nuytsia represents the first-diverging lineage.

Fig. 2 Example of what we did. My co-authors (re-)studied loranth pollen using high-res SEM microscopy; and then we plotted the pollen on the relevant part of the ML bootstrap consensus network (Fig. 1). Green background: the three surviving root parasites of the root-parasitic grade (not supported based on our data set) in outgroup rooted trees; orange background: the palynologically odd Tupeia. Note the lack of clear signal regarding root-proximal relationships, an indication for fast ancient radiation and diversification involving both root and aerial parasitic lineages.

Reviewer #1 pointed to the recent paper of Su et al., which (according to his interpretation) resolved the intrafamily relationships. Since our tree was topologically somewhat different, and did not provide the same level of support, it had to be wrong. My argument was that the genetic data are inconclusive regarding critical branches in the loranth subtree. This is obvious from our bootstrap network (not including any outgroup, to avoid ingroup-outgroup branching artefacts), and also from the earlier molecular phylogenies, including Su et al.’s. Plus, to focus on the loranths we included the best-sampled and most-divergent plastid region, a non-coding region, which was not included in Su et al. because it is unalignable across the order.

A particularly "absurd" notion (according to #1) was that we suggested using pollen morphology to pre-define an alternative root (the best-fitting alternative according to Bayes factors; Grímsson, Kapli et al. 2017). All loranths share a unique, easy to distinguish, triangular pollen morph (Fig. 1) that can be traced back till the Eocene, except for one monotypic genus: Tupeia (antarctica) from New Zealand (ie. at the margin of their modern distribution). Tupeia’s pollen is spheroidal (Fig. 1), the basic form found all over the Santalales tree (Fig. 3). Fig. 4 shows alternative rooting scenarios using Su et al.'s loranth subtree.

Fig. 3 Schematic drawings of Santalales pollen, mapped on the preferred tree of Su et al. (cf. Grímsson, Zetter & Grimm, 2017, pp. 36–41)

Fig. 4 I used this figure in one of the rebuttal letters to explain basic evolutionary concepts triggered by different rooting scenarios using the same tree, the one preferred by Su et al. (#1, and #3, did not realize trees are optimized unrooted). Note: aerial parasitism evolved independently several times in the Santalales (Vidal-Russell & Nickrent 2007), but the triangular loranth pollen is unique. Using Bayes Factors, we established that the pollen-based root is the best-fitting for the data set we used for node dating in Grímsson, Kapli et al. (2017).

Why I lacked confidence in the Su et al. tree(s)

Su et al.’s study establishes relationships within the Santalales using a quite gappy up-to-7-gene data set (3 nuclear + 3 plastid + 1 mitochondrial genes) to revisit the placement of an originally poorly defined family with extremely long-branching members, the Balanophoraceae s.l. They found two main clades, one – the resurrected Mystropetalaceae – reconstructed as sister of the loranths (see scheme in Fig. 3). From what the authors show (but do not discuss), there are a lot of signal issues with their data:
  • The standard nucleotide tree (partition scheme unclear) in the main text differs in many critical aspects (branching patterns, root-tip length ratios, support values) from the two in the supplement (one based on removing the 3rd codon positions, and one amino acid based).
  • Many branches have high posterior-probabilities but low ML bootstrap values (BS), a pattern typical for mixed-genome oligogene data sets with intrinsic conflict.
Regarding the loranth root question:
  • The first three branches within the loranth subtree, defining a ‘root parasitic grade’, have low to high posterior probabilities (PP = 0.65 / 0.99 / 0.52) and consistently low ML bootstrap supports (BS = <50 / 53 / <50).
  • The sister clades have extremely long branches, and the putative sister clade, the Mystropetalaceae, is only covered for nuclear and mitochondrial data. All probabilistic inference methods treat missing data and gaps as “N”; ie. the terminal probability vector p equals (1,1,1,1) for p(A,C,G,T). In case the signal from the best-sampled regions is clear, missing data is no issue; if there is conflict then missing data can be a problem.
In addition, I’m generally skeptical when it comes to “basal grades” and roots inferred by very distinct outgroups. Except for the three species forming the root-parasitic grade (one in South America, two in Australasia), all loranths are aerial parasites. Loranths are a ubiquitous, highly competitive tropical-subtropical group extending into temperate climates with proper summers and little snow. They have a very extensive and relatively old pollen record across all pieces of Gondwana and Laurasia (Grímsson, Kapli et al. 2017, and references therein). The main lineages are sorted continent-wise, and the shift from root to aerial parasitism is supposed to have happened several times within the Santalales and its families. But only once in loranths? And how could only one root-parasitic lineage survive in South America, and two more in Australasia; all with but a single species?

What’s behind the ambiguous bootstrap supports

For my head-to-head with the editor and reviewers (the editor had invited a #3 reviewer, an anonymous “expert on phylogeny”), I re-analysed Su et al.’s data subset on loranths plus sister groups. By reducing the outgroup sample to the sister clades only (Misodendraceae+Schoepfiaceae, Mystropetalaceae), I increased the BS support (100 / 62 / 60) for the root parasitic grade. This indicates either that Su et al.’s analysis was not comprehensive or that IO-LBA (ingroup-outgroup long-branch attraction) is prominent.

Large outgroup samples, all other Santalales + few non-Santalales in Su et al. vs. only loranths and sister clades in my re-analysis, may alleviate LBA to some degree (eg. Sanderson et al. 2000; Stefanović, Rice & Palmer 2004). Higher support with fewer outgroups than with many in the same dataset can be an alarm flag.

The ingroup-only bootstrap consensus network (Fig. 5) shows moderate, but unchallenged support for a split between root and aerial parasites, which agrees with Su et al.’s all-Santalales tree. The low support for the first and second branch in outgroup-rooted trees relates to an ambiguous signal between the root parasites — the signal from the combined data are ambiguous as to whether Atkinsonia (the second Australasian root parasite) or Gaiadendron (the South American root parasite) is the second diverging branch. Note that the position of our (pollen-based) alternative root candidate Tupeia is also unresolved (no split with a BS > 20; compare Figs 1 and 5), which could be expected for a potentially ancestral, relatively underived taxon — a taxon literally close to the family's root.

For most other deeper branches with high PP but low BS in Su et al.’s loranth subtree, the network shows two competing topological alternatives. There is thus an intrinsic signal conflict in Su et al.’s matrix, which explains the topological differences in earlier studies using different gene and taxon sets, preferring the one or other alternative in the final cladogram.

Fig. 5 ML Bootstrap consensus network using Su et al.'s loranth data subset. A, the outgroup-inferred root; B, pollen-based root; C, a clock-based root inferred by Grímsson, Kapli et al. (2017). In contrast to Su et al.'s tree the network distinguishes between ambiguous and unambiguous relationships. Not too different from what we found using a more data-dense and divergent matrix (Fig. 1)

How to deconstruct a tree:

First step — single-gene trees and support networks

There are two main reasons for inferring single-gene trees and establishing single-gene support:
  1. It is the only way assess the level of incongruence in the gene samples.
  2. They inform about the amount of signal that each gene region contributes to the combined tree.
In case of Su et al.’s data set, it becomes clear that the combined topology is supported primarily by signal from the best-sampled (and most variable) plastid gene included in the data set: the plastid matK gene. The second, much less divergent, plastid gene (rbcL) partly reinforces the matK signal, or at least does not contradict it (except for a few odd-balls, which might be possible mix-ups during sequence generation).

It is also clear that the nuclear and plastid data fit aspect-wise, eg. both support or at least don’t reject potential clades, but not entirely. Particularly, the placements of the sister groups of the loranths within the loranth tree are unstable, ie. the position of the outgroup-inferred root.

Fig. 6 One-to-one comparison of trees inferred using the two most divergent nuclear and plastid genes included in Su et al.'s matrix: the matK (plastid) and 25S rDNA (nuclear-encoded ribosomal RNA gene; same scale). The root-parasitic grade and a loranth | sister clades split is moderately to well-supported using matK data, but not using 25S rDNA data. No plastid data were included for the assumed direct sister of loranths, the Mystropetalaceae. Note also the position of Tupeia (dark red) in both graphs, the only loranth without loranth-typical pollen.

Last, the single-gene trees and bootstrap networks illustrate a severe taxon sampling bias. The third nuclear marker, the RBS2 gene, is only covered for five ingroup taxa including Nuytsia but no other root parasite, and it fails to resolve the two aerial tribes, which are otherwise genetically distinct. Analysis of this small subset illustrates nicely the perils of taxon sampling (Fig. 7). The third plastid gene, the accD gene (7 ingroup representatives), supports Nuytsia as the first diverging lineage, but rejects the root parasite grade with equally high support (BS = 87)

Fig. 7 Some RPB2 trees, the new nuclear gene included by Su et al. (not included in our data set lacking taxonomic coverage). A, the tree including the sister clades of loranths. B, ingroup loranth-only tree, codon-positions treated as different data partitions; C, same, unpartitioned analysis. D, the "true tree" (according #1), ie. topology seen in Su et al. Green, splits in agreement with Su et al.'s tree, red, splits contrasting Su et al.'s tree.

The only mitochondrial gene included in the data set (matR) rejects the root-parasitic grade, in addition to the matK-only based Elytrantheae-Lorantheae clade seen in Su et al.’s tree, with nearly unambiguous support. However, it is in general low-divergent, and it may be that the authors relied on fragmentary (potentially pseudogene) data for some taxa. There are huge indels in the gene not found in any other angiosperm, and the two sequenced Loranthus are conspicuously distinct to the other loranths (Fig. 8).

Fig. 8 Neighbour-net based on Su et al.'s matR data. Numbers show the ML bootstrap support for (alternative) splits when sister groups are included or excluded. Note the position of the Mystropetalaceae, possible affected by IO-LBA with the Loranthinae (the latter's signal is in stark contrast to any other gene region). Two root-parasitic species are included: NuyFl = Nuytsia, GaiaPun = Gaiadendron; no matR for Tupeia.

One of the arguments of reviewer #1 was that Su et al. obtained a better resolved phylogeny because they sampled these extra gene regions. In fact, they obtained a better supported phylogeny because they added diffuse-signal or under-sampled gene regions. Hence, they enforced the dominant matK-preferred topology (high PP, low BS), while weaking locally conflicting signal from the nuclear 18S and 25S rDNA.

The only constant is that, no matter which gene region, Nuytsia is always the most distinct taxon of all species close to the root node. With respect to the very distant sister clades, IO-LBA may be inevitable. The BS consensus networks favor a closer relationship of the root parasites, and there is relatively strong signal for a root parasitic clade in the matK (Fig. 5), so that the biased outgroup-root then becomes a proximal (‘basal’) grade (Fig. 6, left).

Data effects of the LBA-missing sort also challenge the placement of the Mystropetalaceae as sister to the loranths. The plastid data establish that Misodendraceae and Schoepfiaceae (M+S) are distinct from the loranths. They provide unambiguous support for a loranth versus M+S split. Having no data on any plastid gene, the extremely long-branched Mystropetalaceae fall in line. Being very distinct from both M+S and loranths in the nuclear (Fig. 5) and mitochondrial (Fig. 8) genes, the tree places them next to the loranths, because the latter's root node and many of its members are much more distant from the all-ancestor than those of the M+S clade (compare with Su et al.'s trees; keep in mind that missing data may increase branch-lengths).

Second step—remove a taxon, or two, or a gene region

If the hypothesis of a root-parasitic grade holds, then one should be able to remove the first diverging taxon (Nuytsia in this example) from the dataset, and still have the position of the second (Atkinsonia) and third diverging (Gaiadendron) remain the same. Furthermore, the support for the corresponding branches should at best increase, and at least not decrease. Atkinsonia is relatively distinct, but Gaiadendron is not (as reflected by their pollen morphology, Fig. 2).

In the case of Su et al.’s data, removal of Nuytsia collapses the root-proximal part of the ingroup tree (Fig. 9). ML-tree inferences select a sub-optimal topology, because the signal weakens for the splits {Atkinsonia + outgroups | all other Loranthaceae} and {root parasites + outgroups | aerial parasites}. The competing alternative (BS = 23) is a first-diverging Atkinsonia-Gaiadendron clade. When both Nuytsia and Atkinsonia are removed, Gaiadendron is placed within the aerial parasites, and the Elytrantheae, a low-diverged Australasian tribe, are favored as first-diverging loranth clade (BS increases from 15 to 39; any alternative BS is < 20; see also their placements in Fig. 5).

Fig. 9 ML tree and bootstrap (top-right) consensus network using Su et al.'s data with the putative first-branching Nuytsia eliminated from the equation (see above). The tree is rooted according to Su et al.'s tree.

Equally disastrous for the support of a root parasitic grade is the elimination of matK from the combined data set. This is a major advantage of BS or support consensus networks: one can estimate whether taxon or gene sampling increases or decreases support for competing topologies; and not only the combined trees’ topologies.

Support consensus networks should be obligatory when studying non-trivial or suboptimal data sets

When it comes to extremely long-branching roots and sister groups, one should generally be careful with combined-data roots defined by outgroups (see also our discussion of the outgroup-inferred Osmundaceae root in Bomfleur, Grimm & McLoughlin 2015). But when the crucial branches in addition lack consistent support, one should actually be alarmed.

In the case of gappy oligogene (as used by Su et al.) or multigene data, one should always test the primary signal in the combined gene regions. Fast and efficient ML implementations make it very easy to infer and compare single-gene trees, and thus establish differential branch support patterns, which can be visualized and investigated using support consensus networks. As biologists working with complex systems and, likely, non-trivial evolutionary pathways, we should not be afraid of conflicting splits patterns, but deal with them!

It can also be useful to plot (tabulate) competing support values from different analysis. RAxML has an option that allows testing the direct correlation between two bootstrap (or Bayesian) tree samples, without being restricted to any particular tree. The Phangorn library for R includes functions to map trees into networks, and transfer support from networks to trees. In my experience, many nuclear and plastid incongruences are:
  1. tree-based, meaning the trees are different, but the supports for topological alternatives are not conflicting;
  2. resolution-based, meaning the nuclear data illuminate different parts of the evolutionary history compared to the plastid data;
  3. rogue-based, meaning there are only a few taxa with strongly conflicting signals in the tree.
Support consensus networks can pinpoint all three scenarios. In the case of scenarios 1 and 2, a combined analysis still works, as also in the case of scenario 3 when the rogues are eliminated (from the combined analysis, not from the study, which unfortunately still a tradition in phylogenetics)

Uploading a matrix and the optimized tree to (eg.) TreeBase is just the first step (although many TreeBase submissions include only naked trees). In the case of ambiguous branch support (BS < 80 or 85, depending on the proportion of nuclear vs. mitochondrial and plastid gene regions; PP < 1.00), much more important would be to document the bootstrap replicate and Bayesian tree samples.

Furthermore, reviewers and editors should not encourage the publishing of trees where low support is masked by cut-offs (eg. BS < 50, PP < 0.95). Which is, unfortunately, the editorial policy of the journal that published the Su et al. study, and the reason for the scarcity of studies showing support consensus or other networks that can illuminate signal issues (but see eg. Denk & Grimm 2010, Wanntorp et al. 2014, and Khanum et al. 2016, published in Taxon). The latter a common feature in plant molecular data sets, independent of the taxonomic hierarchical level.

Further reading, and data

The full details of my re-analysis of Su et al.’s data subset, including loranths and sister clades, can be found in File S6 [PDF] to Grímsson, Grimm & Zetter (2017), included in the Online Supporting Archive (9.5 MB; journal hompage/mirror]. The relevant data and analysis files can be found in subfolder "Su_el_al." of the archive.

References

Bomfleur B, Grimm GW, McLoughlin S (2015) Osmunda pulchella sp. nov. from the Jurassic of Sweden — reconciling molecular and fossil evidence in the phylogeny of modern royal ferns (Osmundaceae). BMC Evolutionary Biology 15: 126. http://dx.doi.org/10.1186/s12862-015-0400-7

Denk T, Grimm GW. 2010. The oaks of western Eurasia: traditional classifications and evidence from two nuclear markers. Taxon 59:351–366. — Includes neighbour-nets based on inter-individual and uncorrected p-distances, with ML branch support mapped and tabulated.

Grímsson F, Grimm GW, Zetter R (2017) Evolution of pollen morphology in Loranthaceae. Grana DOI:10.1080/00173134.2016.1261939. http://dx.doi.org/10.1080/00173134.2016.1261939

Grímsson F, Kapli P, Hofmann C-C, Zetter R, Grimm GW (2017) Eocene Loranthaceae pollen pushes back divergence ages for major splits in the family. PeerJ 5: e3373 [e-pub]. https://peerj.com/articles/3373/ Don't miss reading the peer review documentation, which includes some interesting discussion. (If you agree that the peer review should be transparent in general, then you can sign up here for fighting the windmills.)

Khanum R, Surveswaran S, Meve U, Liede-Schumann S. 2016. Cynanchum (Apocynaceae: Asclepiadoideae): A pantropical Asclepiadoid genus revisited. Taxon 65:467–486. — Includes trees, median-joining and bootstrap consensus networks [I really enjoyed working on this, but retreated from co-authorship to protest against Taxon's editorial policies.]

Sanderson MJ, Wojciechowski MF, Hu J-M, Sher Khan T, Brady SG (2000) Error, bias, and long-branch attraction in data for two chloroplast photosystem genes in seed plants. Molecular Biology and Evolution 17: 782-797.

Stefanović S, Rice DW, Palmer JD (2004) Long branch attraction, taxon sampling, and the earliest angiosperms: Amborella or monocots? BMC Evolutionary Biology 4: 35. http://biomedcentral.com/1471-2148/4/35

Su H-J, Hu J-M, Anderson FE, Der JP, Nickrent DL (2015) Phylogenetic relationships of Santalales with insights into the origins of holoparasitic Balanophoraceae. Taxon 64: 491-506.

Vidal-Russell R, Nickrent DL (2008) The first mistletoes: origins of aerial parasitism in Santalales. Molecular Phylogenetics and Evolution 47: 523-537.

Wanntorp L, Grudinski M, Forster PI, Muellner-Riehl AN, Grimm GW. 2014. Wax plants (Hoya, Apocynaceae) evolution: epiphytism drives successful radiation. Taxon 63:89–102. — Includes trees and a "splits rose" [The editors forced us do drop some quite nice bootstrap consensus and median-joining networks; see also this post]

Tuesday, December 5, 2017

The Synoptic Gospels problem: preparing a phylogenetic approach


This is the second part of my series on phylogenetics and a specific case of textual criticism, the Biblical one. The first part appeared as Another test case for phylogenetics and textual criticism: the Bible, and covered the background to the textual problem — that post should be read first. Here, I provide a preliminary genealogical analysis of some specific data related to the problem.


The synoptic gospels and phylogenetics: how to code data?

Just like in the cases of general stemmatics and historical linguistics, our immediate problem for a phylogenetic approach to Biblical criticism is one of data. Upon investigation, the field proves itself desperately in need of an open access mentality — a great deal of work would be needed to turn the few aggregated data I could find into datasets that could feed the most basic analysis tools.

No open dataset proved either adequate or correct enough. They are mostly quotations or subjective developments of the scientific sources, available only in printed editions and in software for Biblical studies, sometimes at exorbitant prices, and frequently with licenses that explicitly prohibit extracting and reusing the data. This forced me to postpone an analysis of families of manuscripts, as unfortunately there is no complete free edition of the Novum Testamentum Graece (the reference work in the field, usually referred to as Nestle-Aland after its main editors).

However, I could explore the problem of the synoptic gospels in a way and with a dataset closer to the ones of the 19th century analyses, by sitting with a printed Bible and compiling my own synopsis of episodes. My work in this field ends with this second post, but it seems like a good approach to the development of a phylogenetic investigation, to start by reproducing the old analyses with new tools.

After some bibliographic review and inspection of the solutions presented to the problem, my understanding is that there would be three fundamental ways of coding for features of these texts.

(i)
The first and simplest is to compile a list of episodes, themes, and topics found in each gospel (a proper “synopsis”), without considering semantic differences or relative positions, coding for a truth table indicating whether each “event” (i.e. “character”) is found. For example, the imprisonment of John the Baptist is mentioned in the three synoptic gospels (Matthew 4,12; Mark 1,14; Luke 3,18-20) and would be coded as “present” in all of them, even though in Luke the relative order is different (it is narrated before the baptism of Jesus, in a flashforward). On the other hand, the priests conspiring against Jesus is only narrated in two gospels (Mark 11,18; Luke 19,47-48), and the “character” of the meek inheriting the Earth is only found in one of them (Matthew 5,5), as shown in the table below.

Character/Event
MatthewMarkLuke
Imprisonment of John
Priests conspiring

Meek inheritance



This kind of census approach is what most descriptive statistics on the synoptic relationship consider when demonstrating how much there is in common among the gospels, including the graph reproduced back in the first part of this post. As in the case of the statistics of genetic material shared between species, like humans and other apes, caution is needed to understand what is actually meant — the percentages usually reported refer to episode coincidence (in a loose analogy, like the presence of a protein), not text coincidence (like the sequences of genetic bases). This is the reason why these analyses should equally consider “episode homology” and “episode analogy” — one must remember that all gospels as we have them evolved from initial versions, and to be missing an episode favored by the public or the clergy, which denounced other gospels now lost as “uninspired”, could have been an evolutionary pressure to incorporate such episode.

(ii)
A deeper level of coding would be to map the text of episodes and events into “semantic” characters, ignoring textual differences (like synonyms) but coding for differences in intended meaning. For example, the event of Jesus being tested in the wilderness, while narrated in all three gospels (Matthew 4,1-2; Mark 1,12-13; Luke 4,1-2), is really only equivalent in Matthew and Luke, where he is tempted by "the Devil", while in Mark he is tempted by "Satan", which is a figure closer to the Hebrew meaning of "enemy, adversary; accuser". Likewise, while Matthew and Luke both narrate Jesus’ most famous sermon, they are semantically different: the setting is a mountain in the first and a plain in the second.

Character/Event
MatthewMarkLuke
Temptation
by the Devilby Satanby the Devil
Sermon
mountain
plain

This kind of mapping is harder, due to the expertise required to subjectively distinguish meaning, as in the case of the mountain / plain, which scholars in Biblical hermeneutics seem to agree to be more than merely a change of setting for narration. The difficulty is aggravated by the eventual need to quantify the semantic shifts (how far is "the Devil" from "Satan (the adversary)", especially when the episode is missing from the non-synoptic gospel of John?). These three states ("null", "Devil", and "Satan") should not be considered equally different, especially when the texts of the three synoptic gospels are clearly related. Luckily, while not necessarily in a systematic way for phylogenetic purposes, this kind of coding has already been conducted by many Biblical scholars, and we might thus appropriate it in the future.

(iii)
The third way of coding, partly solving the difficulties of the second solution, listed above, would be to compare the Greek text for each event, using some distance metric. For strings, there is the common Levenshtein distance, or, in a blatant self-promotion, my own sequence similarity algorithm. For linguistic texts, there are dozens of possible Natural Language Processing solutions, but usually with no model for Koine Greek (apart from purely statistical ones that can overfit, because in general they are actually trained on the text of the gospels, in the first place).

Character/EventMatthewMarkLuke
Prologue
Βίβλος γενέσεως Ἰησοῦ... (1,1)Ἀρχὴ τοῦ εὐαγγελίου Ἰησοῦ... (1,1)Ἐπειδήπερ πολλοὶ ἐπεχείρησαν... (1,1-4)
Birth of Jesus
Τοῦ δὲ Ἰησοῦ χριστοῦ ἡ γένεσις... (1,18-25)
Ἐγένετο δὲ ἐν ταῖς ἡμέραις ἐκείναις... (2,1-7)
Healing of possessed

καὶ εὐθὺς ἦν ἐν τῇ συναγωγῇ αὐτῶν ἄνθρωπος ἐν πνεύματι... (1,23-28)καὶ ἐν τῇ συναγωγῇ ἦν ἄνθρωπος ἔχων... (4,33-37)
Parable of tares
Ἄλλην παραβολὴν παρέθηκεν αὐτοῖς λέγων... (13,24-30)



By comparing all distance pairs for all characters, we could build a matrix of pairwise distances, similarly to what David frequently does in the EDA analyses posted to this blog. Considering that most synoptic lists have already mapped each event to their texts (sometimes in discontinuous blocks), with a copy of the reconstructed Greek original, from Holmes (2010) in the table immediately above, it should not be too hard to perform such a study.

A simple Splits Graph analysis

For the purpose of this post, I decided to proceed with the first of these three possible solutions, listing whether an event is found in each Gospel or not, ignoring semantic and textual differences. I modified the synopsis by Garmus (1982), itself apparently modified from some Nestle-Aland edition. This produced a final list of 364 characters and their presence in each of the four gospels — I decided to include the non-synoptic John to test where the analyses would place it.

As expected, the data are to a large extent arbitrary and subjective. Garmus has obvious limitations in the way of dealing with events narrated out of the expected chronological sequence (i.e., flashbacks and flashforwards, as in the case of the beheading of John in relation to his actions), as well as with theological excursuses. None of these limits, however, seem to impact the general shape of a network or tree generated from these data, at most strengthening more feeble signals.

Splits tree, modified from the one generated by Huson & Bryant (2010)

As also expected, the graph supports what is by now a general consensus. Mark is likely to be the gospel closest to a hypothetical root (in this case, nearest to the mid-point). John is the most distinct of the four gospels, being closer to Mark than to the Matthew-Luke group (due to the “core” events narrated and the fewer innovations in Mark). Considering edge lengths, Luke seems to be the most innovative taxon of the synoptic gospel neighborhood / group. Such a network could never demonstrate the existence of "Q" (see the first post) as a stand-alone and actual document, but this tentative analysis does support the hypothesis that Matthew and Luke share a common development, overall supporting Marcan priority.

While probably obvious, it is important to remember that phylogenetic methods are tools that imply the existence of users — it should be an additional instrument for investigation, possibly promoting the collaboration of serious Biblical critics and experts in phylogenetic methods. Let’s consider two examples of the need for such expertise.

First, there are much historical, textual, and theological evidence supporting a hypothesis that the gospel of Mark originally ended with what is now Mark 16,8, with the twelve following verses as later additions (something common to many Greek texts, including the Odyssey). If these supposed additions, only known to whoever delves into Biblical scholarship, are marked as missing in our data, as we should at least test, the distance between Mark and all other gospels, including the unrelated Gospel of John and especially in the edge length between Luke and Mark, increases considerably for such an apparently minor change.

Second, if conducting the third and especially the second type of coding that I described above, a researcher should have at least a basic knowledge of the language they are dealing with. Adapting the explanation of Smith (2017), Matthew and Mark might seem to use the same vocabulary for the “parable of the harvest” when read in English translation, but there is a concealed change of meaning (whose theological importance and implication I'm not debating here), as the single English word “seed” tends to be used in translation of two different Greek words: in Matthew, “sperma” (the kernels of grain, in a more agricultural sense) and, in Mark, “sporos” (which carries a connotation of generative matter to be released).

Conclusion

My dataset is available in preliminary state (for example, labels are in Portuguese) here.

In conclusion, phylogenetics still has much to offer to the field of textual criticism, and this should include Biblical criticism, especially if we are able to support analyses of textual development from trees on manuscripts. I hope this pair of will motivate Biblical scholars to collaborate. If so, please write to me.

References

Garmus, Ludovico (ed.) (1982) Bíblia sagrada. Petrópolis: Editora Vozes. [reprint 2001]

Goodacre, Mark (2001) The Synoptic Problem: a Way Through the Maze. New York: T & T Clark International. (available on Archive.org)

Holmes, Michael W. (ed.) (2010) SBL Greek New Testament. Atlanta, GA: Society of Biblical Literature.

Huson, Daniel H.; Bryant David (2006) Application of Phylogenetic Networks in Evolutionary Studies, Mol. Biol. Evol., 23(2):254-267. [SplitsTree.org]

Smith, Mahlon H (2017) A Synoptic Gospels Primer. http://virtualreligion.net/primer/