The clade_weights tool is designed to analyze a tree_table, which can be generated using the treeslider ipyrad-analysis tool. This tool can quantify and generate plots of clade supports in sliding windows along the genome. This is similar to the TWISST software, for showing how different gene tree patterns vary along the genome.
Key features:
1.
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# conda install ipyrad -c bioconda
# conda install toyplot -c eaton-lab
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import ipyrad.analysis as ipa
import toyplot
from ipyrad.analysis.clade_weights import *
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# the path to your CSV
data = "./analysis-treeslider/test.tree_table.csv"
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# check scaffold idx (row) against scaffold names
self = ipa.clade_weights(
data=data,
name="test",
workdir="analysis-clade_weights",
imap={
"SE": ["virg", "mini", "gemi"],
"CA": ["sagr", "oleo"],
"WE": ["bran", "fusi-N", "fusi-S"],
#"REF": ["reference"],
},
minsupport=50,
)
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self.tree_table.head()
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self.run(auto=True, force=True)
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toyplot.plot(
self.clade_weights.rolling(5, win_type="boxcar", center=True).mean(),
height=300,
opacity=0.7,
);
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# make empty clade weights table
self.clade_weights = pd.DataFrame(
{i: [0.] * self.tree_table.shape[0] for i in self.imap.keys()}
)
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treelist = self.tree_table.tree[:10].tolist()
clades = self.imap
idx = 0
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# iterate over trees
for tidx, tree in enumerate(treelist):
# get tips for this subtree
tree = toytree.tree(tree)
tips = set(tree.get_tip_labels())
# iterate over clades to test
for name, clade in clades.items():
idx = 0
tsum = 0
iclade = tips.intersection(set(clade))
isamp = itertools.combinations(iclade, 2)
oclade = tips.difference(iclade)
osamp = itertools.combinations(oclade, 2)
# iterate over quartets
for ipair in isamp:
for opair in osamp:
quartet = set(list(ipair) + list(opair))
todrop = set(tree.get_tip_labels()) - quartet
dt = tree.drop_tips(todrop)
tsum += clade_true(dt.unroot(), iclade)
idx += 1
print(tips)
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clade_weights(treelist, clades, idx)
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weights.tree_table.head()
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weights.run(auto=True)
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weights.clade_weights
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Here I select the scaffold Qrob_Chr03 (scaffold_idx=2), and run 2Mb windows (window_size) non-overlapping (2Mb slide_size) across the entire scaffold. I use the default inference method "raxml", and modify its default arguments to run 100 bootstrap replicates. More details on modifying raxml params later. I set for it to skip windows with <10 SNPs (minsnps), and to filter sites within windows (mincov) to only include those that have coverage across all 9 clades, with samples grouped into clades using an imap dictionary.
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# select a scaffold idx, start, and end positions
ts = ipa.treeslider(
name="test",
data="/home/deren/Downloads/ref_pop2.seqs.hdf5",
workdir="analysis-treeslider",
scaffold_idxs=2,
window_size=2000000,
slide_size=2000000,
inference_method="raxml",
inference_args={"N": 100, "T": 4},
minsnps=10,
mincov=9,
imap={
"reference": ["reference"],
"virg": ["TXWV2", "LALC2", "SCCU3", "FLSF33", "FLBA140"],
"mini": ["FLSF47", "FLMO62", "FLSA185", "FLCK216"],
"gemi": ["FLCK18", "FLSF54", "FLWO6", "FLAB109"],
"bran": ["BJSL25", "BJSB3", "BJVL19"],
"fusi-N": ["TXGR3", "TXMD3"],
"fusi-S": ["MXED8", "MXGT4"],
"sagr": ["CUVN10", "CUCA4", "CUSV6", "CUMM5"],
"oleo": ["CRL0030", "HNDA09", "BZBB1", "MXSA3017", "CRL0001"],
},
)
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# this is the tree inference command that will be used
ts.show_inference_command()
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ts.run(auto=True, force=True)
Our goal is to fill the .tree_table, a pandas DataFrame where rows are genomic windows and the information content of each window is recorded, and a newick string tree is inferred and filled in for each. The tree table is also saved as a CSV formatted file in the workdir. You can re-load it later using Pandas. Below I demonstrate how to plot results from the tree_able. To examine how phylogenetic relationships vary across the genome see also the clade_weights() tool, which takes the tree_table as input.
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# the tree table is automatically saved to disk as a CSV during .run()
ts.tree_table.head()
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# filter to only windows with >50 SNPS
trees = ts.tree_table[ts.tree_table.snps > 50].tree.tolist()
# load all trees into a multitree object
mtre = toytree.mtree(trees)
# root trees and collapse nodes with <50 bootstrap support
mtre.treelist = [
i.root("reference").collapse_nodes(min_support=50)
for i in mtre.treelist
]
# draw the first 12 trees in a grid
mtre.draw_tree_grid(
nrows=3, ncols=4, start=0,
tip_labels_align=True,
tip_labels_style={"font-size": "9px"},
);
Using toytree you can easily draw a cloud tree of overlapping gene trees to visualize discordance. These typically look much better if you root the trees, order tips by their consensus tree order, and do not use edge lengths. See below for an example, and see the toytree documentation.
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# filter to only windows with >50 SNPS (this could have been done in run)
trees = ts.tree_table[ts.tree_table.snps > 50].tree.tolist()
# load all trees into a multitree object
mtre = toytree.mtree(trees)
# root trees
mtre.treelist = [i.root("reference") for i in mtre.treelist]
# infer a consensus tree to get best tip order
ctre = mtre.get_consensus_tree()
# draw the first 12 trees in a grid
mtre.draw_cloud_tree(
width=400,
height=400,
fixed_order=ctre.get_tip_labels(),
use_edge_lengths=False,
);
In this analysis I entered multiple scaffolds to create windows across each scaffold. I also entered a smaller slide size than window size so that windows are partially overlapping. The raxml command string was modified to perform 10 full searches with no bootstraps.
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# select a scaffold idx, start, and end positions
ts = ipa.treeslider(
name="chr1_w500K_s100K",
data=data,
workdir="analysis-treeslider",
scaffold_idxs=[0, 1, 2],
window_size=500000,
slide_size=100000,
minsnps=10,
inference_method="raxml",
inference_args={"m": "GTRCAT", "N": 10, "f": "d", 'x': None},
)
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# this is the tree inference command that will be used
ts.show_inference_command()