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The outputs of the code produce results in units of branch\nlength, needing to be multiplied by mu^2 or divided by product of the\ntotal branch length of the two trees.\n\nThis algorithm works by keeping a running sum of the statistic between\ntwo trees, updating each time we encounter a branch addition or removal.\nThe tricky part is that we have to remove or add LD contributed by\nsamples that already existed or that will remain under a given node\nafter the addition/removal of branches.\n\nWe include a validation against the original formulation of this\nproblem, by including an implementation that was described in McVean\n2002. The original formulation computing the covariance of tMRCAs of\n2, 3, and 4 samples of individuals on the trees in question. 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The outputs of the code produce results in units of branch\nlength, needing to be multiplied by mu^2 or divided by product of the\ntotal branch length of the two trees.\n\nThis algorithm works by keeping a running sum of the statistic between\ntwo trees, updating each time we encounter a branch addition or removal.\nThe tricky part is that we have to remove or add LD contributed by\nsamples that already existed or that will remain under a given node\nafter the addition/removal of branches.\n\nWe include a validation against the original formulation of this\nproblem, by including an implementation that was described in McVean\n2002. The original formulation computing the covariance of tMRCAs of\n2, 3, and 4 samples of individuals on the trees in question. 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The outputs of the code produce results in units of branch\nlength, needing to be multiplied by mu^2 or divided by product of the\ntotal branch length of the two trees.\n\nThis algorithm works by keeping a running sum of the statistic between\ntwo trees, updating each time we encounter a branch addition or removal.\nThe tricky part is that we have to remove or add LD contributed by\nsamples that already existed or that will remain under a given node\nafter the addition/removal of branches.\n\nWe include a validation against the original formulation of this\nproblem, by including an implementation that was described in McVean\n2002. The original formulation computing the covariance of tMRCAs of\n2, 3, and 4 samples of individuals on the trees in question. 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This\nimplementation has several limitations 1) it is very slow and 2) it does\nnot work for trees that are decapitated, as certain samples do not have\nMRCAs.","shortMessageHtmlLink":"Adds a py prototype for two-locus branch stats"}},{"before":"1761e0569dd93a9830c720e6f73d720923da04b3","after":"850600c2e8e05e5b3cbf5b58f58d721ff9fbad3d","ref":"refs/heads/two-locus-branch-stats","pushedAt":"2024-04-23T16:11:01.000Z","pushType":"force_push","commitsCount":0,"pusher":{"login":"lkirk","name":"lkirk","path":"/lkirk","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/8475684?s=80&v=4"},"commit":{"message":"Adds a py prototype for two-locus branch stats\n\nCurrently, this algorithm creates a matrix of LD, performing a pairwise\ncomparison of all trees in the tree sequence.\n\nThis implementation lacks windows/positions, sample sets and\npolarisation. The outputs of the code produce results in units of branch\nlength, needing to be multiplied by mu^2 or divided by product of the\ntotal branch length of the two trees.\n\nThis algorithm works by keeping a running sum of the statistic between\ntwo trees, updating each time we encounter a branch addition or removal.\nThe tricky part is that we have to remove or add LD contributed by\nsamples that already existed or that will remain under a given node\nafter the addition/removal of branches.\n\nWe include a validation against the original formulation of this\nproblem, by including an implementation that was described in McVean\n2002. The original formulation computing the covariance of tMRCAs of\n2, 3, and 4 samples of individuals on the trees in question. This\nimplementation has several limitations 1) it is very slow and 2) it does\nnot work for trees that are decapitated, because certain samples do not\nhave MRCAs.","shortMessageHtmlLink":"Adds a py prototype for two-locus branch stats"}}],"hasNextPage":true,"hasPreviousPage":false,"activityType":"all","actor":null,"timePeriod":"all","sort":"DESC","perPage":30,"cursor":"djE6ks8AAAAEYOWLQAA","startCursor":null,"endCursor":null}},"title":"Activity · lkirk/tskit"}