diff --git a/BioInf/GeneCluEDO.hs b/BioInf/GeneCluEDO.hs new file mode 100644 --- /dev/null +++ b/BioInf/GeneCluEDO.hs @@ -0,0 +1,118 @@ + +-- | Run all steps of the GeneCluEDO algorithms in order. +-- +-- This will produce the following: +-- +-- 1. run the minimal distance algorithm, give the minimal distance score +-- and return all co-optimal paths +-- +-- 2. run the end-probability algorithm and return the probability that +-- each node is the begin/end of a chain +-- +-- 3. run the edge probability algorithm and give the probability for each +-- @from :-> to@ edge +-- +-- 4. with the edge probabilities, run the maximal probability path +-- algorithm, return that probability and all co-optimal paths +-- +-- TODO -Pretty should yield a structure to be given to the eps or svg +-- generator. This allows more flexibility. Does diagrams offer +-- serialization? +-- +-- TODO All this should be wrapped and available as a function. not just +-- providing output files. + +module BioInf.GeneCluEDO + ( runGeneCluEDO + , FillWeight (..) + , FillStyle (..) + ) where + +import Control.Monad (forM_) +import Data.Function (on) +import Data.List (groupBy) +import Numeric.Log +import qualified Data.Text as T +import qualified Data.Text.IO as T +import System.FilePath (addExtension) +import System.IO (withFile,IOMode(WriteMode)) +import Text.Printf + +import ADP.Fusion.Term.Edge.Type (From(..),To(..)) +import Data.PrimitiveArray (fromEdgeBoundaryFst,(:.)(..)) +import Data.PrimitiveArray.ScoreMatrix +import Diagrams.TwoD.ProbabilityGrid +import ShortestPath.SHP.Edge.MinDist (runMaxEdgeProbLast, runCoOptDist, boundaryPartFun,PathBT(..)) + +import BioInf.GeneCluEDO.EdgeProb (edgeProbScoreMatrix, edgeProbPartFun) + + + +runGeneCluEDO + :: FillWeight + -> FillStyle + -> Double + -- ^ "Temperature" for probability-related parts of the algorithms. + -- Lower temperatures favor a single path. + -> FilePath + -- ^ The input score matrix + -> String + -- ^ In the current directory, create output files with this name prefix + -> IO () +runGeneCluEDO fw fs temperature inFile filePrefix = do + scoreMat <- fromFile inFile + let lon = listOfRowNames scoreMat + let n = length lon + let lns = map T.unpack lon + let bcols = max 4 . maximum $ map T.length $ lon + withFile (filePrefix `addExtension` ".run") WriteMode $ \hrun -> do + hPrintf hrun ("Input File: %s\n") inFile + hPrintf hrun ("Temperature: %f\n") temperature + hPrintf hrun ("\n") + let (minD, minDcoopts) = runCoOptDist scoreMat + -- + -- Print the minimal distance and the co-optimal paths + -- + hPrintf hrun "Minimal Distance: %6.3f\n" minD + hPrintf hrun "Optimal Paths:\n" + forM_ minDcoopts (T.hPutStrLn hrun) + hPrintf hrun "\n" + -- + -- end probabilities, both to the output file and create pretty file + -- + hPrintf hrun "Chain Begin/End Probabilities:\n" + let bps = boundaryPartFun temperature scoreMat + forM_ lon $ hPrintf hrun ("%" ++ show (bcols + 4) ++ "s") + hPrintf hrun "\n" + forM_ bps $ \(_, Exp p) -> hPrintf hrun ("%" ++ show (bcols + 4) ++ ".4f") (exp p) + hPrintf hrun "\n" + hPrintf hrun "\n" + svgGridFile (filePrefix `addExtension` "boundary.svg") fw fs 1 n [] lns (Prelude.map snd bps) + -- + -- edge probabilities, output file and pretty file + -- + hPrintf hrun "Edge Probabilities:\n" + let eps = edgeProbPartFun temperature scoreMat + hPrintf hrun ("%" ++ show (bcols + 4) ++ "s") ("" :: String) + forM_ lon $ hPrintf hrun ("%" ++ show (bcols + 4) ++ "s") + hPrintf hrun "\n" + forM_ (groupBy ((==) `on` (fromEdgeBoundaryFst . fst)) eps) $ \rps -> do + let (eb,_) = head rps + hPrintf hrun ("%" ++ show (bcols + 4) ++ "s") (lon !! fromEdgeBoundaryFst eb) + forM_ rps $ \(eb,Exp p) -> hPrintf hrun ("%" ++ show (bcols + 4) ++ ".4f") (exp p) + hPrintf hrun "\n" + svgGridFile (filePrefix `addExtension` "edge.svg") fw fs n n lns lns (Prelude.map snd eps) + -- + -- maximum probability path + -- + hPrintf hrun "\n" + let probMat = edgeProbScoreMatrix scoreMat eps + let (Exp maxP, _, maxPcoopts) = runMaxEdgeProbLast probMat + hPrintf hrun "Maximal Log-Probability Path Score: %6.3f\n" maxP + forM_ (map reverse maxPcoopts) $ \path -> do + forM_ path $ \case + BTnode (_:.To n) -> hPrintf hrun "%s" (lns !! n) + BTedge (From ff:.To tt) -> hPrintf hrun " -> %s" (lns !! tt) + hPrintf hrun "\n" + hPrintf hrun "\n" + diff --git a/BioInf/GeneCluEDO/EdgeProb.hs b/BioInf/GeneCluEDO/EdgeProb.hs new file mode 100644 --- /dev/null +++ b/BioInf/GeneCluEDO/EdgeProb.hs @@ -0,0 +1,143 @@ + +module BioInf.GeneCluEDO.EdgeProb where + +import Control.Arrow (second) +import Control.Monad (forM_) +import Data.List (nub,sort) +import Data.Text (Text,unpack) +import Data.Vector.Unboxed (Unbox) +import Numeric.Log +import qualified Data.Map.Strict as MS +import qualified Data.Text as T +import qualified Data.Vector.Fusion.Stream.Monadic as SM +import Text.Printf + +import ADP.Fusion.Core +import ADP.Fusion.EdgeBoundary +import ADP.Fusion.Set1 +import Data.PrimitiveArray hiding (toList) +import Data.PrimitiveArray.ScoreMatrix +import Diagrams.TwoD.ProbabilityGrid +import FormalLanguage +import ShortestPath.SHP.Grammar.EdgeProb + + + +-- | Minimal distance algebra +-- +-- TODO The two Ints are the indices of the nodes and could be replaced? + +aMinDist :: Monad m => ScoreMatrix Double -> SigEdgeProb m Double Double (From:.To) (Int:.To) +aMinDist s = SigEdgeProb + { edge = \x (From ff:.To tt) -> x + (s .!. (ff,tt)) + , mpty = \() -> 0 + , node = \n -> 0 + , fini = \l (From ff:.To tt) f -> l + (s .!. (ff,tt)) + f + , h = SM.foldl' min 999999 + } +{-# Inline aMinDist #-} + +{- +-- | This should give the correct order of nodes independent of the +-- underlying @Set1 First@ or @Set1 Last@ because the @(From:.To)@ system +-- is agnostic over these. +-- +-- TODO Use text builder + +aPretty :: Monad m => ScoreMatrix t -> SigEdgeProb m Text [Text] (From:.To) (Int:.To) +aPretty s = SigEdgeProb + { edge = \x (From f:.To t) -> T.concat [s `nameOf` f, " -> ", x] + , mpty = \() -> "" + , node = \n -> s `nameOf` n -- ok because it is the first node in the path + , fini = \l (From i:.To j) f -> T.concat [l, "~>~", f] + , h = SM.toList + } +{-# Inline aPretty #-} +-} + +-- | Before using @aInside@ the @ScoreMatrix@ needs to be scaled +-- appropriately! Due to performance reasons we don't want to do this +-- within @aInside@. + +aInside :: Monad m => ScoreMatrix (Log Double) -> SigEdgeProb m (Log Double) (Log Double) (From:.To) (Int:.To) +aInside s = SigEdgeProb + { edge = \x (From ff:.To tt) -> s .!. (ff,tt) * x + , mpty = \() -> 1 + , node = \n -> 1 + , fini = \l (From ff:.To tt) f -> l * (s .!. (ff,tt)) * f + , h = SM.foldl' (+) 0 + } +{-# Inline aInside #-} + + + +type TF1 x = TwITbl Id Unboxed EmptyOk (BS1 First I) x +type TL1 x = TwITbl Id Unboxed EmptyOk (BS1 Last I) x +type EB x = TwITbl Id Unboxed EmptyOk (EdgeBoundary I) x + +type BF1 x b = TwITblBt Unboxed EmptyOk (BS1 First I) x Id Id b +type BL1 x b = TwITblBt Unboxed EmptyOk (BS1 Last I) x Id Id b +type BEB x b = TwITblBt Unboxed EmptyOk (EdgeBoundary I) x Id Id b + + + +-- | Extract the individual partition scores. + +edgeProbPartFun :: Double -> ScoreMatrix Double -> [(EdgeBoundary I, Log Double)] +edgeProbPartFun temperature scoreMat = + let n = numRows scoreMat + partMat = toPartMatrix temperature scoreMat + (Z:.sF:.sL:.sZ) = mutateTablesST $ gEdgeProb (aInside partMat) + (ITbl 0 0 EmptyOk (fromAssocs (BS1 0 (-1)) (BS1 (2^n-1) (Boundary $ n-1)) 0 [])) + (ITbl 1 0 EmptyOk (fromAssocs (BS1 0 (-1)) (BS1 (2^n-1) (Boundary $ n-1)) 0 [])) + (ITbl 2 0 EmptyOk (fromAssocs (0 :-> 0) (0 :-> (n-1)) 0 [])) + Edge + Singleton + :: Z:.TF1 (Log Double):.TL1 (Log Double):.EB (Log Double) + TW (ITbl _ _ _ pf) _ = sZ + bs' = assocs pf + pssum = (Numeric.Log.sum $ Prelude.map snd bs') / (fromIntegral n - 1) + bs = Prelude.map (second (/pssum)) bs' + in bs +{-# NoInline edgeProbPartFun #-} + +-- | Turn the edge probabilities into a score matrix. + +edgeProbScoreMatrix :: (Unbox t) => ScoreMatrix t -> [(EdgeBoundary I, Log Double)] -> ScoreMatrix (Log Double) +edgeProbScoreMatrix (ScoreMatrix mat _ zn sn) xs' = ScoreMatrix m endProbs zn sn + where m = fromAssocs l h 0 xs + (Z:._:.n) = h + (l,h) = bounds mat + xs = [ ((Z:.f:.t),p) | (f :-> t, p) <- xs' ] + -- calculate end state probability + eP = MS.fromListWith (+) [ (f,p) | (f :-> t, p) <- xs' ] + endProbs = fromAssocs 0 n 1 [ (k, 1 - eP MS.! k) | k <- [0..n] ] + -- calculate begin state probability + bP = MS.fromListWith (+) [ (t,p) | (f :-> t, p) <- xs' ] + beginProbs = fromAssocs 0 n 1 [ (k, 1 - eP MS.! k) | k <- [0..n] ] `asTypeOf` endProbs + +{- +test t fp = do + sMat <- fromFile fp + let n = numRows sMat + let lns = fmap unpack $ listOfRowNames sMat + let ps = edgeProbPartFun t sMat + forM_ ps $ \(b :-> _,_) -> printf "%5s " (sMat `rowNameOf` b) + putStrLn "" + forM_ ps $ \(_ :-> b,_) -> printf "%5s " (sMat `rowNameOf` b) + putStrLn "" + forM_ ps $ \(_,Exp p) -> printf "%0.3f " (exp p) + putStrLn "" + let Exp z = Numeric.Log.sum $ Prelude.map snd ps + printf "sum: %0.3f\n" $ exp z + forM_ ps $ \(_,Exp p) -> printf "%0.3f " (1 / (1 - p)) + putStrLn "" + putStrLn "" + putStrLn "" + print n + print lns + print $ length ps + print ps + svgGridFile "test.svg" FWfill FSopacityLinear n n lns lns (Prelude.map snd ps) +-} + diff --git a/Gene-CluEDO.cabal b/Gene-CluEDO.cabal new file mode 100644 --- /dev/null +++ b/Gene-CluEDO.cabal @@ -0,0 +1,121 @@ +name: Gene-CluEDO +version: 0.0.0.1 +author: Christian Hoener zu Siederdissen, 2017 +copyright: Christian Hoener zu Siederdissen, 2017 +homepage: https://github.com/choener/Gene-CluEDO +bug-reports: https://github.com/choener/Gene-CluEDO/issues +maintainer: choener@bioinf.uni-leipzig.de +category: Bioinformatics +license: GPL-3 +license-file: LICENSE +build-type: Simple +stability: experimental +cabal-version: >= 1.10.0 +tested-with: GHC == 7.10.3, GHC == 8.0.1 +synopsis: Hox gene clustering +description: + Gene Cluster Evolution Determined Order + . + Calculate the most likely order of genes in a gene cluster. + . + Apart from being an interesting problem in computational + biology, it also serves as an example problem for dynamic + programming over unordered sets with interfaces. + + + +Extra-Source-Files: + README.md + changelog.md + + + +library + build-depends: base >= 4.7 && < 5.0 + , containers + , filepath + , log-domain >= 0.10 + , text >= 1.0 + , vector >= 0.11 + -- + , ADPfusion == 0.5.2.* + , ADPfusionSet == 0.0.0.* + , FormalGrammars == 0.3.1.* + , PrimitiveArray == 0.8.0.* + , PrimitiveArray-Pretty == 0.0.0.* + , ShortestPathProblems == 0.0.0.* + exposed-modules: + BioInf.GeneCluEDO + BioInf.GeneCluEDO.EdgeProb + default-extensions: BangPatterns + , FlexibleContexts + , GADTs + , LambdaCase + , MultiParamTypeClasses + , OverloadedStrings + , QuasiQuotes + , TemplateHaskell + , TypeFamilies + , TypeOperators + default-language: + Haskell2010 + ghc-options: + -O2 -funbox-strict-fields + + + +executable GeneCluEDO + build-depends: base + , cmdargs >= 0.10 + , filepath + -- + , Gene-CluEDO + hs-source-dirs: + src + default-extensions: BangPatterns + , DeriveDataTypeable + , RecordWildCards + main-is: + GeneCluEDO.hs + default-language: + Haskell2010 + ghc-options: + -O2 + + + +test-suite properties + type: + exitcode-stdio-1.0 + main-is: + properties.hs + ghc-options: + -threaded -rtsopts -with-rtsopts=-N + hs-source-dirs: + tests + default-language: + Haskell2010 + default-extensions: BangPatterns + , CPP + , FlexibleContexts + , FlexibleInstances + , MultiParamTypeClasses + , ScopedTypeVariables + , TemplateHaskell + , TypeFamilies + , TypeOperators + , TypeSynonymInstances + build-depends: base + , QuickCheck + , tasty >= 0.11 + , tasty-quickcheck >= 0.8 + , tasty-th >= 0.1 + , vector + -- + , Gene-CluEDO + + +source-repository head + type: git + location: git://github.com/choener/Gene-CluEDO + diff --git a/LICENSE b/LICENSE new file mode 100644 --- /dev/null +++ b/LICENSE @@ -0,0 +1,675 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. + diff --git a/README.md b/README.md new file mode 100644 --- /dev/null +++ b/README.md @@ -0,0 +1,82 @@ +[![Build Status](https://travis-ci.org/choener/Gene-CluEDO.svg?branch=master)](https://travis-ci.org/choener/Gene-CluEDO) + +[*generalized Algebraic Dynamic Programming Homepage*](http://www.bioinf.uni-leipzig.de/Software/gADP/) + +# Gene-CluEDO: Gene Cluster Evolution Determined Order + +1. Hoener zu Siederdissen, Christian and Prohaska, Sonja J. and Stadler, Peter F. + *Dynamic Programming for Set Data Types* + 2014, Lecture Notes in Bioinformatics, 8826, + preprint: http://www.bioinf.uni-leipzig.de/~choener/pdfs/hoe-pro-2014.pdf + +1. Hoener zu Siederdissen, Christian and Prohaska, Sonja J. and Stadler, Peter F. + *Algebraic Dynamic Programming over General Data Structures* + 2015, BMC Bioinformatics + oa: https://doi.org/10.1186/1471-2105-16-S19-S2 + +1. Prohaska, Sonja J. and Berkemer, Sarah and Externbrink, Fabian and Gatter, Thomas + and Retzlaff, Nancy and The Students of the Graphs and Biological Networks Lab 2017 + and H\"oner zu Siederdissen, Christian and Stadler, Peter F. + *Expansion of Gene Clusters and the Shortest Hamiltonian Path Problem* + 2017 + preprint: http://www.bioinf.uni-leipzig.de/~choener/pdfs/pro-ber-2017.pdf + +This program accepts a matrix with distances between nodes (see below for an +example). It then proceeds to calculate the Hamiltonian path with the shortest +distance between each pair of nodes, where the path has to travel from the +start, then to all other nodes, finally stopping at the last node. + +We further calculate all neighbour probabilities via Inside/Outside. This means +that for any two nodes we calculate the weight of the edge between these two +nodes. The weight is between ``[0, ... ,1]`` where ``0`` denotes the the nodes +are almost surely not direct neighbours on a weighted-randomly drawn path, +while ``1`` denotes that they almost surely are. + +Finally, we calculate the probability that a node is one of the terminal nodes +in the Hamiltonian path, i.e. either the first or the last node. + +## The Biological Problem We Solve + +[Wikipedia on Hox clusters.](https://en.wikipedia.org/wiki/Hox_cluster) + +Hox clusters are a set of genes that are linearly ordered. The genes are +(assumed) to have a single originating gene, and repeated duplication has led +to the cluster with unknown duplication tree. + +The long time scales involved make it hard to produce a tree that can be +trusted. This program therefore produces summary information in the form of +edge path probabilities. + +## Example matrix: + +In this artificial distance matrix, we have prime numbers as distances between +nodes. Store the matrix in a file, say ``mat.dat``. + +``` +# A B C D E +A 0 2 3 5 7 +B 2 0 11 13 17 +C 3 11 0 19 23 +D 5 13 19 0 27 +E 7 17 23 27 0 +``` + +Now, run the algorithm ``./GeneCluEDO -o output.run ./mat.dat``. After the +program has run, ``output.run`` contains the a wealth of information about the +input. The maximum likelihood path, the edge weights, end probabilities, and +maximum expected accuracy path are calculated. Two additional files, here +``output.boundary.svg``, and ``output.edge.svg`` are produced. The boundary +plot provides graphical output of the probability that a node (or gene) is the +start or end node. The edge probability plot provides probabilities for each +edge (i,j) between nodes. This shows the most likely neighbors, and therefore +genetic relationship, over all possible gene orders. + + + +#### Contact + +Christian Hoener zu Siederdissen +Leipzig University, Leipzig, Germany +choener@bioinf.uni-leipzig.de +http://www.bioinf.uni-leipzig.de/~choener/ + diff --git a/Setup.hs b/Setup.hs new file mode 100644 --- /dev/null +++ b/Setup.hs @@ -0,0 +1,2 @@ +import Distribution.Simple +main = defaultMain diff --git a/changelog.md b/changelog.md new file mode 100644 --- /dev/null +++ b/changelog.md @@ -0,0 +1,8 @@ +0.0.0.1 +------- + +- originally named 'HoxCluster' for the earlier papers, now renamed to + Gene-CluEDO during the work on the expansion of gene clusters problem (see + README) +- initial checkin +- travis-ci integration diff --git a/src/GeneCluEDO.hs b/src/GeneCluEDO.hs new file mode 100644 --- /dev/null +++ b/src/GeneCluEDO.hs @@ -0,0 +1,41 @@ + +module Main where + +import Data.Version (showVersion) +import System.Console.CmdArgs +import System.FilePath + +import BioInf.GeneCluEDO + +import Paths_Gene_CluEDO (version) + + + +data Options = Options + { infile :: FilePath + , outprefix :: Maybe String + , temperature :: Double + , fillweight :: FillWeight + , fillstyle :: FillStyle + } + deriving (Show,Data,Typeable) + +oOptions = Options + { infile = def &= args + , outprefix = Nothing &= help "prefix for the output files, calculated from input if not given" + , temperature = 0.01 &= help "lower temperatures favor the more optimal paths, defaults to 0.01" + , fillweight = FWlog &= help "size of boxes: fwlog (log-scaled), fwlinear (linear scaled), fwfull (always full)" + , fillstyle = FSfull &= help "shading of boxes: fsopacitylog (log-scaled), fsopacitylinear (linear scaled), fsfull (always fully opaque)" + } + +allOptions = oOptions + &= program "GeneCluEDO" + &= summary ("GeneCluEDO " ++ showVersion version ++ " (c) Christian Höner zu Siederdissen 2017, choener@bioinf.uni-leipzig.de") + &= verbosity + +main :: IO () +main = do + Options{..} <- cmdArgs allOptions + let filePrefix = maybe (takeBaseName infile) id outprefix + runGeneCluEDO fillweight fillstyle temperature infile filePrefix + diff --git a/tests/properties.hs b/tests/properties.hs new file mode 100644 --- /dev/null +++ b/tests/properties.hs @@ -0,0 +1,11 @@ + +module Main where + +-- | Each optimal path @a -> b -> c@ has has a co-optimal path @c -> b -> +-- a@ whenever the distance matrix is symmetric. + +-- prop_symmetric_coopts :: () + +main :: IO () +main = return () +