Gene-CluEDO (empty) → 0.0.0.1
raw patch · 9 files changed
+1201/−0 lines, 9 filesdep +ADPfusiondep +ADPfusionSetdep +FormalGrammarssetup-changed
Dependencies added: ADPfusion, ADPfusionSet, FormalGrammars, Gene-CluEDO, PrimitiveArray, PrimitiveArray-Pretty, QuickCheck, ShortestPathProblems, base, cmdargs, containers, filepath, log-domain, tasty, tasty-quickcheck, tasty-th, text, vector
Files
- BioInf/GeneCluEDO.hs +118/−0
- BioInf/GeneCluEDO/EdgeProb.hs +143/−0
- Gene-CluEDO.cabal +121/−0
- LICENSE +675/−0
- README.md +82/−0
- Setup.hs +2/−0
- changelog.md +8/−0
- src/GeneCluEDO.hs +41/−0
- tests/properties.hs +11/−0
+ BioInf/GeneCluEDO.hs view
@@ -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"+
+ BioInf/GeneCluEDO/EdgeProb.hs view
@@ -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)+-}+
+ Gene-CluEDO.cabal view
@@ -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+
+ LICENSE view
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+ README.md view
@@ -0,0 +1,82 @@+[](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/ +
+ Setup.hs view
@@ -0,0 +1,2 @@+import Distribution.Simple+main = defaultMain
+ changelog.md view
@@ -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
+ src/GeneCluEDO.hs view
@@ -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+
+ tests/properties.hs view
@@ -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 ()+