clustertools (empty) → 0.1
raw patch · 10 files changed
+774/−0 lines, 10 filesdep +basedep +biodep +bytestringsetup-changed
Dependencies added: base, bio, bytestring, containers, regex-compat, simpleargs
Files
- LICENSE +340/−0
- README +39/−0
- Setup.hs +3/−0
- clustertools.cabal +65/−0
- src/Ace2Contigs.hs +17/−0
- src/Ace2Fasta.hs +29/−0
- src/AddSingletons.hs +30/−0
- src/Cluscomp.lhs +175/−0
- src/ClusterLibs.hs +66/−0
- src/Filter.hs +10/−0
+ LICENSE view
@@ -0,0 +1,340 @@+ GNU GENERAL PUBLIC LICENSE+ Version 2, June 1991++ Copyright (C) 1989, 1991 Free Software Foundation, Inc.+ 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA+ Everyone is permitted to copy and distribute verbatim copies+ of this license document, but changing it is not allowed.++ Preamble++ The licenses for most software are designed to take away your+freedom to share and change it. By contrast, the GNU General Public+License is intended to guarantee your freedom to share and change free+software--to make sure the software is free for all its users. This+General Public License applies to most of the Free Software+Foundation's software and to any other program whose authors commit to+using it. (Some other Free Software Foundation software is covered by+the GNU Library General Public License instead.) You can apply it to+your programs, too.++ When we speak of free software, we are referring to freedom, not+price. 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+ README view
@@ -0,0 +1,39 @@+This contains the following tools:++To build these, you will need a Haskell compiler (the most likely+candidate begin GHC), and my bioinformatics library and the SimpleArgs+module installed (Downloadable from: <http://malde.org/~ketil/biohaskell/>).++filter - remove unwanted sequences from a clustering+ usage: filter seq.list < cluster.L > cluster2.L+ cluster2.L will only contain sequence labels found in seq.list++hist - produce a histogram of cluster sizes from a "label"-formatted+ clustering.++clusc - compare clusterings, calculating numerous pair-based and+ entropy based indices.++xcerpt - given a file containing a list of sequence labels (e.g. a+ "label" formatted clustering), extract matching sequences+ from a FASTA file. Like "agrep -d '^>'" without the bugs.++ Usage: xcerpt list.txt fasta.seq+ creates "fasta.seq.match" and "fasta.seq.rest"++add_single - add singletons to a clustering.+ Usage: add_single all.L clustering.L+ creates clustering.L_s listing all sequences in all.L but not in+ clustering.L, one per line.++ace2contigs - parse an ACE assembly file, and output the contigs in a+ FASTA file (named by tacking on .fasta to the ACE file name),+ and the corresponding quality information (.qual).++ace2fasta - parse an ACE assembly, and output each assembly in a separate+ fasta formatted file, with the necessary gaps inserted to align the+ sequences (suitable for import into e.g. Seaview)++clusterlibs - given a table of regular expressions and library names,+ along with a clustering (TGICL-format), output a table of clusters+ with the library name prepended to the sequences.
+ Setup.hs view
@@ -0,0 +1,3 @@+#!/usr/bin/env runhaskell+import Distribution.Simple+main = defaultMain
+ clustertools.cabal view
@@ -0,0 +1,65 @@+Name: clustertools+Version: 0.1+License: GPL+License-file: LICENSE+Author: Ketil Malde+Maintainer: Ketil Malde <ketil@ii.uib.no>++Category: Bioinformatics+Synopsis: Tools for manipulating sequence clusters+Description: This is a bunch of stuff I needed at some for manipulating sequence+ clusters. See the README for details. The tools included are:+ .+ * filter - remove unwanted sequences from a clustering+ .+ * hist - produce a histogram of cluster sizes from a "label"-formatted clustering.+ .+ * clusc - compare clusterings, calculating numerous pair-based and entropy based indices.+ .+ * add_single - add singletons to a clustering.+ .+ * ace2contigs - parse an ACE assembly file, and output the contigs in a FASTA file.+ .+ * ace2fasta - parse an ACE assembly, and output each assembly in a separate FASTA file+ .+ * clusterlibs - given a table of regular expressions and library names, along with a+ clustering (TGICL-format), output a table of clusters with the library name+ prepended to the sequences.+ .+ The Darcs repository is at: <http://malde.org/~ketil/cluster_tools>.+Homepage: http://malde.org/~ketil/++Tested-With: GHC==6.8.2+Build-Type: Simple+Cabal-Version: >= 1.2++Data-Files: README++Executable filter+ Hs-source-dirs: src+ Main-Is: Filter.hs+ Build-Depends: base>3, containers, simpleargs>=0.1+ ghc-options: -Wall -O2 -funbox-strict-fields++Executable clusc+ Hs-source-dirs: src+ Main-Is: Cluscomp.lhs+ Build-Depends: bytestring++Executable add_single+ Hs-source-dirs: src+ Main-Is: AddSingletons.hs++Executable ace2contigs+ Hs-source-dirs: src+ Main-Is: Ace2Contigs.hs+ Build-depends: bio>=0.3.3++Executable ace2fasta+ Hs-source-dirs: src+ Main-Is: Ace2Fasta.hs++Executable clusterlibs+ Hs-source-dirs: src+ Main-Is: ClusterLibs.hs+ Build-depends: regex-compat
+ src/Ace2Contigs.hs view
@@ -0,0 +1,17 @@+-- ace2contigs+-- Extract the contigs with quality information from ace file++module Main where+import Prelude hiding (reads)++import Bio.Alignment.ACE+import Bio.Sequence++import System.SimpleArgs (getArgs)++main :: IO ()+main = do+ ace <- getArgs+ a <- readACE ace+ let cs = map (fst . contig) $ concat a+ writeFastaQual (ace++".fasta") (ace++".qual") cs
+ src/Ace2Fasta.hs view
@@ -0,0 +1,29 @@+-- ace2fa+-- Read an ACE file, output in Fasta format+-- (for importing into seaview)++module Main where+import Prelude hiding (reads)++import Bio.Alignment.ACE+import Bio.Sequence+import qualified Data.ByteString.Lazy.Char8 as B+import System.SimpleArgs (getArgs)+import System.IO++main :: IO ()+main = do + a <- readACE =<< getArgs+ mapM_ (\ss -> writeFasta (B.unpack $ seqlabel $ head ss) ss) (map pad_all $ concat a) ++pad_all :: Assembly -> [Sequence]+pad_all asm = pad 21 (contig asm) : map padRead (reads asm)+ where padRead (off,_dir,sq,gs) = pad (20+off) (sq,gs) -- note: they are already complemented++-- generate a sequence with '-' for gaps+pad :: Offset -> (Sequence,Gaps) -> Sequence+pad off (Seq n s _,gs) = let s' = (if off < 0 then B.drop (negate off) else B.append (B.replicate off '-')) (insertGaps '-' (s,gs))+ in (Seq n s' Nothing)+++
+ src/AddSingletons.hs view
@@ -0,0 +1,30 @@+-- add singleton clusters to clustering files+-- read files building map over words+-- add the difference++-- usage: add_single reference clustering1 [clustering2]..++import System.Environment (getArgs)+import System.IO+import Control.Monad+import Data.Set++main = do+ (ref:fs) <- getArgs+ r <- readFile ref+ mapM_ (append_s (mkindex r)) fs++append_s :: Set String -> FilePath -> IO ()+append_s ref f = do+ fc <- readFile f+ let fi = mkindex fc+ when (not . Data.Set.null . difference fi $ ref)+ (putStrLn ("Warning: '"++f++"' contains unknown words!"))+ let ss = difference ref fi+ when (Data.Set.null ss) (return ())+ h <- openFile (f++"_s") WriteMode+ mapM_ (hPutStrLn h) (toList ss)+ hClose h++mkindex :: String -> Set String+mkindex = foldr insert empty . words
+ src/Cluscomp.lhs view
@@ -0,0 +1,175 @@++Calculate the contingency matrix for two clusterings.+For clusterings $C = {c_1,c_2,...c_n} and $K = {k_1, k_2,..k_m}$+the matrix is defined as $ a_ij = c_i U k_j $.++The matrix is calculated lazily, and arbitrary functions can+be applied to it to derive various scores: edit distance,+jaccard index, exact count, etc.++\begin{code}++module Main where++import qualified Data.ByteString.Lazy.Char8 as B++import Data.Map hiding (map,filter,null)+import qualified Data.Map as M+import Data.List+import Data.Maybe+import System.Environment (getArgs)+import Text.Printf++type ByteString = B.ByteString++main :: IO ()+main = do+ (a:args) <- getArgs+ a' <- B.readFile a+ let ref = mkC $ readC a'+ n = maximum $ map length ("clustering":args)+ putStrLn (pad n "clustering" ++ "\tVI\tMIF\tJaccard\tRand\tFowMal\tSkew")+ mapM_ (comp n ref) args++pad i s = s ++ replicate (i-length s) ' '++comp :: Int -> Clustering -> FilePath -> IO ()+comp n k f = do+ f' <- B.readFile f+ let mx = matrix k (readC f')+ ct = mkPairs k mx+ sh = printf "%.3f"+ putStrLn $ concat $ intersperse "\t" $ (pad n f) : map sh+ [fst $ iscore mx -- VI+ ,snd $ iscore mx -- MIF+ ,jaccard ct, rand ct, fowmal ct, skew ct] -- ++"\tmatrix: "++show ct)++type CID = Int++type Sequence = ByteString -- hash for efficiency?+type Cluster = [Sequence]++type Clustering = (Map CID Cluster+ ,Map Sequence CID) -- optimization option++cid :: Clustering -> Sequence -> Maybe CID+cid k x = M.lookup x (snd k)++seqs :: Clustering -> CID -> Maybe Cluster+seqs k c = M.lookup c (fst k)++-- read a clustering from file+readC :: ByteString -> [Cluster]+readC = filter (not.null) . map B.words . B.lines++-- build the Clustering from a set of clusters+mkC :: [Cluster] -> Clustering+mkC = foldr ins (M.empty,M.empty) . zip [0..]+ where ins (n,ss) (i2c,s2i) = (M.insert n ss i2c, foldr (\s -> M.insert s n) s2i ss)++\end{code}++Matrix construction.++\begin{code}++type Row = [(Maybe CID,[Sequence])]+type Matrix = [Row]++-- reads a clustering and produces the confusion (-tingency) matrix+matrix :: Clustering -> [[Sequence]] -> Matrix+matrix c = map (matrixRow c)++matrixRow :: Clustering -> [Sequence] -> Row+matrixRow k c1 = comb $ sort $ map (\x -> (cid k x, [x])) c1 -- cid,seq+ where comb ((a,b):(c,d):xs) | a==c = comb ((a,b++d):xs)+ | otherwise = (a,b):comb ((c,d):xs)+ comb [(a,b)] = [(a,b)]+ comb [] = []++\end{code}++\subsection{Jaccard index, and friends}++Indices based on pair counts. This includes jaccard, rand, ...?+"skew" is based on MacNemar's test.++\begin{code}++type Pairs = (Double,Double,Double,Double) -- a b c, and d++jaccard, rand, fowmal, skew :: Pairs -> Double+jaccard (a,b,c,_) = a/(a+b+c)+rand (a,b,c,d) = (a+d)/(a+b+c+d)+fowmal (a,b,c,d) = a/sqrt((a+b)*(a+c)) -- Fowlkes-Mallows index+-- this is the same as fowmal (why?)!+hubert (a,b,c,d) = (a*d - b*c)/sqrt((a+b)*(c+d)*(a+c)*(b+d)) -- Gamma stat?++skew (a,b,c,d) = (b-c)/sqrt(b+c)/sqrt d++mkPairs :: Clustering -> Matrix -> Pairs+mkPairs k m = let (a,b,c,ct) = foldl (jc1 k) (0,0,0,0) m+ in (a/2,b/2,c/2,(ct*(ct-1)-a-b-c)/2)++-- this is pretty ugly!+jc1 :: Clustering -> Pairs -> Row -> Pairs+jc1 k j row = let row' = filter (isJust.fst) row+ l = sum $ map (len.snd) row'+ in foldr (jc2 k l) j row'+jc2 k lc (Just i,ss) (a,b,c,ct) = (a+ls*(ls-1),b+(ls*(lk-ls)),c+(ls*(lc-ls)),ct+ls)+ where ls = len ss+ lk = len $ fromJust (seqs k i) -- fixme! only seqs found in C!++-- todo: accumulate column sizes until end? how does clusqual deal?++\end{code}++\subsection{Entropy-based measures}++The \texttt{iscore} function calculates the variation of information+between the two clustrings. Given clusterings $C$ and $K$ matrix $H$, it is+calculated as:++\(+ \sum_C H_c\log H_c + \sum_K H_k\log H_k -2\sumH_{K,C} H_{k,c} \log H_{k,c}+\)++Todo: implement \[I/H = (H_c - H_k)/H_{c,k} - 1\]++I.e. collect H_k, H_c,k, and H_c, and use it to calculate both measures.++\begin{code}++type Imx = (Double,Double,Double,Double)++-- iscore calculates mutual information fraction and variation of information+iscore :: Matrix -> (Double,Double) -- MIF and VI+iscore mx = let (n,hc,hk,hck) = iscores empty (0,0,0,0) mx+ vi = 1/n*(-2*hck+hc+hk)+ mif = (hc+hk-2*mlog n)/(hck-mlog n)-1+ in (vi,mif)++-- iscores - calculate n, H_c, H_k, H_c,k+iscores:: Map Int Double -> Imx -> Matrix -> Imx+iscores m (n,hc,hk,hck) (r:rs) =+ let r' = map (len.snd) $ filter (isJust.fst) r+ in iscores (accum m r)+ (n+sum r',hc,hk+mlog (sum r'),hck+sum (map mlog r')) rs+iscores m (n,hc,hk,hck) [] =+ (n,(sum $ map mlog $ elems m),hk,hck)++-- accumulate the column sums for the matrix. This must be done+-- explicitly, since one clustering may contain sequences not present+-- in the other+accum :: Map Int Double -> Row -> Map Int Double+accum m row = foldr accum1 m row+ where accum1 (Just cid,seqs) m = insertWith (+) cid (len seqs) m+ accum1 (Nothing,_) m = m++mlog 0 = 0 -- error "log 0!"+mlog x = x*log x/log 2++len = fromIntegral . length++\end{code}+
+ src/ClusterLibs.hs view
@@ -0,0 +1,66 @@+-- clusterLibs+-- calculate clusters by library, using the lib table+-- read patterns from a table, first line is header++{-+import qualified Data.ByteString.Char8.Lazy as B+import Data.Map (Map)+import qualified Data.Map as M+++type Str = B.ByteString+type Clusters = Map Str [Str]++insert1 m k v = M.insertWith (flip (++)) k [v] m+-}++import System.Environment (getArgs)+import Text.Regex+import Data.List (elemIndex,sortBy,intersperse)++type Cluster = (String,[String])++main = do+ [ps,cs] <- getArgs `catch` error "Usage: clusterlibs ps cs"+ pat <- readPatternTable ps+ clus <- classClusters pat cs+ writeClusters clus++readPatternTable :: FilePath -> IO [(Regex,String)]+readPatternTable f = do+ (h:ls) <- return . map words . lines =<< readFile f+ let (p,n) = case (elemIndex "Pattern" h, elemIndex "Name" h) of+ (Just x,Just y) -> (x,y)+ _ -> error ("Need both 'Pattern' and 'Name' headers in '"++f++"'")+ z l = (mkRegex (l!!p),l!!n)+ return $ map z ls++-- will need to match against all, to check for multiple matches+-- tag names with library+classClusters :: [(Regex,String)] -> FilePath -> IO [Cluster]+classClusters ps f = return . getClusters (classify ps) . lines =<< readFile f++classify ps str = case concatMap (class1 str) ps of+ [] -> error ("no match for "++str)+ [x] -> x++":"++str+ s@(_:_) -> error ("multiple matches for "++str++": "++show s)+ where class1 str (r,s) = maybe [] (const [s]) (matchRegex r str)++getClusters f [] = []+getClusters f [_] = error "odd number of cluster lines!"+getClusters f (c:ss:rest) = case head $ words c of+ ('>':name) -> (name,map f $ words ss) : getClusters f rest+ _ -> error ("Cluster '"++c++"' didn't start with '>'")++-- transpose and print+writeClusters :: [Cluster] -> IO ()+writeClusters cs = do+ let cs' = map (\(h,s) -> (h:s)) cs+ cs'' = sortBy (\x y -> compare (length y) (length x)) cs'+ unwords' = concat . intersperse ","+ putStrLn $ unlines $ map unwords' $ transpose cs''++transpose :: [[String]] -> [[String]]+transpose [] = []+transpose ls' = let ls = filter (not . null) ls'+ in map head ls : transpose (map tail ls)
+ src/Filter.hs view
@@ -0,0 +1,10 @@++import System.SimpleArgs+import Data.Set hiding (filter,map,null)++main :: IO ()+main = interact . runFilter . fromList . words =<< readFile =<< getArgs+ +runFilter :: Set String -> String -> String+runFilter s = unlines . filter (not.null) . + map (unwords . filter (flip member s) . words) . lines