clustertools-0.1.2: src/ClusterLibs.hs
-- clusterLibs
-- calculate clusters by library, using the lib table
-- read patterns from a table, first line is header
import System.Environment (getArgs)
import Text.Regex
import Data.List (elemIndex,sortBy,intersperse)
import Data.Map hiding (map)
import Numeric (showFFloat)
import Statistics
type LibTable = [(Regex,String)]
type Cluster = (String,[String])
main :: IO ()
main = do
[ps,cs] <- do [xs,ys] <- getArgs
return [xs,ys]
`catch` error "Usage: clusterlibs <libtable> <clusters>"
pat <- readPatternTable ps
counts <- totalsByLib pat cs
writeTable [("","sum":map snd pat++["significance"])]
writeTable . map (decorate counts) . countClusters pat =<< readClusters cs
-- counts is
decorate :: (String,[Int]) -> (String,[Int]) -> (String,[String])
decorate counts (x,ys) =
let fractions = [ fromIntegral x / fromIntegral (sum $ snd counts) | x <- snd counts]
clustersize = sum ys
significance = unwords $ zipWith isSignificant ys fractions
isSignificant obs frac = showFFloat (Just 3) (pvalue (1-frac) clustersize (clustersize-obs)) ""
in (x,map show (clustersize:ys) ++ [significance])
{-
clus <- classClusters pat cs
writeTable clus
-}
-- --------------------------------------------------
-- | Calculate the p value of observing k sequences
-- from a library, given an a priori background distribution.
pvalue :: Double -> Int -> Int -> Double
pvalue fraction tot obs = cumbin fraction tot obs
-- --------------------------------------------------
-- | Read a LibTable from a file of wsp-separated columns, first line is
-- a header, and only columns "Pattern" and "Name" are used.
readPatternTable :: FilePath -> IO LibTable
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 | length l < max p n = error ("Line in library table too short:\n"++show l)
| otherwise = (mkRegex (l!!p),l!!n)
return $ map z ls
-- --------------------------------------------------
-- | Acquire total counts by library from a clustering
totalsByLib :: LibTable -> FilePath -> IO (String,[Int])
totalsByLib lt f = do
cs <- readClusters f
let unify cs = [("total", concatMap snd cs)]
case countClusters lt (unify cs) of
[x] -> return x
_ -> error "totalsByLib: this is impossible"
-- --------------------------------------------------
-- | Parse clusters
readClusters :: FilePath -> IO [Cluster]
readClusters f = readFile f >>= return . rc . lines
where rc [] = []
rc [_] = error "odd number of cluster lines!"
rc (c:ss:rest) = case head $ words c of
('>':name) -> (name,words ss) : rc rest
_ -> error ("Cluster '"++c++"' didn't start with '>'")
classify :: LibTable -> String -> String
classify ps str = case concatMap (class1 str) ps of
[] -> error ("no match for "++str++" in library table")
[x] -> x
s@(_:_) -> error ("multiple matches for "++str++": "++show s)
where class1 st (r,s) = maybe [] (const [s]) (matchRegex r st)
-- --------------------------------------------------
-- will need to match against all, to check for multiple matches
-- tag names with library
classClusters :: LibTable -> FilePath -> IO [Cluster]
classClusters ps f = return . map class1 =<< readClusters f
where class1 (l,ss) = (l, map (\s -> classify ps s++":"++s) ss)
-- | Output the clusters to stdout
writeTable :: [(String,[String])] -> IO ()
writeTable = putStrLn . unlines . map show1
where
show1 (name,stuff) = concat $ intersperse "\t" (name:stuff)
-- count by classification
countClusters :: LibTable -> [Cluster] -> [(String,[Int])]
countClusters lt = map count1
where count1 (c,ss) = (c,map (\k -> findWithDefault 0 k (counts ss)) (map snd lt))
counts ss = fromListWith (+) $ zip (map (classify lt) ss) $ repeat 1