BioInf.MutationOrder.MinDist

Calculate minimum-distance Hamiltonian Shortest Paths and probabilities for starting nodes.

NOTE: We explicitly model starting nodes. For symmetrical distance matrices, this reports begin/end probabilities. For asymmetrical distance matrices, a second instances with Last instead of First boundary should be created to calculate begin/end probabilities separately.

Documentation

type ScaleFunction = RNA -> RNA -> Double

Given the RNA we come from and the RNA we mutate into, derive the gain or loss by a scaling function.

aMinDist :: Monad m => ScaleFunction -> Landscape -> SigMinDist m Double Double ((Int :. From) :. To) (Int :. To)

Minimal distance algebra

TODO The two Ints are the indices of the nodes and could be replaced?

aMinDistCount :: Monad m => ScaleFunction -> Landscape -> SigMinDist m (Double, Int) (Double, Int) ((Int :. From) :. To) (Int :. To)

Fused co-optimal counter!

TODO for now, Int is assumed to be big enough...

aInside :: Monad m => Maybe Int -> ScaleFunction -> Landscape -> SigMinDist m (Log Double) (Log Double) ((Int :. From) :. To) (Int :. To)

Sum over all states and collapse into boundary unscaled weights.

aPretty :: Monad m => ScaleFunction -> Landscape -> SigMinDist m Text [Text] ((Int :. From) :. To) (Int :. To)

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

aCount :: Monad m => Landscape -> SigMinDist m Integer [Integer] ((Int :. From) :. To) (Int :. To)

Count co-optimals

type TS1 x = TwITbl Id Unboxed EmptyOk (BS1 First I) x

type U x = TwITbl Id Unboxed EmptyOk (Unit I) x

type PF x = TwITbl Id Unboxed EmptyOk (Boundary First I) x

type TS1L x = TwITbl Id Unboxed EmptyOk (BS1 Last I) x

type PFL x = TwITbl Id Unboxed EmptyOk (Boundary Last I) x

type BT1 x b = TwITblBt Unboxed EmptyOk (BS1 First I) x Id Id b

type BTU x b = TwITblBt Unboxed EmptyOk (Unit I) x Id Id b

type BT1L x b = TwITblBt Unboxed EmptyOk (BS1 Last I) x Id Id b

forwardMinDist1 :: ScaleFunction -> Landscape -> (Z :. TS1L Double) :. U Double

Run the minimal distance algebra.

This produces one-boundary sets. Meaning that for each boundary we get the total distance within the set.

backtrackMinDist1 :: ScaleFunction -> Landscape -> ((Z :. TS1L Double) :. U Double) -> [Text]

minDistCount :: ScaleFunction -> Landscape -> (Z :. TS1L (Double, Int)) :. U (Double, Int)

Count the number of co-optimals

countBackMinDist1 :: ScaleFunction -> Landscape -> ((Z :. TS1L Double) :. U Double) -> [Integer]

runCoOptDist :: ScaleFunction -> Landscape -> (Double, [Text])

Given the Set1 produced in forwardMinDist1 we can now extract the co-optimal paths using the Set1 -> () index change.

TODO do we want this one explicitly or make life easy and just extract from all forwardMinDist1 paths?

runCount :: ScaleFunction -> Landscape -> (Double, Int)

boundaryPartFunFirst :: Maybe Int -> ScaleFunction -> Landscape -> [(Boundary First I, Log Double)]

Extract the individual partition scores.

boundaryPartFunLast :: Maybe Int -> ScaleFunction -> Landscape -> BoundaryPart

data BoundaryPart

Constructors

BoundaryPart

Fields

• bpNormalized :: [(Boundary Last I, Log Double)]
• bpUnnormalized :: [(Boundary Last I, Log Double)]
• bpTotal :: Log Double

Instances

Eq BoundaryPart

Methods

(==) :: BoundaryPart -> BoundaryPart -> Bool

(/=) :: BoundaryPart -> BoundaryPart -> Bool

Show BoundaryPart

Methods

showsPrec :: Int -> BoundaryPart -> ShowS

show :: BoundaryPart -> String

showList :: [BoundaryPart] -> ShowS

boundaryPart :: [(Boundary Last I, Log Double)] -> BoundaryPart