FDRControl (BioFSharp.Mz) | BioFSharp.Mz

BioFSharp.Mz

FDRControl Module

Namespace: BioFSharp.Mz
Assembly: BioFSharp.Mz.dll

Types and nested modules

Type/Module	Description
MAYU
QValueInput	Input for QValue calulation
ScoreTargetDecoyCount

Functions and values

Function or value	Description
`binningFunction bandwidth pi0 scoreF isDecoyF data` Full Usage: `binningFunction bandwidth pi0 scoreF isDecoyF data` Parameters: bandwidth : `float` pi0 : `float` scoreF : `'A -> float` isDecoyF : `'A -> bool` data : `'A[]` Returns: `Vector<float> * Vector<float> * Vector<float>`	returns scores, pep, q bandwidth : `float` pi0 : `float` scoreF : `'A -> float` isDecoyF : `'A -> bool` data : `'A[]` Returns: `Vector<float> * Vector<float> * Vector<float>`
`calculatePEPValues totalCountF decoyCountF scoreF dataFreq` Full Usage: `calculatePEPValues totalCountF decoyCountF scoreF dataFreq` Parameters: totalCountF : `'a -> float` decoyCountF : `'a -> float` scoreF : `'a -> float` dataFreq : `'a[]` Returns: `(float * float) list`	Calculates the PEP value based on the ratio of Decoys to targets at a given score totalCountF : `'a -> float` decoyCountF : `'a -> float` scoreF : `'a -> float` dataFreq : `'a[]` Returns: `(float * float) list`
`calculateQValueLogReg fdrEstimate bandwidth data isDecoy decoyScoreF targetScoreF` Full Usage: `calculateQValueLogReg fdrEstimate bandwidth data isDecoy decoyScoreF targetScoreF` Parameters: fdrEstimate : `float` bandwidth : `float` data : `'a[]` isDecoy : `'a -> bool` decoyScoreF : `'a -> float` targetScoreF : `'a -> float` Returns: `float -> float`	Gives a function to calculate the q value for a score in a dataset using Lukas method and Levenberg Marguardt fitting fdrEstimate : `float` bandwidth : `float` data : `'a[]` isDecoy : `'a -> bool` decoyScoreF : `'a -> float` targetScoreF : `'a -> float` Returns: `float -> float`
`calculateQValueStorey data isDecoy decoyScoreF targetScoreF` Full Usage: `calculateQValueStorey data isDecoy decoyScoreF targetScoreF` Parameters: data : `'a[]` isDecoy : `'a -> bool` decoyScoreF : `'a -> float` targetScoreF : `'a -> float` Returns: `float -> float`	Gives a function to calculate the q value for a score in a dataset using Storeys method data : `'a[]` isDecoy : `'a -> bool` decoyScoreF : `'a -> float` targetScoreF : `'a -> float` Returns: `float -> float`
`createQValueInput score isDecoy` Full Usage: `createQValueInput score isDecoy` Parameters: score : `float` isDecoy : `bool` Returns: `QValueInput`	score : `float` isDecoy : `bool` Returns: `QValueInput`
`createScoreTargetDecoyCount score decoyCount targetCount` Full Usage: `createScoreTargetDecoyCount score decoyCount targetCount` Parameters: score : `float` decoyCount : `float` targetCount : `float` Returns: `ScoreTargetDecoyCount`	Gives the decoy and target count at a specific score score : `float` decoyCount : `float` targetCount : `float` Returns: `ScoreTargetDecoyCount`
`createTargetDecoyHis bandwidth isDecoy decoyScoreF targetScoreF data` Full Usage: `createTargetDecoyHis bandwidth isDecoy decoyScoreF targetScoreF data` Parameters: bandwidth : `float` isDecoy : `'a -> bool` decoyScoreF : `'a -> float` targetScoreF : `'a -> float` data : `'a[]` Returns: `(float * int * int * float) array`	Creates a Histogram based on a given score of a target/decoy dataset. Each bin contains the information of the total count, the decoy count and the median score. (Bin, Count, DecoyCount, Median Score) bandwidth : `float` isDecoy : `'a -> bool` decoyScoreF : `'a -> float` targetScoreF : `'a -> float` data : `'a[]` Returns: `(float * int * int * float) array`
`getLogisticRegressionFunction x y epsilon` Full Usage: `getLogisticRegressionFunction x y epsilon` Parameters: x : `vector` y : `vector` epsilon : `float` Returns: `float -> float`	for given data, creates a logistic regression model and returns a mapping function for this model x : `vector` y : `vector` epsilon : `float` Returns: `float -> float`
`initCalculateLin trace bandwidth isDecoy decoyScoreF targetScoreF data` Full Usage: `initCalculateLin trace bandwidth isDecoy decoyScoreF targetScoreF data` Parameters: trace : `string -> unit` bandwidth : `float` isDecoy : `'a -> bool` decoyScoreF : `'a -> float` targetScoreF : `'a -> float` data : `'a[]` Returns: `float -> float`	Calculates monotonized PEP values for a target/decoy dataset based on the decoy/target ratio. Entries are binned with a given bandwidth as intital estiamtor based on the scores. Returns a function which maps from score to PEP value based on a fit of a linear function using linear regression. The linear regression is performed on the logit transformed pep values. The fit focuses on the pep values centered aound the middle of the score distribution trace : `string -> unit` bandwidth : `float` isDecoy : `'a -> bool` decoyScoreF : `'a -> float` targetScoreF : `'a -> float` data : `'a[]` Returns: `float -> float`
`logitTransformPepValues score pepVal` Full Usage: `logitTransformPepValues score pepVal` Parameters: score : `'a array` pepVal : `float array` Returns: `'a array * float array`	Logit transforms pep values (log10) score : `'a array` pepVal : `float array` Returns: `'a array * float array`