Normalization (FSharp.Stats) | FSharp.Stats

FSharp.Stats

Normalization Module

Namespace: FSharp.Stats.Signal
Assembly: FSharp.Stats.dll

Types

Type	Description
MorResult	Summary of the median of ratios (mor) normalization with normed data, determined correctionfactors, and transformation function.

Functions and values

Function or value	Description
`medianOfRatios data` Full Usage: `medianOfRatios data` Parameters: data : `Matrix<float>` - data matrix with columns as features (samples,time points) and rows as measured entities (genes,proteins). Returns: `MorResult` Normalized data matrix with correction factors and normalization function.	Median of ratios normalization As used by Deseq2, see: https://github.com/hbctraining/DGE_workshop/blob/master/lessons/02_DGE_count_normalization.md . Rows are genes, columns are samples data : `Matrix<float>` data matrix with columns as features (samples,time points) and rows as measured entities (genes,proteins). Returns: `MorResult` Normalized data matrix with correction factors and normalization function. Example `// raw data with proteins as rows and samples as columns let myData = Matrix.init 500 5 (fun _ _ -> rnd.NextDouble()) let normedData = Normalization.medianOfRatios myData` val myData: obj val normedData: obj
`medianOfRatiosBy f data` Full Usage: `medianOfRatiosBy f data` Parameters: f : `float -> float` - The transformation function is applied on all values of the matrix before calculating the normalization factors. data : `Matrix<float>` - data matrix with columns as features (samples,time points) and rows as measured entities (genes,proteins). Returns: `MorResult` Normalized data matrix with correction factors and normalization function.	Median of ratios normalization As used by Deseq2, see: https://github.com/hbctraining/DGE_workshop/blob/master/lessons/02_DGE_count_normalization.md . Rows are genes, columns are samples f : `float -> float` The transformation function is applied on all values of the matrix before calculating the normalization factors. data : `Matrix<float>` data matrix with columns as features (samples,time points) and rows as measured entities (genes,proteins). Returns: `MorResult` Normalized data matrix with correction factors and normalization function. Example `// raw data with proteins as rows and samples as columns let myData = Matrix.init 500 5 (fun _ _ -> rnd.NextDouble()) let normedData = Normalization.medianOfRatiosBy (fun x -> ln (x+1)) myData` val myData: obj val normedData: obj
`medianOfRatiosWide data` Full Usage: `medianOfRatiosWide data` Parameters: data : `Matrix<float>` - data matrix with columns as measured entities and rows as features (samples,time points) (genes,proteins). Returns: `MorResult` Normalized data matrix with correction factors and normalization function.	Median of ratios normalization As used by Deseq2, see: https://github.com/hbctraining/DGE_workshop/blob/master/lessons/02_DGE_count_normalization.md . Columns are genes, rows are samples data : `Matrix<float>` data matrix with columns as measured entities and rows as features (samples,time points) (genes,proteins). Returns: `MorResult` Normalized data matrix with correction factors and normalization function. Example `// raw data with proteins as columns and samples as rows let myData = Matrix.init 5 500 (fun _ _ -> rnd.NextDouble()) let normedData = Normalization.medianOfRatiosWide myData` val myData: obj val normedData: obj
`medianOfRatiosWideBy f data` Full Usage: `medianOfRatiosWideBy f data` Parameters: f : `float -> float` - The transformation function is applied on all values of the matrix before calculating the normalization factors. data : `Matrix<float>` - data matrix with columns as measured entities and rows as features (samples,time points) (genes,proteins). Returns: `MorResult` Normalized data matrix with correction factors and normalization function.	Median of ratios normalization As used by Deseq2, see: https://github.com/hbctraining/DGE_workshop/blob/master/lessons/02_DGE_count_normalization.md . Columns are genes, rows are samples f : `float -> float` The transformation function is applied on all values of the matrix before calculating the normalization factors. data : `Matrix<float>` data matrix with columns as measured entities and rows as features (samples,time points) (genes,proteins). Returns: `MorResult` Normalized data matrix with correction factors and normalization function. Example `// raw data with proteins as columns and samples as rows let myData = Matrix.init 5 500 (fun _ _ -> rnd.NextDouble()) let normedData = Normalization.medianOfRatiosWideBy (fun x -> ln (x+1)) myData` val myData: obj val normedData: obj
`quantile data` Full Usage: `quantile data` Parameters: data : `Matrix<float>` - data matrix with columns as measured entities and rows as features (samples,time points) (genes,proteins). Returns: `Matrix<float>` Normalized data matrix.	Quantile normalization with equal number of elements (rows) for each sample (column). Column mean and column standard deviation are qual after normalization. Rows are genes, columns are samples. data : `Matrix<float>` data matrix with columns as measured entities and rows as features (samples,time points) (genes,proteins). Returns: `Matrix<float>` Normalized data matrix. Example `// raw data with proteins as rows and samples as columns let myData = Matrix.init 500 5 (fun _ _ -> rnd.NextDouble()) let normedData = Normalization.quantile myData` val myData: obj val normedData: obj
`zScoreTransform yData` Full Usage: `zScoreTransform yData` Parameters: yData : `Vector<float>` - collection of values to be transformed Returns: `Vector<float>` transformed yData in unchanged order	z score normalization/transformation using the sample standard deviation. Rarely used since variance is not equal to 1. Bortz J., Schuster C., Statistik für Human- und Sozialwissenschaftler, 7 (2010), p. 35 yData : `Vector<float>` collection of values to be transformed Returns: `Vector<float>` transformed yData in unchanged order Example `// transform data, such that data has zero mean and sample standard deviation of 1 Normalization.zScoreTransform (vector [\|1.1;5.3;-9.0;13.2;17.3;-2.3\|])`
`zScoreTransformPopulation yData` Full Usage: `zScoreTransformPopulation yData` Parameters: yData : `Vector<float>` - collection of values to be transformed Returns: `Vector<float>` transformed yData in unchanged order	z score normalization/transformation using the population standard deviation. Bortz J., Schuster C., Statistik für Human- und Sozialwissenschaftler, 7 (2010), p. 35 yData : `Vector<float>` collection of values to be transformed Returns: `Vector<float>` transformed yData in unchanged order Example `// transform data, such that data has zero mean and population standard deviation of Normalization.zScoreTransformPopulation (vector [\|1.1;5.3;-9.0;13.2;17.3;-2.3\|])`