scaleData: Standardize a matrix to have optionally row means zero and...

In msImpute: Imputation of label-free mass spectrometry peptides

Description

Standardize a matrix to have optionally row means zero and variances one, and/or column means zero and variances one.

Usage

scaleData(
  object,
  maxit = 20,
  thresh = 1e-09,
  row.center = TRUE,
  row.scale = TRUE,
  col.center = TRUE,
  col.scale = TRUE,
  trace = FALSE
)

Arguments

object	numeric matrix giving log-intensity where missing values are denoted by NA. Rows are peptides, columns are samples.
maxit	numeric. maximum iteration for the algorithm to converge (default to 20). When both row and column centering/scaling is requested, iteration may be necessary.
thresh	numeric. Convergence threshold (default to 1e-09).
row.center	logical. if row.center==TRUE (the default), row centering will be performed resulting in a matrix with row means zero. If row.center is a vector, it will be used to center the rows. If row.center=FALSE nothing is done.
row.scale	if row.scale==TRUE, the rows are scaled (after possibly centering, to have variance one. Alternatively, if a positive vector is supplied, it is used for row centering.
col.center	Similar to row.center
col.scale	Similar to row.scale
trace	logical. With trace=TRUE, convergence progress is reported, when iteration is needed.

Details

Standardizes rows and/or columns of a matrix with missing values, according to the biScale algorithm in Hastie et al. 2015. Data is assumed to be normalised and log-transformed. Please note that data scaling might not be appropriate for MS1 data. A good strategy is to compare mean-variance plot (plotCV2) before and after imputation. If the plots look differently, you may need to skip data scaling. The MS1 data are more variable (tend to have higher CV^2), and may contain outliers which will skew the scaling.

Value

A list of two components: E and E.scaled. E contains the input matrix, E.scaled contains the scaled data

References

Hastie, T., Mazumder, R., Lee, J. D., & Zadeh, R. (2015). Matrix completion and low-rank SVD via fast alternating least squares. The Journal of Machine Learning Research, 16(1), 3367-3402.

Hediyeh-zadeh, S., Webb, A. I., & Davis, M. J. (2020). MSImpute: Imputation of label-free mass spectrometry peptides by low-rank approximation. bioRxiv.

See Also

selectFeatures, msImpute

Examples

data(pxd010943)
y <- pxd010943
y <- log2(y)
keep <- (rowSums(!is.na(y)) >= 4)
y <- as.matrix.data.frame(y[keep,])
y <- scaleData(y, maxit=30)

msImpute documentation built on Nov. 8, 2020, 5:26 p.m.