ISS_preprocess: Preprocess ISS data based on different normalization method.

In mashranga/MolDia: Single cell In-Situ sequencing (ISS) data analysis

Description

Pre-process RCA data interms of normalization, scalling and centering

Usage

ISS_preprocess(data, normalization.method = NULL, scale.factor = 10000,
  do.scale = FALSE, do.center = FALSE, display.progress = FALSE)

Arguments

data	Input data in class MolDiaISS. Output of readISS
normalization.method	Method for normalization. Default is NULL. Available methods are "LogNormalize", "RankNormalize" and "QuantileNormalize".
scale.factor	Sets the scale factor for cell-level normalization.
do.scale	Whether to scale the data. See details.
do.center	Whether to center the data. See details.
display.progress	Displays a progress bar

Details

Setting center to TRUE will center the expression for each gene by subtracting the average expression for that gene. Setting scale to TRUE will scale the expression level for each gene by dividing the centered gene expression levels by their standard deviations if center is TRUE and by their root mean square otherwise.

Value

Return a object in MolDiaISS with value in slot norm.data or scale.data

References

Van den Berg, R. A., Hoefsloot, H. C., Westerhuis, J. A., Smilde, A. K., & van der Werf, M. J. (2006). Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics, 7, 142 http://doi.org/10.1186/1471-2164-7-142

Examples

mydata  <- readISS(file = system.file("extdata", "CellBlobs_QT_0.35.csv", package="MolDia"),
                   cellid = "CellID", centX = "centroidX", centY = "centroidY")
res     <- ISS_preprocess(mydata, normalization.method = "QuantileNormalize", do.scale = FALSE, do.center = FALSE)

mashranga/MolDia documentation built on May 26, 2019, 9:36 a.m.