Linnorm: Linnorm Normalizing Transformation Function

Description Usage Arguments Details Value Examples

View source: R/Linnorm.R

Description

This function performs the Linear model and normality based transformation method (Linnorm) for (sc)RNA-seq expression data or large scale count data.

Usage

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Linnorm(datamatrix, RowSamples = FALSE, spikein = NULL,
  spikein_log2FC = NULL, showinfo = FALSE, perturbation = "Auto",
  Filter = FALSE, minNonZeroPortion = 0.75, L_F_p = 0.3173,
  L_F_LC_Genes = "Auto", L_F_HC_Genes = 0.01, BE_F_p = 0.3173,
  BE_F_LC_Genes = "Auto", BE_F_HC_Genes = 0.01, BE_strength = 0.5,
  max_F_LC = 0.75, DataImputation = FALSE, ...)

Arguments

datamatrix

The matrix or data frame that contains your dataset. Each row is a feature (or Gene) and each column is a sample (or replicate). Raw Counts, CPM, RPKM, FPKM or TPM are supported. Undefined values such as NA are not supported. It is not compatible with log transformed datasets.

RowSamples

Logical. In the datamatrix, if each row is a sample and each row is a feature, set this to TRUE so that you don't need to transpose it. Linnorm works slightly faster with this argument set to TRUE, but it should be negligable for smaller datasets. Defaults to FALSE.

spikein

character vector. Names of the spike-in genes in the datamatrix. Defaults to NULL.

spikein_log2FC

Numeric vector. Log 2 fold change of the spike-in genes. Defaults to NULL.

showinfo

Logical. Show algorithm running information. Defaults to FALSE.

perturbation

Integer >=2 or "Auto". To search for an optimal minimal deviation parameter (please see the article), Linnorm uses the iterated local search algorithm which perturbs away from the initial local minimum. The range of the area searched in each perturbation is exponentially increased as the area get further away from the initial local minimum, which is determined by their index. This range is calculated by 10 * (perturbation ^ index). Defaults to "Auto".

Filter

Logical. Should Linnorm filter the dataset in the end results? Defaults to FALSE.

minNonZeroPortion

Double >= 0.01, <= 0.95. Minimum non-Zero Portion Threshold. Genes not satisfying this threshold will be removed. For exmaple, if set to 0.3, genes without at least 30 percent of the samples being non-zero will be removed. Defaults to 0.75.

L_F_p

Double >= 0, <= 1. Filter genes with standard deviation and skewness less than this p value before applying Linnorm algorithm. Defaults to 0.3173.

L_F_LC_Genes

Double >= 0.01, <= 0.95 or Character "Auto". Filter this portion of the lowest expressing genes before applying Linnorm algorithm. It can be determined automatically by setting to "Auto". Defaults to "Auto".

L_F_HC_Genes

Double >= 0.01, <= 0.95. Filter this portion of the highest expressing genes before applying Linnorm algorithm. Defaults to 0.01.

BE_F_p

Double >=0, <= 1. Filter genes with standard deviation and skewness less than this p value before applying Linnorm's batch effect normalization algorithm. Defaults to 0.3173.

BE_F_LC_Genes

Double >= 0.01, <= 0.95 or Character "Auto". Filter this portion of the lowest expressing genes before applying Linnorm's batch effect normalization algorithm. It can be determined automatically by setting to "Auto". Defaults to "Auto".

BE_F_HC_Genes

Double >= 0.01, <= 0.95. Filter this portion of the highest expressing genes before applying Linnorm's batch effect normalization algorithm. Defaults to 0.01.

BE_strength

Double >0, <= 1. Before Linnorm transformation, how strongly should Linnorm normalize batch effects? Defaults to 0.5.

max_F_LC

Double >=0, <= 0.95. When L_F_LC or B_F_LC is set to auto, this is the maximum threshold that Linnorm would assign. Defaults to 0.75.

DataImputation

Logical. Perform data imputation on the dataset after transformation. Defaults to FALSE.

...

place holder for any new arguments.

Details

This function normalizes and transforms the input dataset using the Linnorm algorithm.

Value

This function returns a transformed data matrix.

Examples

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#Obtain example matrix:
data(LIHC)
#Transformation:
transformedExp <- Linnorm(LIHC)

Linnorm documentation built on Nov. 17, 2017, 11:50 a.m.