Linnorm.HVar: Linnorm-Hvar pipeline for highly variable gene discovery.

In Linnorm: Linear model and normality based normalization and transformation method (Linnorm)

Description

This function first performs Linnorm transformation on the dataset. Then, it will perform highly variable gene discovery.

Usage

Linnorm.HVar(datamatrix, RowSamples = FALSE, spikein = NULL,
  spikein_log2FC = NULL, log.p = FALSE, sig.value = "p", sig = 0.05,
  MZP = 0.25, FG_Recov = 0.5, plot.title = "Mean vs SD plot", ...)

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.
log.p	Logical. Output p/q values in log scale. Defaults to FALSE.
sig.value	Character. "p" or "q". Use p or q value for highlighting significant genes. Defaults to "p".
sig	Double >0, <= 1. Significant level of p or q value for plotting. Defaults to 0.05.
MZP	Double >=0, <= 1. Minimum non-Zero Portion Threshold for this function. Genes not satisfying this threshold will be removed from HVG anlaysis. For exmaple, if set to 0.3, genes without at least 30 percent of the samples being non-zero will be removed. Defaults to 0.25.
FG_Recov	Double >=0, <= 1. In the low count gene filtering algorithm, recover this portion of genes that are filtered. Defaults to 0.5.
plot.title	Character. The plot's title. Defaults to "Mean vs SD plot".
...	arguments that will be passed into Linnorm's transformation function.

Details

This function discovers highly variable gene in the dataset using Linnorm transformation.

Value

This function will output a list with the following objects:

• Results: A matrix with the results.

• plot: Mean vs Standard Deviation Plot which highlights significant genes.

• Linnorm: Linnorm transformed and filtered data matrix.

The Results matrix has the following columns:

• Transformed.Avg.Exp: Average expression of non-zero Linnorm transformed data.

• Transformed.SD: Standard deviation of non-zero Linnorm transformed data.

• Normalized.Log2.SD.Fold.Change: Normalized log2 fold change of the gene's standard deviation.

• p.value: p value of the statistical test.

• q.value: q value/false discovery rate/adjusted p value of the statistical test.

Examples

data(Islam2011)
results <- Linnorm.HVar(Islam2011)

Linnorm documentation built on Nov. 8, 2020, 6:48 p.m.