Linnorm.Cor: Linnorm-gene correlation network analysis.

Description Usage Arguments Details Value Examples

View source: R/Linnorm.Cor.R

Description

This function first performs Linnorm transformation on the dataset. Then, it will perform correlation network analysis on the dataset.

Usage

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Linnorm.Cor(datamatrix, RowSamples = FALSE, input = "Raw",
  method = "pearson", MZP = 0.5, sig.q = 0.05, plotNetwork = TRUE,
  plotNumPairs = 5000, plotdegree = 0, plotname = "networkplot",
  plotformat = "png", plotVertexSize = 1, plotFontSize = 1,
  plot.Pos.cor.col = "red", plot.Neg.cor.col = "green",
  vertex.col = "cluster", plotlayout = "kk",
  clusterMethod = "cluster_edge_betweenness", ...)

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.

input

Character. "Raw" or "Linnorm". In case you have already transformed your dataset with Linnorm, set input into "Linnorm" so that you can put the Linnorm transformed dataset into the "datamatrix" argument. Defaults to "Raw".

method

Character. "pearson", "kendall" or "spearman". Method for the calculation of correlation coefficients. Defaults to "pearson".

MZP

Double >=0, <= 1. Minimum non-Zero Portion Threshold for this function. Genes not satisfying this threshold will be removed for correlation calculation. For exmaple, if set to 0.3, genes without at least 30 percent of the samples being non-zero will be considered for this study. Defaults to 0.5.

sig.q

Double >=0, <= 1. Only gene pairs with q values less than this threshold will be included in the "Results" data frame. Defaults to 0.05.

plotNetwork

Logical. Should the program output the network plot to a file? An "igraph" object will be included in the output regardless. Defaults to TRUE.

plotNumPairs

Integer >= 50. Number of gene pairs to be used in the network plot. Defaults to 5000.

plotdegree

Integer >= 0. In the network plot, genes (vertices) without at least this number of degree will be removed. Defaults to 0.

plotname

Character. Name of the network plot. File extension will be appended to it. Defaults to "networkplot".

plotformat

Character. "pdf" or "png". Network plot output format. Defaults to "png".

plotVertexSize

Double >0. Controls vertex Size in the network plot. Defaults to 1.

plotFontSize

Double >0. Controls font Size in the network plot. Defaults to 1.

plot.Pos.cor.col

Character. Color of the edges of positively correlated gene pairs. Defaults to "red".

plot.Neg.cor.col

Character. Color of the edges of negatively correlated gene pairs. Defaults to "green".

vertex.col

Character. "cluster" or a color. This controls the color of the vertices. Defaults to "cluster".

plotlayout

Character. "kk" or "fr". "kk" uses Kamada-Kawai algorithm in igraph to assign vertex and edges. It scales edge length with correlation strength. However, it can cause overlaps between vertices. "fr" uses Fruchterman-Reingold algorithm in igraph to assign vertex and edges. It prevents overlatps between vertices better than "kk", but edge lengths are not scaled to correlation strength. Defaults to "kk".

clusterMethod

Character. "cluster_edge_betweenness", "cluster_fast_greedy", "cluster_infomap", "cluster_label_prop", "cluster_leading_eigen", "cluster_louvain", "cluster_optimal", "cluster_spinglass" or "cluster_walktrap". These are clustering functions from the igraph package. Defaults to "cluster_edge_betweenness".

...

arguments that will be passed into Linnorm's transformation function.

Details

This function performed gene correlated study in the dataset by using Linnorm transformation

Value

This function will output a list with the following objects:

The "Results" data frame has the following columns:

Examples

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data(Islam2011)
#Analysis on Islam2011 embryonic stem cells
results <- Linnorm.Cor(Islam2011[,1:48], plotNetwork=FALSE)

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