Package 'DNLC'

Calculate local moran's I matrix for a network and a gene expression matrix

Description

cal_lmi_data() will calculate the local moran's I data for a input igraph object and gene expression data matrix.

Usage

cal_lmi_data(gene_expr, gene_graph)

Arguments

gene_expr	Expression for genes. Each row is a gene, and each column is a sample.
gene_graph	The graph of gene network.

Details

cal_lmi_data() will calculate the local moran's I matrix for a input igraph object and gene expression data matrix. The function will return a matrix in the same dimension of the input gene expression matrix. Every gene x's lmi data takes a row.

Value

A table of local moran's I data. Row name is gene id. Each row stands for the local moran's I data of gene x. Each column stands for a sample.

Examples

## Not run: 
simulation <- init_simulation_gene_net()
lmi_data = cal_lmi_data( simulation$gene_expr, simulation$gene_graph)
t_data = DNLC_statistics(simulation$gene_graph, simulation$gene_expr,
  simulation$patient_matrix, lmi_data = lmi_data)
fdr_result <- gene_fdrtest(t_data)
sig_genes <- significant_genes(fdr_obj = fdr_result, thres = 0.2)
## End(Not run)

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calculate t statistics for gene graph using DNLC method.

Description

a function to calculate t statistics for genes in the graph.

Usage

DNLC_statistics(gene_graph, gene_expr = "x", clinic_data = "y", 
    confounder_matrix = NULL, lmi_data = NULL)

Arguments

gene_graph	graph of gene
gene_expr	expr of gene
clinic_data	patient data
confounder_matrix	other message describe clinic message
lmi_data	lmi data for each gene.

Details

This function first calculates the matrix of local moran's I, and then conducts testing for the association of each gene's local moran's I with the clinical outcome variable. Clinical confounder variables such as age, gender etc can be included.

Value

all_gene_id	gene ids in graph
t_data	t-data for each gene

Examples

## Not run: 
simulation <- init_simulation_gene_net()
t_data = DNLC_statistics(simulation$gene_graph, simulation$gene_expr,
  simulation$patient_matrix, lmi_data = simulation$lmi_matrix)
fdr_result <- gene_fdrtest(t_data)
sig_genes <- significant_genes(fdr_obj = fdr_result, thres = 0.2)

## End(Not run)

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Use local false discovery rate for the detection of genes with significant LMI change

Description

This function use locfdr function to calculate fdr_result

Usage

gene_fdrtest(gene.data)

Arguments

gene.data

gene_id_all: gene id t_data: t statistic for each gene

Value

return fdr_result for t_data

fdr$name	all gene id
fdr$fdr	fdr value for gene

Examples

## Not run: 
simulation <- init_simulation_gene_net()
t_data = DNLC_statistics(simulation$gene_graph, simulation$gene_expr,
  simulation$patient_matrix, lmi_data=simulation$lmi_matrix)
fdr_result <- gene_fdrtest(t_data)
sig_genes <- significant_genes(fdr_obj = fdr_result, thres = 0.2)

## End(Not run)

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Create a random network for simulation

Description

This function will create a network for DNLC. This function will change correlation of chosen genes and its one hop neighbor between treatment groups to simulate LMI changes.

Usage

init_simulation_gene_net(base_correlation = 0.4, 
    change_correlation = 0.8, sample_size = 100, num_gene = 5000, change_gene_num=5)

Arguments

base_correlation	base correlation of network
change_correlation	change correlation for selected genes
sample_size	multi size of patient data
num_gene	gene number in the network
change_gene_num	number of genes around which the correlation structure is to be changed

Value

lmi_matrix	matrix of local moran's I data
patient_matrix	matrix of patient data
neigh_list	id of changed gene.
gene_graph	igraph object of gene network
gene_expr	gene expression data matirx

Examples

## Not run: 
simulation <- init_simulation_gene_net()
t_data = DNLC_statistics(simulation$gene_graph, simulation$gene_expr,
  simulation$patient_matrix, lmi_data = simulation$lmi_matrix)
fdr_result <- gene_fdrtest(t_data)
sig_genes <- significant_genes(fdr_obj = fdr_result, thres = 0.2)
## End(Not run)

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Selecting significant genes according to fdr result

Description

Choose the significant genes according to fdr result

Usage

significant_genes(fdr_obj, thres)

Arguments

fdr_obj	fdr result come from function gene_fdrtest
thres	threshold to identify significant genes

Value

ID of significant genes

Examples

## Not run: 
simulation <- init_simulation_gene_net()
t_data = DNLC_statistics(simulation$gene_graph, simulation$gene_expr,
  simulation$patient_matrix, lmi_data = simulation$lmi_matrix)
fdr_result <- gene_fdrtest(t_data)
sig_genes <- significant_genes(fdr_obj = fdr_result, thres = 0.2)

## End(Not run)

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