| Title: | Differential Hub Gene Analysis |
|---|---|
| Description: | Identification of hub genes in a gene co-expression network from gene expression data. The differential network analysis for two contrasting conditions leads to the identification of various types of hubs like Housekeeping, Unique to stress (Disease) and Unique to control (Normal) hub genes. |
| Authors: | Samarendra Das <[email protected]> and Baidya Nath Mandal <[email protected]> |
| Maintainer: | Samarendra Das <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1 |
| Built: | 2025-02-11 02:55:38 UTC |
| Source: | https://github.com/cran/dhga |
The function computes the adjacency matrix using the soft threshold parameter to construct a Gene Co-expression Network
Adjacency(x, beta, threshold)Adjacency(x, beta, threshold)
x |
x is a data frame of gene expression values where rows represent genes and columns represent samples/time points under a particular condition/stress/trait. |
beta |
beta is a soft threshold parameter determined from the scale free property of biological networks (Gene Co-expression Network). |
threshold |
threshold is a scalar representing the threshold value for the adjacency matrix to construct the Gene Co-expression Network. |
The function returns a list of outputs like adjacency matrix, Node-list and Edge-list for constructing a Gene Co-expression Network at certain threshold level
Samarendra Das and Baidya Nath Mandal
data(rice_normal) beta=6 threshold=0.4 Adjacency(rice_normal,6,0.4)data(rice_normal) beta=6 threshold=0.4 Adjacency(rice_normal,6,0.4)
The function returns hub status of each gene in a gene co-expression network under two contrasting conditions
DiffHub(x,y,m1,m2,s,beta,alpha, plot=TRUE)DiffHub(x,y,m1,m2,s,beta,alpha, plot=TRUE)
x |
x is a data frame of gene expression values where rows represent genes and columns represent samples/time point under stress condition. |
y |
y is a data frame of gene expression values where rows represent genes and columns represent samples/time points under control condition. |
m1 |
m1 is a scalar representing sample size and is less than or equal to number of columns in x. |
m2 |
m2 is a scalar representing sample size and is less than or equal to number of columns in y. |
s |
s is a scalar representing number of times each of the m samples will be resampled. |
beta |
beta is a soft threshold parameter determined from the scale free property of biological networks (GCN). |
alpha |
alpha is a scalar representing statistical level of significance. Default is alpha=0.0001 |
plot |
plot is a character representing whether the hubplot can be drawn (TRUE) or not (FALSE). |
The function returns a list with two components. First component returns the list of genes along with their hub status. Second component gives a table containing number of genes under different categories of hubs viz. housekeeping, unique to stress and unique to normal. The function also returns a venn plot of hub genes and unique hub genes under two conditions.
Samarendra Das and Baidya Nath Mandal
data(rice_salt) data(rice_normal) DiffHub(rice_salt,rice_normal,18,18,80, 6, alpha=0.0001, plot=TRUE)data(rice_salt) data(rice_normal) DiffHub(rice_salt,rice_normal,18,18,80, 6, alpha=0.0001, plot=TRUE)
The function returns list of top ranked hub genes under a particular condition/trait
hub.pval.cutoff(x, beta, m, s, n)hub.pval.cutoff(x, beta, m, s, n)
x |
x is a data frame of gene expression values where rows represent genes and columns represent samples/time point under a particular condition/trait. |
beta |
beta is a soft threshold parameter determined from the scale free property of biological networks (Gene Co-expression Network). |
m |
m is a scalar representing sample size and is less than or equal to number of columns in x. |
s |
s is a scalar representing number of times each of the m samples will be resampled. |
n |
n is a scalar representing number of top ranked hub genes to be selected under that particular condition/trait |
The function returns a vector containing list of top ranked hub genes for that particular condition/trait
Samarendra Das and Baidya Nath Mandal
data(rice_salt) x=as.data.frame(rice_salt) beta=6 m=18 s=80 n=20 hub.pval.cutoff(x, beta, m, s, n)data(rice_salt) x=as.data.frame(rice_salt) beta=6 m=18 s=80 n=20 hub.pval.cutoff(x, beta, m, s, n)
The function returns the list of hub genes in a Gene Co-expression Network based on Weighted Gene Score without considering statistical significance values.
hub.wgs(x, beta, n)hub.wgs(x, beta, n)
x |
x is a data frame of gene expression values where rows represent genes and columns represent samples/time. |
beta |
beta is a soft threshold parameter determined from the scale free property of biological networks (Gene Co-expression Network). |
n |
n is a scalar representing number of genes to be declared as hub in the Gene Co-expression Network based on Weighted Gene Score. |
The function returns a list of hub genes in the Gene Co-expression Network
Samarendra Das and Baidya Nath Mandal
data(rice_salt) hub.wgs(rice_salt,beta=6, n=20)data(rice_salt) hub.wgs(rice_salt,beta=6, n=20)
The function produces a venn diagram of number of hub genes and unique hub genes under two conditions
HubPlot(pvalue.stress, pvalue.control, alpha)HubPlot(pvalue.stress, pvalue.control, alpha)
pvalue.stress |
pvalue.stress is a vector of p-values for genes in the GCN under stress condition. pvalue.stress can be obtained from the output of hub.stress function. |
pvalue.control |
pvalue.control is a vector of p-values for genes in the GCN under normal or control condition. pvalue.control can be obtained from the output of hub.control function. |
alpha |
alpha is a scalar representing statistical level of significance. Default is alpha=0.0001 |
The function produces a venn plot of hub genes and unique hub genes under two conditions
Samarendra Das and Baidya Nath Mandal
data(rice_salt) pval.stres <- pvalue.hub(rice_salt, beta=6, m=18, s=80, plot=FALSE) pvalue.stress <- pval.stres[, 2] data(rice_normal) pval.control <- pvalue.hub(rice_normal, beta=6, m=18, s=80, plot=FALSE) pvalue.control <- pval.control[, 2] HubPlot(pvalue.stress, pvalue.control, alpha=0.0001)data(rice_salt) pval.stres <- pvalue.hub(rice_salt, beta=6, m=18, s=80, plot=FALSE) pvalue.stress <- pval.stres[, 2] data(rice_normal) pval.control <- pvalue.hub(rice_normal, beta=6, m=18, s=80, plot=FALSE) pvalue.control <- pval.control[, 2] HubPlot(pvalue.stress, pvalue.control, alpha=0.0001)
The function returns hub status of each gene in a gene co-expression network (GCN)
HubStatus(pvalue.stress, pvalue.control, alpha)HubStatus(pvalue.stress, pvalue.control, alpha)
pvalue.stress |
pvalue.stress is a vector of p-values for genes in the GCN under stress condition. pvalue.stress can be obtained from the output of hub.stress function. |
pvalue.control |
pvalue.control is a vector of p-values for genes in the GCN under normal or control condition. pvalue.control can be obtained from the output of hub.control function. |
alpha |
alpha is a scalar representing statistical level of significance. Default is alpha=0.0001 |
The function returns a list with two components. First component returns the list of genes along with their hub status. Second component gives a table containing number of genes under different categories of hubs viz. housekeeping, unique to stress and unique to normal
Samarendra Das and Baidya Nath Mandal
data(rice_salt) pval.stres <- pvalue.hub(rice_salt, beta=6, m=18, s=80, plot=FALSE) p1 <- pval.stres[, 2] data(rice_normal) pval.control <- pvalue.hub(rice_normal, beta=6, m=18, s=80, plot=FALSE) p2 <- pval.control[, 2] HubStatus(p1,p2,alpha=0.0001)data(rice_salt) pval.stres <- pvalue.hub(rice_salt, beta=6, m=18, s=80, plot=FALSE) p1 <- pval.stres[, 2] data(rice_normal) pval.control <- pvalue.hub(rice_normal, beta=6, m=18, s=80, plot=FALSE) p2 <- pval.control[, 2] HubStatus(p1,p2,alpha=0.0001)
The function computes ths statisical significance values of gene connections in a GCN for a particular condition/trait/stress
pvalue.hub(x, beta, m, s, plot=TRUE)pvalue.hub(x, beta, m, s, plot=TRUE)
x |
x is a data frame of gene expression values where rows represent genes and columns represent samples/time point under a particular condition/trait/stress. |
beta |
beta is a soft threshold parameter determined from the scale free property of biological networks (Gene Co-expression network). |
m |
m is a scalar representing sample size and is less than or equal to number of columns in x. |
s |
s is a scalar representing number of times each of the m samples will be resampled. |
plot |
plot is a character must either take logical value TRUE/FALSE representing whether the plot of the pvalues of gene connections of all genes in Gene Co-expression Network needs to be constructed or not. |
The function returns a vector of p-values for all the genes in the GCN
Samarendra Das and Baidya Nath Mandal
data(rice_salt) x=as.data.frame(rice_salt) beta=6 m=18 s=80 pvalue.hub(x, beta, m, s, plot=TRUE)data(rice_salt) x=as.data.frame(rice_salt) beta=6 m=18 s=80 pvalue.hub(x, beta, m, s, plot=TRUE)
rice_normal data has the gene expression values of 200 genes over 20 samples. The row names of the data are the names of the probes.
data(rice_normal)data(rice_normal)
A data frame with 200 rows and 20 columns.
rice_normal data has the gene expression values of 200 genes identified by Wang et al. (2013). After loading the data, the dataset is available in R in the name of rice_normal.
Wang J, Chen L, Wang Y, Zhang J, Liang Y, Xu D (2013) A Computational systems biology study for understanding salt tolerance mechanism in Rice. PLoS one 8(6): e64929. doi:10.1371/journal.pone.0064929.
data(rice_normal)data(rice_normal)
rice_salt data has the gene expression values of 200 genes over 20 samples. The row names of the data are the names of the probes.
data("rice_salt")data("rice_salt")
A data frame with 200 rows and 20 columns.
rice_salt data has the gene expression values of 200 genes identified by Wang et al. (2013). After loading the data, the dataset is available in R in the name of rice_salt object.
Wang J, Chen L, Wang Y, Zhang J, Liang Y, Xu D (2013) A Computational systems biology study for understanding salt tolerance mechanism in Rice. PLoS one 8(6): e64929. doi:10.1371/journal.pone.0064929.
data(rice_salt)data(rice_salt)
The function computes the weighted gene scores of all genes in a Gene Co-expression Network
WeightedGeneScore(x, beta, plot=TRUE)WeightedGeneScore(x, beta, plot=TRUE)
x |
x is a data frame of gene expression values where rows represent genes and columns represent samples/time points under a particular condition/stress/trait. |
beta |
beta is a soft threshold parameter determined from the scale free property of biological networks (Gene Co-expression Network). |
plot |
plot is a character must either take logical value TRUE/FALSE representing whether the plot of the gene connection degrees of all genes in Gene Co-expression Network needs to be constructed or not. |
The function returns a vector of weighted gene scores of genes (nodes) in the Gene Co-expression Network along with the gene connectivity plot.
Samarendra Das and Baidya Nath Mandal
data(rice_normal) beta=6 WeightedGeneScore(rice_normal, beta, plot=FALSE)data(rice_normal) beta=6 WeightedGeneScore(rice_normal, beta, plot=FALSE)