| Type: | Package |
| Title: | Ensemble Gene Regulatory Network Inference |
| Version: | 0.1.6 |
| Author: | Chiranjib Sarkar 
[aut, cre, ctb],
Dipayan Sarkar
[aut],
Rajender Parsad [aut],
Dwijesh Mishra
[aut] |
| Maintainer: | Chiranjib Sarkar <cschiranjib9@gmail.com> |
| Description: | Gene regulatory network constructed using combined score obtained from individual network inference method. The combined score measures the significance of edges in the ensemble network. Fisher's weighted method has been implemented to combine the outcomes of different methods based on the probability values. The combined score follows chi-square distribution with 2n degrees of freedom. <doi:10.22271/09746315.2020.v16.i3.1358>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.1 |
| Imports: | fdrtool, gdata, MASS, readr, stats |
| Depends: | R (≥ 3.5.0) |
| Suggests: | testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2022-11-17 16:41:22 UTC; 91701 |
| Repository: | CRAN |
| Date/Publication: | 2022-11-18 10:00:09 UTC |
Correlation based network
Description
Correlation based network
Usage
CRN(x)
Arguments
x |
microarray dataset with genes in columns and samples in rows. |
Value
s matrix containing connectivity scores
Examples
library(EGRNi)
data(gene_exp)
t_geneexp<-t(gene_exp)
CRN(t_geneexp)
Ensemble Gene Regulatory Network Inference
Description
EGRN computes F-score from probability values obtained individual method for each edge. The F-score follows ch-square distribution with 2k degrees of freedom, where k is the number of individual methods consider for ensemble study.The EGRN combines the outcomes obtained from four methods i.e. correlation, principal component regression, partial least square regression and ridge regression.The function EGRN has been implemented using Fisher's weighted method.
Usage
EGRN(x, n, w)
Arguments
x |
Matrix containing gene expression data with genes in row and samples in column |
n |
Number of Bootstrap samples to obtain estimate of mean connectivity score and mean square error |
w |
Matrix containing weight for all individual methods |
Details
The function works step-by-step as follows:The input gene expression data is considered for withdrawing n number of bootstrap samples to obtain the estimate of pairwise connectivity score for all possible pairs of genes in the dataset. The t-test statistic is calculated for each pair of genes and performed probability value and false discovery rate calculation from mixture distribution. The p-values for each edge are further used for computing F-score using fisher's weighted method. The fisher's weighted method provides the F-score which follows chi-square distribution with degrees of freedom twice the number of individual methods considered for ensemble study. The EGRN provides the network file as output containing the interacting pair of genes in row with final score.
Value
Fw_sum matrix containing F score for significant gene pairs
Author(s)
Chiranjib Sarkar(cschiranjib9@gmail.com)
References
Sarkar, C., Parsad, R., Mishra, D.C. and Rai, A. (2020). An ensemble approach for gene regulatory network study in rice blast. Journal of Crop and Weed , 16 , 1-8.
Examples
#load "EGRNi" library and gene expression data
library(EGRNi)
data(gene_exp)
data(weight)
EGRN(gene_exp[1:50,], 2, weight)
To compute F score from edge scores
Description
Edg2Fw computes the F-score from edge score using Fisher's weighted method. One pair of genes with k numbers of edge scores obtained from k numbers of independent method are combined using the probability value. The weight matrix w contains the weight for k number of methods.
Usage
Edg2Fw(s, w, k)
Arguments
s |
Matrix containing edge scores obtained from k numbers of methods with gene pairs in row and edge scores in column |
w |
Matrix containing weight for all individual methods |
k |
Numbers of independent methods considered for computing edge scores |
Details
Edg2Fw function generates mixture distribution based on edge score for each method given column wise in s matrix. The probability value for each pair of gene obtained from mixture distribution are combined using Fisher's weighted method. The combined score Fw follows chi-square distribution with 2k degrees of freedom.
Value
Fw_sum matrix containing F score for significant gene pairs
Author(s)
Chiranjib Sarkar(cschiranjib9@gmail.com)
References
Sarkar, C., Parsad, R., Mishra, D.C. and Rai, A. (2020). An ensemble approach for gene regulatory network study in rice blast. Journal of Crop and Weed , 16 , 1-8.
Examples
#load "EGRNi" library and Edge score data
library(EGRNi)
data(Edgescore)
data(weight)
Edg2Fw(Edgescore, weight, 4)
Edge score obtained from 4 different methods for Ensemble Gene Regulatory Network Inference
Description
Edge score obtained from 4 different methods for Ensemble Gene Regulatory Network Inference
Usage
Edgescore(path)
Arguments
path |
path to file name |
Value
a tibble
Author(s)
Chiranjib Sarkar(cschiranjib9@gmail.com)
References
Sarkar, C., Parsad, R., Mishra, D.C. and Rai, A (2020). An ensemble approach for gene regulatory network study in rice blast. Journal of Crop and Weed , 16 , 1-8.
To compute F score using probability value
Description
F_score computes the combined edge score from multiple probability values of edges obtained from independent methods. The probability score follows uniform distribution [0,1].The F score follows chi-square distribution with 2k degrees of freedom.
Usage
F_score(p, w, k)
Arguments
p |
Matrix containing probability values of edges for each method column wise having gene pairs in row |
w |
Matrix containing weight for all individual methods |
k |
Numbers of independent methods considered for computing edge scores |
Details
F_score function generates mixture distribution based on probability value for each method given column wise in p matrix. The probability value for each pair of gene are combined using Fisher's weighted method. The combined score Fw follows chi-square distribution with 2k degrees of freedom. The F_score provides the network file as output containing the interacting pair of genes in row with final score.
Value
Fw_sum matrix containing F score for significant gene pairs
Author(s)
Chiranjib Sarkar(cschiranjib9@gmail.com)
References
Sarkar, C., Parsad, R., Mishra, D.C. and Rai, A. (2020). An ensemble approach for gene regulatory network study in rice blast. Journal of Crop and Weed , 16 , 1-8.
Examples
#load "EGRNi" library and probability value data
library(EGRNi)
data(pvalue)
data(weight)
F_score(pvalue, weight, 4)
To compute F score for significant edges from individual methods
Description
IntsctEdg2Fw computes the Fw score using Fisher's weighted method for the significant edges obtained in k numbers of individual methods. The probability values are combined to compute the Fw score which follows chi-square distribution. The significant edges (>fdr) are selected using intersection.
Usage
IntsctEdg2Fw(s, w, k, fdr)
Arguments
s |
Matrix containing edge scores obtained from k numbers of methods with gene pairs in row and edge scores in column |
w |
Matrix containing weight for all individual methods |
k |
Numbers of independent methods considered for computing edge scores |
fdr |
Cut-off for selecting significant edges |
Details
IntsctEdg2Fw function generates mixture distribution based on edge score for each method given column wise in s matrix. The probability value for each pair of gene obtained from mixture distribution are combined using Fisher's weighted method. The combined score Fw follows chi-square distribution with 2k degrees of freedom.
Value
Fw matrix containing F score for significant gene pairs
Author(s)
Chiranjib Sarkar(cschiranjib9@gmail.com)
References
Sarkar, C., Parsad, R., Mishra, D.C. and Rai, A. (2020). An ensemble approach for gene regulatory network study in rice blast. Journal of Crop and Weed , 16 , 1-8.
Examples
#load "EGRNi" library and Edge score data
library(EGRNi)
data(Edgescore)
data(weight)
IntsctEdg2Fw(Edgescore,weight, k=4, fdr=0.1)
Principal component regression based network
Description
Principal component regression based network
Usage
PCN(x)
Arguments
x |
microarray dataset with genes in columns and samples in rows. |
Value
s matrix containing connectivity scores
Author(s)
Chiranjib Sarkar(cschiranjib9@gmail.com)
References
Sarkar, C., Parsad, R., Mishra, D.C. and Rai, A (2020). An ensemble approach for gene regulatory network study in rice blast. Journal of Crop and Weed , 16 , 1-8.
Examples
library(EGRNi)
data(gene_exp)
t_geneexp<-t(gene_exp)
PCN(t_geneexp)
Partial least square based network
Description
Partial least square based network
Usage
PLSN(x)
Arguments
x |
microarray dataset with genes in columns and samples in rows. |
Value
s matrix containing connectivity scores
Examples
library(EGRNi)
data(gene_exp)
t_geneexp<-t(gene_exp)
PLSN(t_geneexp)
Ridge regression based network
Description
Ridge regression based network
Usage
RidgN(x)
Arguments
x |
microarray dataset with genes in columns and samples in rows. |
Value
s matrix containing connectivity scores
Examples
library(EGRNi)
data(gene_exp)
t_geneexp<-t(gene_exp)
RidgN(t_geneexp)
Gene expression data for Ensemble Gene Regulatory Network Inference
Description
Gene expression data for Ensemble Gene Regulatory Network Inference
Usage
gene_exp(path)
Arguments
path |
path to file name |
Value
a tibble
Author(s)
Chiranjib Sarkar(cschiranjib9@gmail.com)
References
Sarkar, C., Parsad, R., Mishra, D.C. and Rai, A (2020). An ensemble approach for gene regulatory network study in rice blast. Journal of Crop and Weed , 16 , 1-8.
Probability values for Ensemble Gene Regulatory Network Inference
Description
Probability values for Ensemble Gene Regulatory Network Inference
Usage
pvalue(path)
Arguments
path |
path to file name |
Value
a tibble
Author(s)
Chiranjib Sarkar(cschiranjib9@gmail.com)
References
Sarkar, C., Parsad, R., Mishra, D.C. and Rai, A (2020). An ensemble approach for gene regulatory network study in rice blast. Journal of Crop and Weed , 16 , 1-8.
Weights for Ensemble Gene Regulatory Network Inference
Description
Weights for Ensemble Gene Regulatory Network Inference
Usage
weight(path)
Arguments
path |
path to file name |
Value
a tibble
Author(s)
Chiranjib Sarkar(cschiranjib9@gmail.com)
References
Sarkar, C., Parsad, R., Mishra, D.C. and Rai, A (2020). An ensemble approach for gene regulatory network study in rice blast. Journal of Crop and Weed , 16 , 1-8.