Package dhga. September 6, 2016
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1 Type Package Title Differential Hub Gene Analysis Version 0.1 Date Package dhga September 6, 2016 Author Samarendra Das and Baidya Nath Mandal Maintainer Samarendra Das Depends R (>= 3.3.1), VennDiagram Identification of hub genes in a gene co-epression network from gene epression 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. License GPL (>= 2) NeedsCompilation no Repository CRAN Date/Publication :12:54 R topics documented: Adjacency DiffHub hub.pval.cutoff hub.wgs HubPlot HubStatus pvalue.hub rice_normal rice_salt WeightedGeneScore Inde 11 1
2 2 Adjacency Adjacency Construction of Gene Co-epression Network The function computes the adjacency matri using the soft threshold parameter to construct a Gene Co-epression Network Adjacency(,, threshold) threshold is a data frame of gene epression values where rows represent genes and columns represent samples/time points under a particular condition/stress/trait. biological networks (Gene Co-epression Network). threshold is a scalar representing the threshold value for the adjacency matri to construct the Gene Co-epression Network. The function returns a list of outputs like adjacency matri, Node-list and Edge-list for constructing a Gene Co-epression Network at certain threshold level =6 threshold=0.4 Adjacency(rice_normal,6,0.4)
3 DiffHub 3 DiffHub Differential Hub status of the genes in a gene co-epression network The function returns hub status of each gene in a gene co-epression network under two contrasting conditions DiffHub(,y,m1,m2,s,,alpha, plot=true) is a data frame of gene epression values where rows represent genes and columns represent samples/time point under stress condition. y y is a data frame of gene epression 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. 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. biological networks (GCN). alpha alpha is a scalar representing statistical level of significance. Default is alpha= 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. DiffHub(rice_salt,rice_normal,18,18,80, 6, alpha=0.0001, plot=true)
4 4 hub.pval.cutoff hub.pval.cutoff Identification of hub genes in a Gene Co-epression Network based on gene connection significance values The function returns list of top ranked hub genes under a particular condition/trait hub.pval.cutoff(,, m, s, n) m s n is a data frame of gene epression values where rows represent genes and columns represent samples/time point under a particular condition/trait. biological networks (Gene Co-epression Network). m is a scalar representing sample size and is less than or equal to number of columns in. s is a scalar representing number of times each of the m samples will be resampled. 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 =as.data.frame(rice_salt) =6 m=18 s=80 n=20 hub.pval.cutoff(,, m, s, n)
5 hub.wgs 5 hub.wgs Identification of hub genes in a Gene Co-epression Network based on Weighted Gene Score The function returns the list of hub genes in a Gene Co-epression Network based on Weighted Gene Score without considering statistical significance values. hub.wgs(,, n) n is a data frame of gene epression values where rows represent genes and columns represent samples/time. biological networks (Gene Co-epression Network). n is a scalar representing number of genes to be declared as hub in the Gene Co-epression Network based on Weighted Gene Score. The function returns a list of hub genes in the Gene Co-epression Network hub.wgs(rice_salt,=6, n=20) HubPlot Venn plot of hub genes under two conditions namely stress and control The function produces a venn diagram of number of hub genes and unique hub genes under two conditions
6 6 HubStatus 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= The function produces a venn plot of hub genes and unique hub genes under two conditions pval.stres <- pvalue.hub(rice_salt, =6, m=18, s=80, plot=false) pvalue.stress <- pval.stres[, 2] pval.control <- pvalue.hub(rice_normal, =6, m=18, s=80, plot=false) pvalue.control <- pval.control[, 2] HubPlot(pvalue.stress, pvalue.control, alpha=0.0001) HubStatus Hub status of the genes in a gene co-epression network The function returns hub status of each gene in a gene co-epression network (GCN) HubStatus(pvalue.stress, pvalue.control, alpha)
7 pvalue.hub 7 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= 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 pval.stres <- pvalue.hub(rice_salt, =6, m=18, s=80, plot=false) p1 <- pval.stres[, 2] pval.control <- pvalue.hub(rice_normal, =6, m=18, s=80, plot=false) p2 <- pval.control[, 2] HubStatus(p1,p2,alpha=0.0001) pvalue.hub Compuation of gene connection sigificance values in a Gene Coepression network The function computes ths statisical significance values of gene connections in a GCN for a particular condition/trait/stress pvalue.hub(,, m, s, plot=true)
8 8 rice_normal m s plot is a data frame of gene epression values where rows represent genes and columns represent samples/time point under a particular condition/trait/stress. biological networks (Gene Co-epression network). m is a scalar representing sample size and is less than or equal to number of columns in. s is a scalar representing number of times each of the m samples will be resampled. 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 Coepression Network needs to be constructed or not. The function returns a vector of p-values for all the genes in the GCN =as.data.frame(rice_salt) =6 m=18 s=80 pvalue.hub(,, m, s, plot=true) rice_normal The gene epression data of rice under control or normal condition rice_normal data has the gene epression values of 200 genes over 20 samples. The row names of the data are the names of the probes. Format A data frame with 200 rows and 20 columns.
9 rice_salt 9 Details rice_normal data has the gene epression 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. Source 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): e doi: /journal.pone rice_salt The gene epression data of rice under salinity stress condition rice_salt data has the gene epression values of 200 genes over 20 samples. The row names of the data are the names of the probes. data("rice_salt") Format A data frame with 200 rows and 20 columns. Details rice_salt data has the gene epression 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. Source 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): e doi: /journal.pone
10 10 WeightedGeneScore WeightedGeneScore Computation of weighted gene score of genes in Gene Co-epression Network The function computes the weighted gene scores of all genes in a Gene Co-epression Network WeightedGeneScore(,, plot=true) plot is a data frame of gene epression values where rows represent genes and columns represent samples/time points under a particular condition/stress/trait. biological networks (Gene Co-epression Network). 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-epression Network needs to be constructed or not. The function returns a vector of weighted gene scores of genes (nodes) in the Gene Co-epression Network along with the gene connectivity plot. =6 WeightedGeneScore(rice_normal,, plot=false)
11 Inde Topic Adjacency Topic Edgelist Topic Nodelist Topic Weighted Gene Score Topic co-epression Topic datasets rice_normal, 8 rice_salt, 9 Topic gene Topic hub gene Topic hub status Topic level of significance Topic network Topic p value Topic plot Topic top rank Topic venn plot Topic weighted gene score rice_normal, 8 rice_salt, 9 11
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Type Package Package SPreFuGED July 29, 2016 Title Selecting a Predictive Function for a Given Gene Expression Data Version 1.0 Date 2016-07-27 Author Victor L Jong, Kit CB Roes & Marinus JC Eijkemans
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Type Package Package HDtest September 13, 2018 Title High Dimensional Hypothesis Testing for Mean Vectors, Covariance Matrices, and White Noise of Vector Time Series Version 2.1 Date 2018-9-10 Author Meng
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Package comparec February 19, 2015 Type Package Title Compare Two Correlated C Indices with Right-censored Survival Outcome Version 1.3.1 Date 2014-12-18 Author Le Kang, Weijie Chen Maintainer Le Kang
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Type Package Package Rarity December 23, 2016 Title Calculation of Rarity Indices for Species and Assemblages of Species Version 1.3-6 Date 2016-12-23 Author Boris Leroy Maintainer Boris Leroy
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Type Package Package LBLGXE July 20, 2015 Title Bayesian Lasso for detecting Rare (or Common) Haplotype Association and their interactions with Environmental Covariates Version 1.2 Date 2015-07-09 Author
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Type Package Title Box-Cox Power Transformation Version 2.3 Date 2017-11-10 Depends R (>= 2.15.0) Package AID November 10, 2017 Imports MASS, tseries, nortest, ggplot2, graphics, psych, stats Author Osman