plotGLM {MORE} | R Documentation |
MORE includes the possibility of studying graphically the relationship of genes and regulators: given a specific gene and regulator, study the regulators for a given gene, analyze which genes are regulated by a specific regulator...
plotGLM(GLMoutput, gene, regulator = NULL, reguValues = NULL, plotPerOmic = FALSE, gene.col = 1, regu.col = NULL, order = TRUE, xlab = "", cont.var = NULL, cond2plot = NULL,...)
GLMoutput |
It is the name of the object where we have saved the execution of the function |
gene |
ID of the gene to be plotted. |
regulator |
ID of the regulator to be plotted. If NULL (default value), all the regulators of the gene are plotted. |
reguValues |
Vector containing the values of a regulator that the user can optionally provide. If NULL (default value), these values are taken from |
plotPerOmic |
If TRUE, all the significant regulators of the given gene and the same omic are plotted in the same graph. If FALSE (default value), each regulator is plotted in a separate plot. |
gene.col |
Color to plot the gene. By default, 1 (black). |
regu.col |
Color to plot the regulator. If NULL (default), a color will be assigned by the function, that will be different for each regulatory omic. |
order |
If TRUE (default), the values in X axis are ordered. |
xlab |
Label for X axis. |
cont.var |
Vector with length equal to the number of observations in data, which optionally may contain the values of the numerical variable (e.g. time) to be plotted in X axis. By default, NULL. |
cond2plot |
Vector or factor indicating the experimental group of each value to represent. If NULL (default), the labels are taken from the experimental design matrix. |
Sonia Tarazona, Blanca Tomas Riquelme
data(TestData) require(MASS); require(igraph); require(car); require(glmnet); require(psych) ## Omic type OmicType = c(1, 0, 0) names(OmicType) = names(TestData$data.omics) ## GetGLM function SimGLM = GetGLM(GeneExpression = TestData$GeneExpressionDE, associations = TestData$associations, data.omics = TestData$data.omics, edesign = TestData$edesign[,-1, drop = FALSE], Res.df = 10, epsilon = 0.00001, alfa = 0.05, MT.adjust = "fdr", family = negative.binomial(theta = 10), elasticnet = 0.7, stepwise = "two.ways.backward", interactions.reg = TRUE, correlation = 0.8, min.variation = 0, min.obs = 10, omic.type = OmicType) ## Given a gene and regulator plotGLM(GLMoutput = SimGLM, gene = "ENSMUSG00000012535", regulator = "6_29609160_29609425", plotPerOmic = FALSE, gene.col = "red4", regu.col = "green4") ## Given a gene par(mfrow = c(2,2)) plotGLM(GLMoutput = SimGLM, gene = "ENSMUSG00000012535", regulator = NULL, plotPerOmic = FALSE, gene.col = "blue4") ## Given a regulator plotGLM(GLMoutput = SimGLM, gene = NULL, regulator = "mmu-miR-15a-3p", plotPerOmic = FALSE, gene.col = "red4", regu.col = "green4") ## With cont.var parameter plotGLM(GLMoutput = SimGLM, gene = "ENSMUSG00000026563", regulator = "Sp100", plotPerOmic = FALSE, gene.col = "darkblue", regu.col = "green4", cont.var = c(1,2,3,4,5,6,7,8), xlab = "CondA | CondB")