**R – Modern Data**, and kindly contributed to R-bloggers)

In every statistical analysis, the first thing one should do is try and visualise the data before any modeling. In microarray studies, a common visualisation is a heatmap of gene expression data.

In this post I simulate some gene expression data and visualise it using the `heatmaply`

package in `R`

by Tal Galili. This package extends the plotly engine to heatmaps, allowing you to inspect certain values of the data matrix by hovering the mouse over a cell. You can also zoom into a region of the heatmap by drawing a rectangle over an area of your choice

The following function simulates data from a multivariate normal distribution, and allows exposure dependent correlations between the data. You will need the `mvrnorm`

function from the MASS library to run this function:

simulateExprData <- function(n, n0, p, rho0, rho1){ # n: total number of subjects # n0: number of subjects with exposure 0 # n1: number of subjects with exposure 1 # p: number of genes # rho0: rho between Z_i and Z_j for subjects with exposure 0 # rho1: rho between Z_i and Z_j for subjects with exposure 1 # Simulate gene expression values according to exposure 0 or 1, # according to a centered multivariate normal distribution with # covariance between Z_i and Z_j being rho^|i-j| n1 <- n - n0 times <- 1:p H <- abs(outer(times, times, "-")) V0 <- rho0^H V1 <- rho1^H # rows are people, columns are genes genes0 <- MASS::mvrnorm(n = n0, mu = rep(0,p), Sigma = V0) genes1 <- MASS::mvrnorm(n = n1, mu = rep(0,p), Sigma = V1) genes <- rbind(genes0,genes1) return(genes) } genes <- simulateExprData(n = 100, n0 = 50, p = 100, rho0 = 0.01, rho1 = 0.95)

Next we need to install and load the `heatmaply`

package:

install.packages('devtools') devtools::install_github('talgalili/heatmaply') library(heatmaply)

The syntax is extremely simple. Lets plot a few different interactive heatmaps of the data. We first plot the `genes`

and specify how many groups we want for the rows (subjects) and columns (genes) of the data. In this case we specify 2 groups for both the rows and columns:

heatmaply(genes, k_row = 2, k_col = 2)

We see that the clustering algorithm is not able to properly cluster the subjects because we would have expected to see two equal size groups in the dendrogram on the y-axis. Let’s instead plot the correlation matrix of the genes:

heatmaply(cor(genes), k_row = 2, k_col = 2)

We now see two distinct groups in the dendrogram, as we would expect because that’s the way we generated the data

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