pheatmap

I have data-set with 90 observations(rows) across 20 columns. I have generated a pretty neat heatmap which clusters my data in two groups with the package pheatmap. Although its not entirely clean but the two clusters of dendrogram pretty much separates my samples in 2 distinct groups as per my conditions. Now I want to reduce this set of 90 to a stricter set around 20-30 obeservations but still want to preserve the same clustering order as shown in pheatmap . Is there a way to do that? or any other package that reduces my observations to a minimum set which can still preserve by clustering

I am very new to R and recently I have been playing around with the pheatmap library to generate, well, heatmaps. My problem is that I want to color my heatmap in a specific way. I'll describe below: Values < 1 should be a color ramp (e.g. dark blue to light blue) A value exactly equal to 1 should be dark grey Values > 1 should be a color ramp (e.g. dark red to light red) I have played around with the breaks parameter and the color parameter with various palettes but I can't seem to nail a good solution. the closest I've come is something like this: pheatmap(mtx, color = c('#4444FF','#F4FF4F',

I have used single cell RNA-seq data stored in a single cell experiment assay to perform hierarchical clustering of ~ 11.3000 expressed genes (not clustering samples) using pheatmap in R to show the heterogeneity of the data. I need the output image to be 2 x 4 inches, which makes the dendrogram a blur. Is it possible to reduce the line width used for the dendrogram? I tried setting the line width by gpar, but it doesn't seem to change sessionInfo() R version 3.4.1 (2017-06-30) Platform: i386-w64-mingw32/i386 (32-bit) Running under: Windows >= 8 x64 (build 9200) pheatmap_1.0.8 R code gpar(lwd

Reproducible Data: data(crabs, package = "MASS") df <- crabs[-(1:3)] set.seed(12345) df$GRP <- kmeans(df, 4)$cluster df.order <- dplyr::arrange(df, GRP) Data Description: df has 5 numerical variables. I did the K-means algorithm according to these 5 attributes and produced a new categorical variable GRP which has 4 levels. Next, I ordered it with GRP and named it df.order . What I did with pheatmap : ## 5 numerical variables for coloring colormat <- df.order[c("FL", "RW", "CL", "CW", "BD")] ## Specify the annotation variable `GRP` shown on left side of the heatmap ann_row <- df.order["GRP"] ##

Using R package pheatmap to draw heatmaps. Is there a way to assign a color to NAs in the input matrix? It seems NA gets colored in white by default. E.g.: library(pheatmap) m<- matrix(c(1:100), nrow= 10) m[1,1]<- NA m[10,10]<- NA pheatmap(m, cluster_rows=FALSE, cluster_cols=FALSE) Thanks nico It is possible, but requires some hacking. First of all let's see how pheatmap draws a heatmap. You can check that just by typing pheatmap in the console and scrolling through the output, or alternatively using edit(pheatmap) . You will find that colours are mapped using mat = scale_colours(mat, col =

I'm using pheatmap with large data. My purpose is to clusterize rows and columns and to analyze main clusters. I upload the data table and perform the heatmap as follows: library (pheatmap) data<-read.table ("example.txt", header = TRUE) pheatmap(data) By this I get the heatmap of my data. My example.txt look like this: a b c d e f a 1 0.1 0.9 0.5 0.65 0.9 b 0.1 1 0.39 0.83 0.47 0.63 c 0.9 0.39 1 0.42 0.56 0.84 d 0.5 0.83 0.42 1 0.95 0.43 e 0.65 0.47 0.56 0.95 1 0.14 f 0.9 0.63 0.84 0.43 0.14 1 May be this is a very stupid question, but anyway I'll post it. After running pheatmap(data), how

I know how to group rows (genes) by annotation category using pheatmap, and I know how to perform Person's correlation clustering on the whole set of rows (genes), but what I would like to accomplish would be to perform clustering (and show independent dendrograms) on each category independently. Is that even possible? Or am I forced to create a separate heat map for each category to do the clustering on a category basis? Check my MWE below: set.seed(1) library(pheatmap) mymat <- matrix(rexp(600, rate=.1), ncol=12) colnames(mymat) <- c(rep("treatment_1", 3), rep("treatment_2", 3), rep(