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11

ComplexHeatmap is built for plotting side-by-side heat maps with the same clustering - you use the + notation, similar to ggplot2. To use the same example data as @b.nota: library(ComplexHeatmap) # First matrix set.seed(2) m <- replicate(8, rnorm(26)) rownames(m) <- letters[1:26] # Second matrix set.seed(3) m2 <- replicate(8, rnorm(26)) rownames(m2)...


6

You expect that the order of genes is the same for both heatmaps, but the chance is small that the 4000 genes will exactly be clustered in the same order. What you can do, is use the order of genes (rows) from one heatmap and create a new heatmap with the other data set. You'll lose the tree of course because it will not be clustering anymore. Here a small ...


6

While it was easy to get your data after your edit, the data was not quite useful, the second data frame/matrix has 2 columns only! I had to create a fake data set, that looks like this: my_df <- data.frame( gene_class = rep(letters[1:4],3), some_value = rep(1:3*10,4) ) rownames(my_df) <- paste("Gene-",rep(LETTERS[1:4],each = 3),1:4,sep="") > ...


5

It is indeed true that for the DE analysis one should include batch into the formula to avoid changing the original counts. Still, for everything else such as plotting heatmaps use of removeBatchEffects is perfectly fine and (at least to me) a standard and well-accepted procedure. It essentially does not matter what you use to correct for the batch effect ...


5

@StupidWolf answered me on stackoverflow and he's answer solved my question. I will share it here as well. @StupidWolf: We read in the alignment: library(Biostrings) library(ggmsa) protein_sequences <- system.file("extdata", "sample.fasta", package = "ggmsa") aln = readAAMultipleAlignment(protein_sequences) ggmsa(protein_sequences, start = 265, end = ...


4

It is possible with annotation_colors argument : First create a list with your conditions and then add the argument : annot_colors=list(HealthStatus=c(Cancer="#F0978D",Healthy="#63D7DE")) pheatmap(heat1, annotation_col=df, color=colorRampPalette(c("navy", "white", "red"))(50), ...


3

In case it is still helpful as the post is rather old, the code below would generate a heatmap with annotations thanks to the ComplexHeatmap package. But before that I would like to stress out the importance of example data, I bet a lot of experts could not help you just because of they did not have single cell data at their disposal. I am using some scRNA-...


2

You have 2 options provided that both datasets have the same genes (rows): Keep the original order of rows in the heatmap. If they’re in the same order in the dataset then they will be in the heatmap. You can suppress reordering with any of the following arguments to heatmap.2 Rowv = NULL dendrogram = “col” or dendrogram = “none” reorderfun = function(x) ...


2

There's no need to normalize them, you're not comparing them. Just use them as they are.


2

I've used some dummy data for simplicity. It's fairly easy to do this with ggplot, but you have to format your matrix-like data for ggplot input. eg : # make data set.seed(1) ngenes <- 20 nsamps <- 10 my_dat <- matrix(rnorm(ngenes*nsamps), ncol = nsamps, nrow = ngenes) colnames(my_dat) <- paste("samp", seq_len(nsamps)) rownames(my_dat) <- ...


2

As others already pointed out, there is no official heatmap color scheme. I am not sure there is an official color scheme for any other types of plots either. However, heatmaps seem to be far more standardized. Traditionally, heatmaps have been red-black-green as you originally stated. Most heatmaps from the microarray era and early RNA-seq are like that. ...


2

Looking at the source for pheatmap, there is a function called scale_mat that is used to preprocess and normalize the input matrix, depending on the value of scale, which specifies one of either none, row, or column normalization options. Separate and similar functions are used for color value scaling downstream. I have not watched the video or read the ...


2

For visualization of expression values it usually makes sense to standardize the data first. The problem with plain expression values is that they differ a lot between genes and as such highly-expressed genes dominate the color scale. As you see in your heatmap, the first and last rows barely show any difference because the color scale is dominated by a ...


1

Specify your genes of interest using the features parameter of the ScaleData() function. If this parameter is not specified, only "variable genes" are scaled and that makes sense, genes that do not demonstrate variability across conditions is not interesting in general. Anyhow, since the DoHeatmap() would be looking at the "scaled data slot&...


1

See the DoHeatmap function in Seurat, which seems to be what that paper has used: https://satijalab.org/seurat/articles/pbmc3k_tutorial.html#cb50 > DoHeatmap(pbmc, features = myGeneList, group.by="Condition")


1

I'm not sure exactly what was going on with your data, but I read it in as a raw data.frame using as.matrix(read.table(., row.names=1, header=T)). The issue was that you were reading it in without a header, so the col.names were being read in as part of the matrix. But apart from that, the rest of your command was fine: library(gplots) data = structure(c(-0....


1

What is fed into ComplexHeatmap is what it outputs! So if one of your genes has a Missense_Mutation as well as a Frame_Shift_Del and you would like to plot its value, of course you will have a category and color that is different than those of Missense_Mutation or Frame_Shift_Del, that is only natural. What you can do is to create additional variables like ...


1

Although there is no standard as DeepTools' Devon Ryan said, there are still some habits. Usually red means upregulated, as you can see in the default color scheme in MATLAB's clustergram function. But you also want to make sure color-blinded people can see the difference. So another color scheme might be better, such as red=high while blue=low. Anyway, if ...


1

You are almost there except for a few tricks: If you want columns to be ordered using the values in Cell 4, you will need to provide a custom column order either with the column_order argument of the Heatmap() function or by providing ordered data. Below is the code to perform the latter. Most of the code is yours, I just tweaked it a little. library(...


1

You need to order the marker matrix (e.g. by avg_logFC) before calling DoHeatMap. library(dplyr) all.markers <- FindAllMarkers(object = obj) top20 <- all.markers %>% group_by(cluster) %>% top_n(20, avg_logFC) DoHeatmap(object = obj, genes.use = top20$gene, slim.col.label = TRUE, remove.key = TRUE)


1

Turns out, this is not possible. Concatenating two heatmaps causes their legends to be grouped, and while they can be positioned together at the top, bottom, at the left or the right sides, they cannot be separated spatially. The discussion can be viewed at the bioconductor support link from the question. Relevant snippets: No, since you put the two ...


1

I am not sure if it's a v3 or also v2 thing, but have you tried by setting group.by and label? From ?Seurat::DoHeatmap: group.by A vector of variables to group cells by; pass 'ident' to group by cell identity classes label Label the cell identies above the color bar If you prefer external tools, you can use pheatmap and setting annotation_col e ...


1

I see that you created the ColAnn variable with the Condition annotation but did not include it in the draw call (or even in the Heatmap() function). Could that be the problem here? EDIT: Oops, just noticed that this post is really old. Doesn't have an answer, so I guess I'll let mine stay.


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