Entering edit mode
6.8 years ago
Mehmet
▴
820
Dear All,
I want to add FPKM values into cells of heatmap using complex heatmap package. I made a heatmap, I tried to use:
cell_fun = function(j, i, x, y, width, height, fill) {grid.text(sprintf("%.1f", df[i, j]), x, y, gp = gpar(fontsize = 10))},
df= my data matrix having FPKM values of each gene in each sample.
Some values are seen in cell, while others are different from df file.
I already figured out.
For the solution:
the data frame that has FPKM values of each gene must have only FPKM values of each gene in each sample/condition. Previously, I kept gene name in the data frame, but order of FPKM values in cells of heatmap was different that the data frame.
Great, can you paste the solution for everyone else? I, only now, just saw your request in the other question (I was traveling today)
Hi Kevin,
I would like to have your opinion about differential gene expression analysis workflow;
I have ~50 genes and have RNA reads obtained from four tissues. I want to see expression differences of those genes in the four tissues. I am planning to perform the steps after quality control: 1. Alignment of each RNA reads to reference genome separately. This will produce 4 .bam files. 2. Read counting of four .bam files. in this step, should I combine all bam files or should I do read counting for each .bam files separately? 3. Normalization. Which method do you recommend? 4. DEG analysis; edgeR or DESeq.
is my workflow suitable?
Hello Mehmet, which read counting method do you plan to use? For normalisation, my preference is DESeq2.
Your sample size is just 4?, that is, 1 sample per tissue?