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Lots of people also do grid plots of Pearson/Spearman correlation, sometimes with a hierarchically clustered dendrogram on the axes. Also easy to do in R with the cor() function once you have a matrix of genes by samples. -
You can also just do a scatter plot, for example in R, of Rep1 vs Rep2. This can be a bit easier to understand when communicating your results to other researchers.Leave a comment:
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Hi- A plot of principal components (PCA plot) and/or multidimensional scaling (MDS plot) and/or a dendrogram are usually quite helpful in spotting sample outliers and to check that the replicates cluster together. Once you have a matrix of genes and samples they are quite easy to generate in e.g. R. See also the vignettes in edgeR and DESeq (R/bioconductor packages).Originally posted by jake13 View Post
DarioLeave a comment:
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Check that replicates are good
Hi,
I'm new to RNA-seq analysis. I have biological duplicates of 4 different conditions. I want to check how good the replicates are for each condition. I have got them mapped to the genome and have run cuffdiff on the files, but I was wondering what comparisons people do/tools people use to check their replicates?
Thanks
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Genomics studies in neuroscience face a special challenge due to the brain’s complexity and scarcity of samples. Mapping changes in cell type and state using conventional next-generation sequencing methods remains challenging. Advances in technologies like single-cell sequencing, spatial transcriptomics, and long-read sequencing have opened the door to deeper studies of the brain and diseases like Alzheimer’s, amyotrophic lateral sclerosis (ALS), and schizophrenia.
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Cancer survival rates have significantly increased in the last few decades in the United States, reaching a combined 70% 5-year survival rate by 2021. Behind this number, there are years of research to find new therapies, drug targets, and early detection methods. But there is one core challenge that keeps slowing down these advances, and it’s about drug resistance.
There is no single reason why many patients don’t respond to treatment as expected. Cancer is...Yesterday, 05:17 AM -
Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.07-01-2026, 11:43 AM
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