How to estimate the inter sample and inter replicate correlation coefficient?

Question

[ATCG]

How to estimate the inter sample and inter replicate correlation coefficient? My dataset consists of 2 RNA-Seq samples treatment and control and each sample has 3 replicates. I would like to show that the replicates have a higher correlation coefficient than that of the samples( treatment and control). Thanks!

[ATpoint]

Multiple options. cor on the count matrix, or PCA, showing that replicates cluster together.

[ATCG]

Okay thanks. How is this done in R? and how can I actually print the r2 value? I know how to do and plot PCA but not sure how I can get the r2 value..

Also, is there a way to perform this analysis on the fastq files before alignment?

[ATpoint]

The R function is called cor, and it will return a matrix that indicates the pairwise correlation between all samples.

Also, is there a way to perform this analysis on the fastq files before alignment?

No. It works on counts. PCA on normalized log2-tranformed ones. cor can be done on raw counts since it measures linear correlations and common normalization techniques simply compute linear scaling factors, so cor does not change.

[ATCG]

The cor function does what I was expecting to do. For example form a counts matrix

a=cor(counts(dds))
cor.samples=corrplot(a, method="number")

Will produce a plot such as this one: https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html

But I would like to show a linear correlation plot such as this one

library("ggpubr")
ggscatter(z, x = "WT", y = "TREATMENT", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "WT", ylab = "TREATMENT")

Is this a good way of accomplishing this?

[ATpoint]

Use this code on your count matrix after subsetting it to the two samples of interest or the mean between the groups.