# How to analyze the correlation of two different tissue gene expression under three conditions?

I have three conditions A, B, and C. For each state, I have gene expression data of two different tissues(liver and pancreas). I am studying the possible network/correlation/ between tissues for each condition independently. Secondly, I would like to figure out what change happened by comparing the three states. So please let me know the right analysis type and R tool I could implement. If you want to see the data, here below I simulated it.

# Condition A
# liver
set.seed(22)
li.A <- matrix(rnorm(100), nrow = 20)
rownames(li.A) <- LETTERS[1:20]
colnames(li.A) <- paste0("S_", ncol = 1:5)
# pancreas
set.seed(42)
pa.A <- matrix(rnorm(100), nrow = 20)
rownames(pa.A) <- LETTERS[1:20]
colnames(pa.A) <- paste0("S_", ncol = 1:5)

# Condition B
# liver
set.seed(44)
li.B <- matrix(rnorm(100), nrow = 20)
rownames(li.B) <- LETTERS[1:20]
colnames(li.B) <- paste0("S_", ncol = 1:5)
# pancreas
set.seed(84)
pa.B <- matrix(rnorm(100), nrow = 20)
rownames(pa.B) <- LETTERS[1:20]
colnames(pa.B) <- paste0("S_", ncol = 1:5)

# Condition C
# liver
set.seed(88)
li.C <- matrix(rnorm(100), nrow = 20)
rownames(li.C) <- LETTERS[1:20]
colnames(li.C) <- paste0("S_", ncol = 1:5)
# pancreas
set.seed(168)
pa.C <- matrix(rnorm(100), nrow = 20)
rownames(pa.C) <- LETTERS[1:20]
colnames(pa.C) <- paste0("S_", ncol = 1:5)


THANK YOU!