Fpertille Fpertille - 1 month ago 6
R Question

Relationship or correlation between two dataframes with several columns

I have two dataframes and I would like to show graphically (scatter plot) the correlation between the rows of these two dataframes (genes vs protein) to see each rows are related. Therefore, I can see two strategies to be used:
1. A linear regression between both dataframe (no idea how)
2. A Person correlation between both using the mean (and standard deviation) of the columns.

Some one can help me to design these graphs?

Here is an exemple of my data:

genes <- "gene sample1 sample2 sample3 sample4
gene1 1863.4 1972.94 1603.96 1185.6
gene2 213.88 247.14 189.02 208.793
gene3 8.06 9.25 9.59 7.33
gene4 22.36 3.76 10.64 19.17"
genes<-read.table(text=genes,header=T)

protein <- "protein sample1 sample2 sample3 sample4
protein1 314.2871797 426.8856595 405.7971059 334.1369651
protein2 4747.866647 3070.916824 2780.352062 2990.085431
protein3 1621.566329 1290.470104 1554.27426 1601.357345
pretein4 8832.210499 7796.675008 8461.733171 9500.429355"
protein<-read.table(text=protein,header=T)


Thank you

Answer

I appreciate the answers that were scored positively by me, and also helped me to solve the trick as follows:

#exemple data
genes <- "gene  sample1 sample2 sample3 sample4
    gene1   1863.4  1972.94 1603.96 1185.6
gene2   213.88  247.14  189.02  208.793
gene3   8.06    9.25    9.59    7.33
gene4   22.36   3.76    10.64   19.17"
genes<-read.table(text=genes,header=T)

protein <- "protein sample1 sample2 sample3 sample4
protein1    314.2871797 426.8856595 405.7971059 334.1369651
protein2    4747.866647 3070.916824 2780.352062 2990.085431
protein3    1621.566329 1290.470104 1554.27426  1601.357345
pretein4    8832.210499 7796.675008 8461.733171 9500.429355"
protein<-read.table(text=protein,header=T)

#getting the individuals average:
mRNA_expression<- data.frame(genes=genes[,1], Means=rowMeans(genes[,-1]))
Protein_abundance<- data.frame(protein=protein[,1], Means=rowMeans(protein[,-1]))

#merging both to do the correlation graph
mean_corr <- data.frame(mRNA_expression[,2],Protein_abundance[,2])
names(mean_corr) <- c("mRNA_expression","Protein_abundance")

#deleting NA lines
mean_corr <- mean_corr[complete.cases(mean_corr),]

#appling log10
mean_corr <- log10 (mean_corr)

library(ggplot2)

#to check the distribution
ggplot(mean_corr, aes(x=Protein_abundance, y=mRNA_expression)) + labs(x = "Protein abundance (log10)", y = "mRNA expression (log10)") +  theme(axis.title.y=element_text(margin=margin(0,10,0,0))) +  theme(axis.title.x=element_text(margin=margin(10,0,0,0))) +
  geom_point(shape=1)  # Use hollow circles
#Different kind of plots::

ggplot(mean_corr, aes(x=Protein_abundance, y=mRNA_expression)) + labs(x = "Protein abundance (log10)", y = "mRNA expression (log10)") +  theme(axis.title.y=element_text(margin=margin(0,10,0,0))) +  theme(axis.title.x=element_text(margin=margin(10,0,0,0))) + 
  geom_point(shape=1) +    # Use hollow circles
  geom_smooth(method=lm)   # Add linear regression line 
#  (by default includes 95% confidence region)

ggplot(mean_corr, aes(x=Protein_abundance, y=mRNA_expression))+ labs(x = "Protein abundance (log10)", y = "mRNA expression (log10)") +  theme(axis.title.y=element_text(margin=margin(0,10,0,0))) +  theme(axis.title.x=element_text(margin=margin(10,0,0,0))) +
  geom_point(shape=1) +    # Use hollow circles
  geom_smooth(method=lm,   # Add linear regression line
              se=FALSE)    # Don't add shaded confidence region

ggplot(mean_corr, aes(x=Protein_abundance, y=mRNA_expression)) + labs(x = "Protein abundance (log10)", y = "mRNA expression (log10)") +  theme(axis.title.y=element_text(margin=margin(0,10,0,0))) +  theme(axis.title.x=element_text(margin=margin(10,0,0,0))) +
  geom_point(shape=1) +    # Use hollow circles
  geom_smooth()            # Add a loess smoothed fit curve with confidence region

#statistics
#to check the correlation
cor(mean_corr)

#linear regression
#lm(genes_mean ~ protein$mean, data=mean_corr)
lm(Protein_abundance ~ mRNA_expression, data=mean_corr)