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