Hi,
If our phenotypes in a table named clinical data is like so
## FamID CAD sex age tg hdl ldl
## 10002 10002 1 1 60 NA <NA> <NA>
## 10004 10004 1 2 50 55 23 75
## 10005 10005 1 1 55 105 37 69
## 10007 10007 1 1 52 314 54 108
## 10008 10008 1 1 58 161 40 94
## 10009 10009 1 1 59 171 46 92
instead of one variable in our clinical table for instance hdl like below
phenoSub$phenotype <- rntransform(as.numeric(phenoSub$hdl), family="gaussian")
how can I use another variables like tg, hdl, ldl at the same time???
I tried so but went wrong
phenoSub$phenotype <- rntransform(as.numeric(phenoSub$c(colnames(clinical)), family="gaussian")
Thank you
•
written
by
zizigolu
1 answer
Hello again, to use multiple variables, you will have to create a generalised linear model in the form y ~ x, where y is your phenotype or end-point, and x is a predictor.
In this example, we predict coronary artery disease (CAD) using high- and low-density lipids, and triglycerides.
rntransform(CAD ~ hdl + ldl + tg, data=df, family="gaussian")
[1] NA 1.2815516 -0.2533471 -0.2533471 -0.2533471 -0.2533471
I had assumed that you were aiming to predict coronary artery disease (CAD). The rntransform function is just attempting to normalise your variables so that they can be more reliably used in modeling.
Thus, when you run:
phenoSub$phenotype <- rntransform(as.numeric(phenoSub$hdl), family="gaussian")
,all that is returned is a normalised version of your hdl variable, which can then be more reliably used in a binomial logistic regression model.
When I run:
rntransform(CAD ~ hdl + ldl + tg, data=df, family="gaussian")
,it is normalising the residuals from my model
Here's what the package says:
Details: Rank-transformation to normality generates perfectly normal distribution from ANY distribution, unless many/heavy ties are present in variable (or residuals, if formula is used).
Thus, if you have no endpoint, my recommendation is to run rntransform independently on all of your variables of interest (preferably in a loop), and then run whatever regression models you were originally aiming to do in order to test your hypotheses, presumably relating to SNP genotypes.
Further reading:
- Look at the tutorial Genome-wide association analysis - Association analysis of typed SNPs using parallel processing, where they first normalise the same data that you're looking at.
- Take a look at recent work by people I worked with just this past
year ( Screening
for interaction effects in gene expression data - page 12), where they
used
rntransform
Good luck
#Get column indices of variables to be transformed
variableIndices <- which(colnames(phenoSub) %in% c("hdl", "ldl", ..., ...))
#Create a new data-frame to store the transformed variables
newDataFrame <- data.frame(row.names=rownames(phenoSub))
#Keep a loop counter
j <- 1
#Loop through indices in variableIndices
for (i in variableIndices) {
temp <- rntransform(as.numeric(phenoSub[,i]), family="gaussian")
newDataFrame <- data.frame(newDataFrame, temp)
colnames(newDataFrame)[j] <- colnames(phenoSub)[i]
j <- j + 1
}
Then newDataFrame will contains your transformed variables. You can then merge this with your genotype data and then test your hypotheses through modelling.
No problem, you may also want to take a look at this study, in which rntransform was also used. However, they appeared to have used the normalisation of regression model residuals: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3400731/
Yes, that is possible. In that case, you would use a linear model with cholesterol as the y variable:
lm(cholesterol ~ gene1)
lm(cholesterol ~ gene2)
lm(cholesterol ~ gene3)
...
lm(cholesterol ~ geneX)
Then take the statistically significant genes an pt them together into a preliminary 'final' model that would require yet further testing:
lm(cholesterol ~ gene4 + gene10 + gene678)
Hello friend. I also just replied to your new question: A: Predicting phenotype based on the expression of genes
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