I am running a multiple linear regression model using lm function in R. to study the impact of some characteristics on the gene expression level. my data matrix contains one continuous dependent variable (i.e. gene expression levels) and 50 explanatory variables which are the count of these characteristics on each gene and many of these counts are zeros. I checked all of the regression assumptions and I found two issues the first one is the Heteroskedasticity and the other one is the autocorrelation problem. the later is not series. I wonder I using the multiple linear regression is correct or no and if there is any other regression techniques can be used to solve these problems.