Hi everyone,

I am currently doing research related to smallRNAs and I have a data set where I have around 50 smallRNAs (rnaseq) in close to 100 samples.

sample data looks like:

age     srna1     srna2     srna3     srna4     srna5     gender     smoker     disease
51     0          512       67        186       83        1          1          1
53     18         3449      112       422       54        2          1          1
48     0          69        0         9         0         1          1          0
55     0          966       38        64        47        2          1          1

I have to look for differences between disease(n1) and controls(n2) along with age and gender information. I have been reading in papers where the authors are talking about adjusting counts for age and gender.

I tried to perform logistic regression taking age and gender as covariates. However, I am confused now as how to get the adjusted (for age and gender) counts.

# Read the dataset in a dataframe
d <- read.table("input_file.txt", header=T, sep="\t")

# Extract the filtered Srna`s
fSrna <- subset(d, select=-c(age, gender, smoker, disease))
Y <- cbind()
for (i in names(fSrna)){Y=cbind(Y, fSrna[[i]])}

# Perform logistic regression
glmresults <- glm(disease ~ age + gender + Y, family=binomial(link="logit"), data=d)

names(glmresults)
 [1] "coefficients"      "residuals"         "fitted.values"    
 [4] "effects"           "R"                 "rank"             
 [7] "qr"                "family"            "linear.predictors"
[10] "deviance"          "aic"               "null.deviance"    
[13] "iter"              "weights"           "prior.weights"    
[16] "df.residual"       "df.null"           "y"                
[19] "converged"         "boundary"          "model"            
[22] "call"              "formula"           "terms"            
[25] "data"              "offset"            "control"          
[28] "method"            "contrasts"         "xlevels"

I would be glad if someone can help me to understand this more clearly and provide directions to calculate the adjusted or corrected counts for the smallRNAs.

Related biostar posts:

• How to adjust data for "continuous" covariates ?
• How To Adjust Data For Confounding Covariates Before Pca?
• Identify The Effect Of Covariates With Count Data
• Normalizing For Batch Effect In RNA-Seq Data

Thanks in advance.