Using RNA-seq expression data in a classification model
Hi all,
So I have noticed that for microarray data, there are countless accounts of using microarray expression data to predict some phenotypic quantity, classification, etc... I have been tasked with building a classification model using RNA-seq expression as covariates in a logistic regression, but there doesn't appear to be much literature on doing this sort of analysis. This leads to the natural question for me, is this a bad idea?
Thanks!
• 3,454 views
•
link
updated
by
Ram
•
written
by
knpayne2 •
•
written
by
knpayne2 •
0 answers
No answers yet.
Log in to answer this question.
More posts like this
-
Feature selection for multi-class classification problem.
written by Shakunthala Natarajan •
-
KEGG pathways
written by Blue •
-
Validate RNAseq Models in Microarray Data?
written by vitor •
-
Set fixed amount of variables to use for lasso regression
written by bart •
-
Approach to building regression models or classifiers with high number of parameters but small samp…
written by Jeffrey3555054 •
-
Best order of imputation and normalization in data preprocessing
written by david.f.stein •
-
How to use limma for a categorial outcome variable? Where outcome is the cohort status and predicto…
written by halo22
-
Blog: Machine Learning Training in Chennai
written by softlogicseo •
-
Standardization of gene expression and methylation dataset to the same scale
written by ymukhiddin •
-
Covariate Selection For Microarray Data
written by aditi.qamra
Do you already have the table with read counts ready? And do you have training set with the classes you want to to use and their expression levels? If so, it's pretty straightforward to just go ahead and see if logistic regression makes sense or not. See corresponding chapters in ISLR book: http://www-bcf.usc.edu/~gareth/ISL/