Contents
Can I use Deseq2 to analyze a dataset without replicates?
edgeR or DEseq is ok, and edgeR is suitable for a single replicate, I perefer to not use cufflinks. Better not to use cufflinks with no replicates, Deseq2 and edgeR can work samples with no replicates.
Does RNA-seq need replicates?
Recommendations for RNA-seq experiment design At least six replicates per condition for all experiments. At least 12 replicates per condition for experiments where identifying the majority of all DE genes is important.
Why is it important to have biological replicates in an RNA-seq experiment?
As the figure above illustrates, biological replicates are of greater importance than sequencing depth, which is the total number of reads sequenced per sample. The figure shows the relationship between sequencing depth and number of replicates on the number of differentially expressed genes identified [1].
What is the difference between biological and technical replicates?
Generally, biological replicates are defined as measurements of biologically distinct samples that show biological variation (21). In contrast, technical replicates are repeated measurements of the same sample that show independent measures of the noise associated with the equipment and the protocols.
What is DESeq2 RLog?
The transformation is useful when checking for outliers or as input for machine learning techniques such as clustering or linear discriminant analysis. rlog takes as input a DESeqDataSet and returns a RangedSummarizedExperiment object.
What are the steps for differential expression in deseq2?
The major steps for differeatal expression are to normalize the data, determine where the differenal line will be, and call the differnetal expressed genes. How each of these steps is done varies from program to program.
How is differential expression analysis used in medicine?
Differential expression analysis is used to identify differences in the transcriptome (gene expression) across a cohort of samples. Often, it will be used to define the differences between multiple biological conditions (e.g. drug treated vs. untreated samples). There are many, many tools available to perform this type of analysis.
When to use differential expression in RNA Seq?
Often the goal of a RNA-seq type experiment is to find differentially expressed genes. Below I give guidelines for calling differential expression. Imagine you do RNA-seq on 6 samples that are all biological replicated of each other. When you analyze them, you split them into two groups.
When to remove the dependence on the mean in differential expression?
The differential expression analysis started from raw counts and normalized using discrete distributions however when performing clustering we must remove the dependence of the variance on the mean. In other words we must remove the experiment wide trend in the data before clustering.