Validating microarray data with real time rt pcr

Correlation between RNAseq and microarray is usually pretty good.

In my survey of dozens of papers, R-square is around 0.8.

There were various sizes for floppies back then, I used at least 8″, 5″, 3.5″ and 3″ disks.

As technology matured, many of them faded away and the one that remains on the market is the results of survival of the fittest.

In this post, I want to compare gene expression analysis using two platforms: RNA-seq and DNA microarray.

When DNA microarray was technology first introduced, spot c DNA microarray was quite variable between arrays and it was necessary to run technical replicates as well as dye-swap experiments.

In this case, you need to figure out which form(s) of the gene is (are) actually differentially expressed.

While this can be done with q-RT-PCR or northern blot analysis, it takes more time and effort to confirm it.

Recently, however, Next Gen sequencing (NGS) technology has provided a new path for gene expression analysis.

Illumina says the sensitivity of microarray (human) for the major vendor is 10 million mapped reads/sample on average, RNA-seq should provide a lot higher sensitivity than microarray.

In order to find whether the results obtained from RNA-seq or microarray are accurate, quantitative real-time PCR (q-RT-PCR) is most commonly used.

Some m RNAs have only a few copies per cell, while the most abundant ones have 10,000 copies per cell.

Before talking about dynamic range, let’s talk about how similar data obtained by RNA-seq and microarray are.

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