I have gene expression data from different conditions from different studies. Instead of using the actual TPM values for Pearson Correlation coefficient (PCC) calculation, I have decided to use Fold change values from different studies to eliminate biases from different studies. My question is whether using these raw fold change values for identifying co-expressed genes is a correct way to do it or should perform quantile normalization on these fold change values before using them for PCC calculation? (Note: Distribution of fold change values in different studies is quite different)
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by SEQadmin2
Tumors contain complex mixtures of cell populations that evolve in response to treatment and host-specific factors. Tissue biopsies provide only static snapshots of a tumor’s state. But what if we could follow that evolution over time using a minimally invasive technique?
Liquid biopsies can detect tumor-derived material circulating in the bloodstream, providing a way to study treatment response, detect emerging resistance, and discover new biomarkers. Circulating...-
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10-14-2026, 09:55 AM -
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by SEQadmin2
Researchers using sequencing and genomics tools often have to make trade-offs. They can choose between speed or scale, short reads or long-range information, or targeted panels or a view of the whole transcriptome. New technologies that have been released this year are built to address those tough choices.
We asked six companies the same four questions to learn about their latest products. The new technologies bring a lot to the table, including rethinking sequencing...-
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