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)
-
Last edited by Jayesh; 12-19-2019, 10:30 AM.
-
The part about this topic feels surprisingly grounded, and the details on the page is the bit that made me slow down. I’d keep <a href="https://kinpict.com/">ai family photo generator</a> bookmarked for later.Today, 06:37 PM -
NGS has revolutionized clinical microbiology by offering new ways to identify and surveil pathogens without prior knowledge of the target. One popular approach is 16S rRNA and ITS amplicon sequencing, which targets specific microbial markers. But studies are increasingly incorporating shotgun metagenomics, a technique that sequences all DNA from a sample without targeting specific microbial markers. This approach allows more detailed taxonomic classification,...10-30-2026, 06:46 AM
| Topics | Statistics | Last Post | ||
|---|---|---|---|---|
|
0 responses
28 views
0 reactions
|
by SEQadmin2
11-12-2026, 07:59 AM
|
|||
|
0 responses
35 views
0 reactions
|
by SEQadmin2
11-05-2026, 09:25 AM
|
|||
|
2 responses
56 views
0 reactions
|
by kinpict.com
Today, 06:49 PM
|
|||
|
0 responses
175 views
0 reactions
|
by SEQadmin2
10-28-2026, 09:57 AM
|