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)
Unconfigured Ad
Collapse
X
Latest Articles
Collapse
-
by SEQadmin2
The immune system’s power comes from its genetic diversity, allowing myriad threats to be neutralized through first recognizing foreign antigens. That diversity is also what makes the immune system so difficult to study. Recent advances in sequencing technology and computational biology, however, are giving researchers new tools to understand immune responses and immune-related diseases in greater detail.
This convergence of genetics, immunology, and computation...-
Channel: Articles
Yesterday, 05:41 AM -
ad_right_rmr
Collapse
News
Collapse
| Topics | Statistics | Last Post | ||
|---|---|---|---|---|
|
Started by SEQadmin2, Today, 12:32 PM
|
0 responses
3 views
0 reactions
|
Last Post
by SEQadmin2
Today, 12:32 PM
|
||
|
Started by SEQadmin2, 08-24-2026, 10:32 AM
|
0 responses
48 views
0 reactions
|
Last Post
by SEQadmin2
08-24-2026, 10:32 AM
|
||
|
Started by SEQadmin2, 08-20-2026, 11:17 AM
|
0 responses
49 views
0 reactions
|
Last Post
by SEQadmin2
08-20-2026, 11:17 AM
|
||
|
Started by SEQadmin2, 08-18-2026, 10:05 AM
|
0 responses
55 views
0 reactions
|
Last Post
by SEQadmin2
08-18-2026, 10:05 AM
|