Not that all the computation are scaled by the genome size and not at the read number as in [Hu et al.](https://doi.org/10.1093/nar/gkv670), this is also why we add a scaling factor (default to $10^3$).
Not that all the computation are scaled by the genome size and not at the read number as in [Hu et al.](https://doi.org/10.1093/nar/gkv670), this is also why we add a scaling factor (default to $10^3$).
This scaling the $\text{ratio}IP\left(t\right)$ is multiplied by this scaling factor.
The $\text{ratio}IP\left(t\right)$ is multiplied by this scaling factor.
With this method, we retain the interesting properties of [Hu et al.](https://doi.org/10.1093/nar/gkv670) normalization on the average read density between samples (i.e., we can compare two different samples in a quantitative way) and we account for the local bias of read density observed in the WCE samples (differential chromatin accessibility, repetition, low mappability region, etc.).
With this method, we retain the interesting properties of [Hu et al.](https://doi.org/10.1093/nar/gkv670) normalization on the average read density between samples (i.e., we can compare two different samples in a quantitative way) and we account for the local bias of read density observed in the WCE samples (differential chromatin accessibility, repetition, low mappability region, etc.).
...
@@ -112,6 +112,6 @@ With this method, we retain the interesting properties of [Hu et al.](https://do
...
@@ -112,6 +112,6 @@ With this method, we retain the interesting properties of [Hu et al.](https://do
To compute the coverage density $X_y(t)$ with $X \in \left[IP, WCE\right]$ and $y \in \left[c, x\right]$ we count the number of read $r(t)$ overlapping with position $t$.
To compute the coverage density $X_y(t)$ with $X \in \left[IP, WCE\right]$ and $y \in \left[c, x\right]$ we count the number of read $r(t)$ overlapping with position $t$.
For properly paired reads (with a mate read on the same chromosome and with a starting position ending after the end of the read) we also count a density of 1 between the end of the first reads and the start of his mate read $g(t)$. $X_y(t) = r(t) + g(t)$.
For properly paired reads (with a mate read on the same chromosome and with a starting position ending after the end of the read) we also count a density of 1 between the end of the first reads and the start of his mate read $g(t)$. $X_y(t) = r(t) + g(t)$.
Some fragment can be artificially long, therefore, we compute a robust mean $\mu$ of the gap size, between two reads of a pair, by removing the 0.1 upper and lower value of fragment length. Fragment that has a size higher than $\phi^{-1}(0.95, /mu, 1.0)$ are set to end at the $\phi^{-1}(0.95, /mu, 1.0)$ value, with $\phi()$ the Normal CDF function.
Some fragment can be artificially long, therefore, we compute a robust mean $\mu$ of the gap size, between two reads of a pair, by removing the 0.1 upper and lower value of fragment length. Fragment that has a size higher than $\phi^{-1}(0.95, \mu, 1.0)$ are set to end at the $\phi^{-1}(0.95, \mu, 1.0)$ value, with $\phi()$ the Normal CDF function.
Some fragment can be shorter than the read length in this case we don't count the overlapping reads region as a coverage of 2 fragment but as a coverage of 1 fragment.
Some fragment can be shorter than the read length in this case we don't count the overlapping reads region as a coverage of 2 fragment but as a coverage of 1 fragment.
