Dear fellows:

I have some ATAC bam files(A549 in drugged and control condition) which have different sequence depths that need to convert to bigwig.

The tools I use for conversion is **deeptools bamCoverage**, and I use **deeptools multiBamSummary** to generate scale factor for each bam file. then I use **bamCoverage** with `--normalizeUsing RPGC` and `--scaleFactor of each bam` file to generate bigwig.

After I got the bigwig file of each sample, I load them into IGV. My propose is to display different heights of peak pileups(eg. higher peak pileups in treated samples and lower pileups on control) at target differential access positions(already known) between those samples.

Here are my questions:
1. Does my normalization way(use `RPGC` and `scale factors` when converting bam to bigwig by **bamCoverage**) make sense?
2. when I load **already normalized** bigwig files into IGV without adjusting any IGV params, does the displayed track height of each sample could give the expected trend(the higher track means actual reads pileups and vice versa), or do I still need to use IGV's function **autoscale** or adjust **data range** to some range to let the peak fit the trend?

Here are two figures related to my question: Fig. A and Fig.B both use normalized bigwig tracks by `RPGC` and a `scaling factor` of positive and negative treated and control samples. the red rectangle area means the expected differential access positions will show the different track heights. 

In Fig A, I use IGV autoscale function to 3 tracks; In Fig B, I set the data range of 3 tracks both to 50. The position 1 in two figures gives the conflict interpretation: in Fig A auto-scaled track, their height seems the same; but in Fig B tracks be set the same data range, the negative_treated track has the highest peak.

##### Fig. A: IGV auto scaled
![ABCC3_autoscale][1]

##### Fig. B: IGV set the same data range to 50
![ABCC3_same_range][2]

Can I have your advice on solving my issues?

Many thanks!


  [1]: /media/images/3fd7d10e-a8e8-48b5-ab29-474b2f94
  [2]: /media/images/9957d964-330b-4387-aa7a-230a2e51
Yes, the RPGC normalization make sense. 

Considering the visualization within IGV, I would recommend to using either option #2 or the 'group autoscale' feature (select all tracks, right click, left click on `group autoscale`). With that setting, all tracks will have the same range, like in your second example except that the range will automatically fit all the data. In both option #2 and <group autoscale>, higher peak means more coverage which is the correct interpretation.