How does MACS handle read density normalization?

Question

[tnc25]

Hi, I am curious about the process of how MACS normalizes read density. For example, my ChIP sample has 1/2 the number of reads as my input. Does MACS correct for this? I read in the manual that by default MACS normalizes by scaling the smaller dataset towards the larger dataset. I can reverse this by using the --to-small option. Am I interpreting the meaning of the --to-small option correctly? Thank you for your help.

Answer 1

[Ian]

In MACS1.4 and MACS2 by default the larger sample is scaled down to the smaller:

--to-large              When set, scale the small sample up to the bigger
                        sample. By default, the bigger dataset will be scaled
                        down towards the smaller dataset, which will lead to
                        smaller p/qvalues and more specific results. Keep in
                        mind that scaling down will bring down background
                        noise more. DEFAULT: False