CUT&Tag differential peak analysis using Diffbind, with pre-calculated normalization factor.
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mwong043 • 0
Last seen 1 day ago

Hi. I would like to perform differential peak analysis for my CUT&Tag data. I converted the bam files of the samples to bedgraph files. I then normalized the signal of the bedbraph files using a normalization factor calculated based on the E. coli bacterial read count. Since my Tn5 enzyme was extracted in E coli, the bacterial read count can therefore serve as an internal spike in control. I then use SEACR, which is the recommended peak caller for CUT&Tag experiments to call peaks for my samples.

For the next step I would like to perform differential peak analysis using Diffbind. But my question is how to normalize the samples using pre-calculated normalizing factor? In the Reference Manual it mentioned that users can supply normalization factors by using the DBA_NORM_USER command, how exactly this can be done?

For example, I have 6 samples in 2 groups, with the following normalization factor : Group 1: 0.85, 0.96, 1.2,

Group 2: 1.1, 0.9, 0.8

Thanks. Matthew

DiffBind • 74 views
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Rory Stark ★ 3.8k
Last seen 21 hours ago
CRUK, Cambridge, UK

You can supply your own normalization factors by calling the the dba.normalize() function after running dba.count(). The normalize parameter should be a vector of the same length as the number of samples. Larger values should correspond to samples with greater numbers of E. coli reads.

Alternatively, in addition to the SEACR peaks and CUT&Tag bam files, you can supply the E. coli reads to DiffBind and have it re-compute the normalization factors. These can be specified as a separate E. coli-aligned bam files using the Spikein column of the sample sheet, then set spikein=TRUE (instead of using the normalize parameter). If your E. coli reads are included in the same bam files as your CUT&Tag reads, instead of using specifying Spikein files, set spikein to a vector of the E. coli chromosome names.


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