If you're looking to compare modification enrichment in genomic features then there are a couple of ways to do this.
You could put probes over your feature of interest and then do an enrichment quantitation and compare either the means or the distributions between your two samples. This would tell you if one sample was more enriched than another on average. The problem with this approach is that you may well see overall differences in enrichment which come from technical effects (how well the ChIP worked) rather than biological. These effects should be global though, so you could, for example, compare enrichment in promoters vs exons.
Alternatively you could make a simpler comparison by simply counting the number of promoters which showed enrichment and then comparing values between your samples. In many cases a simple quantitation of corrected read counts will show a nice bivalent distribution where you can easily set a threshold to separate the enriched from non-enriched populations. You could then apply this to your two samples and compare the number of promoters which pass the filter. This might not work well if there isn't a clear distinction between enriched and non-enriched in your sample though.
The probe trend plot probably isn't best suited to this kind of analysis. Its strength is in showing the pattern of enrichment to see if that changes, rather than judging the strength of enrichment which is normally better handled by the conventional quantitation tools. If you do want to use the trend plot to do this then you will need to use the cumulative distribution plot, but beware that (as the docs you quoted state), this is susceptible to bias from extreme outliers since it just sums the counts across all probes and makes no distinction between them in the final plot.
You could put probes over your feature of interest and then do an enrichment quantitation and compare either the means or the distributions between your two samples. This would tell you if one sample was more enriched than another on average. The problem with this approach is that you may well see overall differences in enrichment which come from technical effects (how well the ChIP worked) rather than biological. These effects should be global though, so you could, for example, compare enrichment in promoters vs exons.
Alternatively you could make a simpler comparison by simply counting the number of promoters which showed enrichment and then comparing values between your samples. In many cases a simple quantitation of corrected read counts will show a nice bivalent distribution where you can easily set a threshold to separate the enriched from non-enriched populations. You could then apply this to your two samples and compare the number of promoters which pass the filter. This might not work well if there isn't a clear distinction between enriched and non-enriched in your sample though.
The probe trend plot probably isn't best suited to this kind of analysis. Its strength is in showing the pattern of enrichment to see if that changes, rather than judging the strength of enrichment which is normally better handled by the conventional quantitation tools. If you do want to use the trend plot to do this then you will need to use the cumulative distribution plot, but beware that (as the docs you quoted state), this is susceptible to bias from extreme outliers since it just sums the counts across all probes and makes no distinction between them in the final plot.
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