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How bsseq carries on the differential methylation analysis, many novices are not very clear about this. In order to help you solve this problem, the following editor will explain it in detail. People with this need can come and learn. I hope you can get something.

Bsseq is mainly used to analyze the data of WGBS. The installation process is as follows

Source ("http://bioconductor.org/biocLite.R")"

BiocLite ("bsseq")

The analysis of bsseq mainly includes the following four steps:

Read the original data

BSmooth

T-test test

DMR

1. Read the original data

The raw data format required by bsseq is as follows:

A total of 6 columns of data, separated by tabs, each row represents a methylation site, the first five columns are easy to understand, describing the chromosome location and category of methylation sites, and bbseq is used by default to analyze CpG type methylation sites. Of course, other types of data, such as CHG and CHH, are also supported, but the parameters need to be adjusted. Cov represents the number of reads covering this site, and M represents the number of reads in which methylation has occurred.

One such raw data for each sample is used to represent the results of the sample methylation calling, and such data can also be obtained from the results of bismark. When the raw data is ready, you first need to read the raw data of all samples, and then import it into R to generate an object defined by bbseq. Under the path of the bbseq installation, a script called get_BS.chr22.R is provided that shows how to read the raw data from all the samples.

The code is as follows

Here, taking the raw data of mc_imr90_r1_22 and mc_imr90_r2_22 samples as examples, the reading process is shown in detail. We just need to modify the above code according to our own data. Just pay attention to the sampleNames and pData data.

2 Smooth

Taking the tested data BS.chr22 as an example, the process of smooth is as follows

In the actual analysis, because there are many methylation sites, this step takes a long time. In order to improve the speed, we can add the mc.cores parameter, which specifies the number of CPU for parallel execution.

BS.chr22.1

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