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Introduction：

DNA methylation plays key roles in gene expression and regulation. More and more evidence shows that there are different levels of methylation patterns change in a variety of human cancers.

The whole genome bisulfite sequencing (WGBS) and Illumina methylation microarray are most common DNA methylation detection methods. Illumina methylation microarray is very cost-effective for large sample research; currently it is widely used in clinical and research. The development of this chip has mainly experienced 27K, 450K and 850K (or EPIC). Especially for large sample research, the Illumina methylation chip shows a strong price advantage, and is still widely used in clinical and research. The Infinium Methylation microarray series launched three products 27K, 450K and 850K (or EPIC), and the CpG sites covered by them increased sequentially.

This pipeline provides the analysis process for Illumina 450K and EPIC microarray, and mainly relies on the current mainstream analysis package ChAMP. The ChAMP package is a and complete analysis pipeline from reading original data files to final tertiary analysis results, including data loading, quality control, normalization, and differential analysis.

Workflow Case Analysis Versions Manual Download

Background:
Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is growing in incidence but treatment options remain limited, particularly for late stage disease. As liver cirrhosis is the principal risk state for HCC development, markers to detect early HCC within this patient population are urgently needed. Perturbation of epigenetic marks, such as DNA methylation (5mC), is a hallmark of human cancers, including HCC. Identification of regions with consistently altered 5mC levels in circulating cell free DNA (cfDNA) during progression from cirrhosis to HCC could therefore serve as markers for development of minimally-invasive screens of early HCC diagnosis and surveillance.

Methods:
To discover DNA methylation derived biomarkers of HCC in the background of liver cirrhosis, we profiled genome-wide 5mC landscapes in patient cfDNA using the Infinium HumanMethylation450k BeadChip Array. We further linked these findings to primary tissue data available from TCGA and other public sources. Using biological and statistical frameworks, we selected CpGs that robustly differentiated cirrhosis from HCC in primary tissue and cfDNA followed by validation in an additional independent cohort.

Results:

We identified CpGs that segregate patients with cirrhosis, from patients with HCC within a cirrhotic liver background, through genome-wide analysis of cfDNA 5mC landscapes. Lasso regression analysis pinpointed a panel of probes in our discovery cohort that were validated in two independent datasets. A panel of five CpGs (cg04645914, cg06215569, cg23663760, cg13781744, and cg07610777) yielded area under the receiver operating characteristic (AUROC) curves of 0.9525, 0.9714, and 0.9528 in cfDNA discovery and tissue validation cohorts 1 and 2, respectively. Validation of a 5-marker panel created from combining hypermethylated and hypomethylated CpGs in an independent cfDNA set by bisulfite pyrosequencing yielded an AUROC of 0.956, compared to the discovery AUROC of 0.996.

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Hlady R A, Zhao X, Pan X, et al. Genome-wide discovery and validation of diagnostic DNA methylation-based biomarkers for hepatocellular cancer detection in circulating cell free DNA[J]. Theranostics, 2019, 9(24): 7239.

1. Workflow Selection

1). Raw data <.idat>

If the data you want to upload is the raw intensity data files (*.idat) of microarray, please select this workflow.

2). Beta value matrix <.csv>

If the data you want to upload is methylation beta value matrix, which means the ratio of intensities between methylated and unmethylated alleles, please select this mode.

2. Customized Parameter

· Project Information

Project name : Give your analysis a name, it will serve as the recognizable name “Job Name” in “My Jobs - Results”.

· Data

1. Raw data <.idat>

In the current version, you need to add each sample one by one.

1). "Data From": "DRS", "Local", "Web"

Location of data to be analyzed.

Web = Only upload the data temporarily for analysis;

DRS = Data stored in the Data Repository Storage already;

Local = Data stored in the HPC@SJTU already.

2). "File Name"

Filename of the data to be analyzed. (_Grn.idat).

The files for each sample consists of a Red and a Grn (Green) IDAT file. Please enter one of the file names. Note: do not lose the suffix.

3). "File Name 2"

Filename of the data to be analyzed.(_Red.idat)

The files for each sample consists of a Red and a Grn (Green) IDAT file. Please enter another file name. Note: do not lose the suffix.

4). "Sample Group": "Case", "Control"

Group of each sample.

5). "Sample ID"

Customize the name for each sample. It will appear in the figures and table results as the sample identifier.

6). "Data ID"

If "Local" OR "Web" is selected in "Data From", you need to fill in the corresponding ID here.

An Example of Customized Parameter (.idat)：

2. Beta value matrix <.csv>

For the current version, you need to provide two csv files, beta value and phenotype data. These two files need to be added one by one in data.

1). "Data From": "DRS", "Local", "Web"

Location of data to be analyzed.

Web = Only upload the data temporarily for analysis;

DRS = Data stored in the Data Repository Storage already;

Local = Data stored in the HPC@SJTU already.

2). "File Name"

Filename of the data to be analyzed. Note: do not lose the suffix.

3). "Data ID"

If "Local" OR "Web" is selected in "Data From", you need to fill in the corresponding ID here.

An Example of Customized Parameter (.csv):

· Script

1). "Core"

Number of cores applied for in your task.

· Other

1). "File Format"

Which workflow is selected.

idat = Raw data <.idat>

matrix = Beta value matrix <.csv>

2). "Array Type"

450k = Infinium HumanMethylation450 BeadChip – Illumina

EPIC = Infinium HumanMethylationEPIC BeadChip – Illumina (850K)

3). "Normalization Method"(click for details)

Option to normalize data with a selection of normalization methods.

4). "Adjust Method"

Multiple testing correction method for p-value.

5). "DMR Method" (click for details)

Options to estimate regions for which a genomic profile deviates from its baseline value.

6). "DMP p-value"

The minimum threshold of significance for probes to be includede in DMPs.

7). "DMR p-value"

The minimum threshold of significance for probes to be includede in DMRs.

3. Upload Data

You need to compress the file which filled in “Data” into the corresponding format according to the following requirements.

a. If the data to be analyzed is not stored on our platform, only upload the data temporarily (Web) to use the analysis function. Please compress all the data to be analyzed into a single file, supported compression formats: rar, zip, gz, tar.gz.

b. If the data to be analyzed has been stored on our platform, including Data Repository Storage (DRS), or stored in the HPC@SJTU through the administrator (Local), please upload any content compressed file that conforms to these formats: rar, zip, gz, tar.gz.

An Example of Input Data (.idat)：

The input data are IDAT files, representing two different color channels (red and green) prior to normalization. The basename of each IDAT file consists of three parts: slide (sentrix_ID), array (Sentrix_Position), and channel.

NOTE: The format of the input file should be strictly in accordance with the example data.

An Example of Input Data (.csv)：

① beta_matrix.csv

row = cg ID;

column = samples;

no missing values

② pheno_data.csv

column 1 = “Sample_Name”;

column 2 = “Sample_Group” (at least 2 groups);

no missing value