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This article mainly shows you the "sample analysis of Oncomine tumor chip database", the content is easy to understand, clear, hope to help you solve your doubts, the following let the editor lead you to study and learn "sample analysis of Oncomine tumor chip database" this article.

As the number one killer of human health, the importance of tumor research is self-evident. With the development of chip and NGS technology, a lot of tumor-related data have been published. However, these data come from different organizations and teams. Due to the lack of unified data management and organization, these data are no longer used after publication. In order to improve data utilization and promote the development of cancer research, Oncomine's development team collects tumor-related chip data from various sources and processes them with standardized analysis processes. The results of data analysis are queried and visualized through web services. The corresponding article links are as follows

Https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1635162/

The web address of the database is as follows

Https://www.oncomine.org/resource/main.html

The website is only free for researchers and requires academic mailboxes to register corresponding accounts. The database mainly contains the following two types of tumor chip data.

MRNA expression

DNA copy number

Each batch of chip data is represented by dataset. The corresponding tumor type and sample number of all chip data are shown below.

For chip data, the following types of analysis are mainly carried out.

Coexpression analyses

Differential expression analyses

Outlier analyses

The panel on the home page is divided into three parts, as shown below

The leftmost part is used to retrieve or filter the data, the middle panel is used to display all the datasets, and the rightmost panel is used to display the detailed results of the dataset. The most basic form of presentation is the heat map, which is shown as follows

1. Coexpression analysis

Specify a gene of interest in the search box, and then you can view the results of genes co-expressed with that gene in a specific data set, as shown below

Correlation is from 0 to 1, and the closer it is to 0, the higher the correlation.

two。 Difference analysis

There are two kinds of difference analysis.

Cancer_vs_cancer

Cancer_vs_normal

The heat map of the difference analysis is as follows

Pvalue, fold change and other statistical indicators of differences are given. The difference analysis of multiple genes is shown in the form of the above heat map, and the results of difference analysis of a single gene are shown in the following form.

The expression of genes in each sample is shown in the form of a bar chart, and each column represents a sample.

3. Outlier analysis

Due to the heterogeneity of tumor samples, there will be cases in which proto-oncogenes are only overexpressed in some samples. when all samples are used for differential analysis, the results of differential analysis of these genes are not significant. For this reason, a special algorithm for outlier analysis is developed, the full name is as follows

Cancer outlier profile analysis

Referred to as COPA, to identify potential proto-oncogenes that are highly expressed only in some tumor samples. The results are as follows

The higher the COPA value, the more likely the gene is to be a real outlier gene. Through the Oncomine database, we can easily explore the tumor-related DNA copy number and gene expression profile data.

The above is all the contents of this article "sample Analysis of Oncomine tumor Chip Database". Thank you for reading! I believe we all have a certain understanding, hope to share the content to help you, if you want to learn more knowledge, welcome to follow the industry information channel!

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