Shulou(Shulou.com)06/01 Report--

What is the PCA analysis in chip_seq quality assessment? I believe many inexperienced people don't know what to do about it. Therefore, this paper summarizes the causes and solutions of the problem. Through this article, I hope you can solve this problem.

PCA, which we call principal component analysis, is a classical data dimensionality reduction algorithm, which maps high-dimensional data to low-dimensional space by representing high-dimensional data with several principal components. In the actual processing, because we can only have an intuitive feeling of two-dimensional and three-dimensional data, we usually draw two-dimensional and three-dimensional scatter plots.

PCA is essentially a kind of sort analysis. The reduced data is displayed by scatter plot in two-dimensional or three-dimensional plane. The closer the distance between the two sample points is, the more consistent the two samples are. PCA diagram is widely used in bioinformatics. The algorithm is widely used in genome, transcriptome and other data analysis. This paper mainly introduces PCA analysis in chip_seq data analysis.

In transcriptome, we can do PCA analysis of samples through gene expression profiles. In chip_seq data analysis, in order to obtain data similar to gene expression profiles, researchers put forward an idea that the genome is divided into equal intervals, called bin, and then the coverage in each interval is calculated. Once the coverage of all the bin in the sample is obtained, the data can be used for PCA analysis. The specific operation steps are as follows, which are realized through deeptools.

1. Calculate the coverage of bin

The input file is the bam file generated by comparing the genome, and the usage is as follows

MultiBamSummary bins\

-- bamfiles file1.bam file2.bam\

-- binSize 10000\

-- numberOfProcessors 10\

-- outRawCounts results.txt\

-o results.npz\ 2. PCA analysis

It is implemented through the plotPCA command, and the usage is as follows

PlotPCA\

-in results.npz\

-o PCA.png

The output is shown below

By default, the software chooses the first and second principal components to draw a two-dimensional scatter plot, in which some basic judgments can be made on the data quality by observing the distance between sample points. Theoretically, there should be a large distance between input and antibody-treated samples, while biological repetitive samples should be close.

It should be noted that the contribution rate of the first two principal components is an important indicator. It is assumed that the sum of the contribution rate of the two principal components is 90%, which means that the two-dimensional scatter diagram can only represent 90% of the information of the original sample. When the contribution rate is too low, the information expressed on the scatter diagram is far from the information of the original sample, so it does not have much reference significance.

The Scree plot in the lower part is similar to the gravel diagram, but in the form of double axes, the blue column chart represents the eigenvalues of the first five principal components, the red curve represents the cumulative eigenvalues, and each point represents the proportion of the cumulative eigenvalues. When the red curve tends to flatten, it means that even if other principal components are added, the information displayed will not change significantly, that is, the first few principal components can effectively represent the overall information. In the above picture, the first four principal components can effectively represent the overall information.

Although we can screen out the principal components from the gravel map, because we can only directly observe the three-dimensional space at most, we can only draw three-dimensional scatter diagrams in PCA analysis. If the first three principal components can not effectively represent the overall information, we can only consider using other dimensionality reduction algorithms. This problem is also a common problem of all dimensionality reduction algorithms.

After reading the above, have you mastered the method of PCA analysis in chip_seq quality assessment? If you want to learn more skills or want to know more about it, you are welcome to follow the industry information channel, thank you for reading!

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.