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Single Nucleotide Polymorphisms-SNPs

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...

Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

Scatter Plot

Scatter Plot

The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:

Modified Boxplots

Modified Boxplots

A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...

Residual Plots

Residual Plots

A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Related Experiment Video

Updated: May 13, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Visualization of SNPs with t-SNE.

Alexander Platzer¹

¹Gregor Mendel Institute, Vienna, Austria. alexander.platzer@gmi.oeaw.ac.at

|March 5, 2013

Summary

Principal Component Analysis (PCA) is an older method for visualizing Single Nucleotide Polymorphisms (SNPs). Newer methods like t-Distributed Stochastic Neighbor Embedding (t-SNE) offer superior visualization for large SNP datasets.

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single Nucleotide Polymorphisms (SNPs) represent a significant and growing data source in biological research.
Principal Component Analysis (PCA) is the conventional method for visualizing SNP data between individuals.

Purpose of the Study:

To compare the effectiveness of PCA with t-Distributed Stochastic Neighbor Embedding (t-SNE) for visualizing large SNP datasets.
To introduce novel metrics for evaluating the performance of data visualization techniques.

Main Methods:

Comparative analysis of PCA and t-SNE for SNP data visualization.
Development and application of key figures for visualization performance evaluation, including cross-validation with machine learning and cluster validity indices.

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

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Last Updated: May 13, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Main Results:

t-Distributed Stochastic Neighbor Embedding (t-SNE) demonstrates superior performance compared to PCA in visualizing large SNP datasets.
The proposed key figures effectively evaluate visualization performance, with t-SNE consistently outperforming PCA.

Conclusions:

While PCA remains useful for data transformation, t-SNE is recommended as a replacement for visualizing SNP data.
The proposed key figures provide a robust framework for assessing visualization quality in high-dimensional biological data.