The Classification of HLA Supertypes by GRID/CPCA and Hierarchical Clustering Methods
Biological experiments often produce enormous amount of data, which are usually analyzed by data clustering. Cluster analysis refers to statistical methods that are used to assign data with similar properties into several smaller, more meaningful groups. Two commonly used clustering techniques are introduced in the following section: principal component analysis (PCA) and hierarchical clustering. PCA calculates the variance between variables and groups them into a few uncorrelated groups or principal components (PCs) that are orthogonal to each other. Hierarchical clustering is carried out by separating data into many clusters and merging similar clusters together. Here, we use an example of human leukocyte antigen (HLA) supertype classification to demonstrate the usage of the two methods. Two programs, Generating Optimal Linear Partial Least Square Estimations (GOLPE) and Sybyl, are used for PCA and hierarchical clustering, respectively. However, the reader should bear in mind that the methods have been incorporated into other software as well, such as SIMCA, statistiXL, and R.
- miRNA与肿瘤
- 大鼠基质金属蛋白酶抑制剂(TIMP)酶联免疫分析
- 自身免疫性疾病
- 补体介导的细胞毒试验(Complement Mediated Cytotoxicity Test)
- 治疗基因
- 白细胞介素IL-3
- In Vitro Treg Suppression Assays
- Identification of Peptides that Mimic N. meningitidis LOS Epitopes Via the Use of Combinatorial Phage-Display Libraries
- Generation of T Cell Hybridomas from Naturally Occurring FoxP3+ Regulatory T Cells
- BrdU Staining Protocol