Bioinformatics Analysis FAQ

• • How Do Principal Component Analysis (PCA) and Factor Analysis (FA) Differ?

  Principal Component Analysis (PCA) and Factor Analysis (FA) are widely used multivariate statistical techniques for dimensionality reduction and extracting key information from data. Although both methods reduce data dimensionality, they differ in their objectives, underlying assumptions, and applications. The following outlines the key distinctions between PCA and FA:   Principal Component Analysis (PCA) 1. Objective PCA aims to transform original variables into a set of uncorrelated principal compon......

• • How Can the Precursor Protein Be Identified After Peptide Sequencing? Which Software Tools Are Available?

  After identifying a peptide sequence, assigning it to its corresponding precursor protein typically involves the following steps:   1. Database Search The peptide sequence is searched in established protein databases such as UniProt, NCBI, and Swiss-Prot to identify potential precursor proteins. This is the most commonly used approach, in which experimentally derived peptide sequences are compared against protein sequence databases to find the most likely matches.   2. Computational Tools for Peptide-......

• • How to Conduct a Simple GO Annotation Analysis?

  A simple Gene Ontology (GO) annotation analysis can be performed by following these steps:   1. Prepare a Gene List Start with a list of genes of interest, such as differentially expressed genes obtained from transcriptome sequencing.   2. Select an Online Analysis Tool Several bioinformatics tools are available for GO annotation and enrichment analysis. Commonly used tools include: (1) DAVID (Database for Annotation, Visualization and Integrated Discovery): A widely used platform that supports GO enr......

• • How to Understand Principal Component Scores in Principal Component Analysis?

  Principal component scores represent the coordinates of each sample in the principal component space. They provide insights into the sample’s position and its relative contribution to each principal component. A higher principal component score indicates a stronger projection of the sample onto that principal component, signifying greater alignment with the corresponding variance direction.   Interpretation as New Coordinates Principal component scores can be interpreted as the coordinates of each dat......

• • Which Online Tools Can Compare Antibody Heavy and Light Chain Variable Region Sequences?

  Several specialized bioinformatics tools and databases can be used to align and compare the variable region sequences of antibody heavy and light chains. These platforms not only facilitate sequence alignment but also provide structural and functional insights into antibodies. Below are commonly used tools:   IMGT (International ImMunoGeneTics Information System) 1. Description IMGT/V-QUEST is a dedicated tool for immunoglobulin (Ig) and T cell receptor (TCR) sequence analysis. It allows alignment of ......

• • How to Perform PLS-DA and OPLS-DA Analysis and Visualization in R?

  Performing PLS-DA (Partial Least Squares Discriminant Analysis) and OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis) in R, along with generating relevant visualizations, requires the use of specific R packages. The general workflow consists of the following steps:   1. Preparation Before conducting the analysis, users must install appropriate R packages that provide the necessary functions for PLS-DA and OPLS-DA. Commonly used packages include mixOmics, ropls, and pls.   2. Data Prepro......

• • How to Identify Relevant Pathways from GO and KEGG Enrichment Analysis? What Approach and Databases Are Recommended?

  The identification of pathways relevant to research objectives using GO and KEGG enrichment analysis can be achieved through the following approach:   Clarifying Research Background and Objectives Before conducting enrichment analysis, it is crucial to define the research objectives and hypotheses. Understanding the biological processes, diseases, or conditions of interest helps pinpoint the most relevant pathways.   Performing GO and KEGG Enrichment Analysis Utilize appropriate bioinformatics too......

• • What to Do if Many Data Points Are Outliers in PLS-DA Fitting? Is the Data Still Valid?

  When performing PLS-DA fitting, the appearance of many outliers may indicate issues such as overfitting or other model-related problems. Below are several solutions and suggestions to address this issue:   Verify Data Quality The first step is to check the data's quality and accuracy. Inspect the data for outliers, missing values, or other potential errors. If any data quality issues are found, reprocessing or cleaning the data may be necessary to ensure reliable results.   Feature Selection If the da......

• • After KEGG Identifies Pathways, Where to See Each Pathway's Function?

  After using the KEGG (Kyoto Encyclopedia of Genes and Genomes) database to obtain signaling pathways, you can directly view detailed information and functions for each pathway within the KEGG database. KEGG provides extensive pathway maps and related details that help in understanding the biological functions and mechanisms of the pathways.   Visit the KEGG Website Navigate to the official KEGG PATHWAY Database on the KEGG website.   Search for a Specific Pathway Enter the name or KEGG identifier of t......

• • How to Handle log2foldchange with Zero FPKM in KEGG Enrichment Analysis?

  When performing KEGG enrichment analysis, if the FPKM value in the control or treatment group is 0, the log2foldchange can be handled by the following methods:   Add a Small Constant Value Before calculating log2foldchange, add a small constant (e.g., 0.1 or 0.01) to all FPKM values. This avoids log2(0) while preserving the relative nature of the data. This method is suitable when many genes have low expression.   Use Estimated Values Replace a 0 FPKM value with an estimated value, such as the lower d......