Principal Component Analysis (PCA) in Biological Research

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-22

Key Takeaways

Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms high-dimensional biological data (e.g., gene expression, proteomics) into a lower-dimensional space defined by principal components, which capture the largest sources of variance.
Before PCA, data standardization (e.g., to mean zero and unit variance) is critical to prevent variables with larger magnitudes from dominating the analysis, ensuring all measured features contribute equally.
PCA is an exploratory tool used for visualizing sample relationships, detecting batch effects, identifying outliers, and generating hypotheses, but it does not perform statistical hypothesis testing or identify differentially expressed genes.
Interpretation relies on score plots to visualize sample groupings and loading plots to identify which original variables (e.g., genes, proteins) drive the observed separation or variation among samples.
PCA assumes linear relationships among variables; for data with significant nonlinear interactions, alternative methods like t-SNE or UMAP may be more appropriate for visualization.
Reproducibility requires meticulous documentation of data provenance, preprocessing steps (normalization, transformation, scaling), missing value handling, and PCA parameters used.

Quick Answer

PCA reduces high-dimensional biological data to a few principal components that capture the largest sources of variation, enabling visualization and pattern detection across samples or genes.
Standardize your data matrix before running PCA, then interpret component loadings to identify which variables drive sample separation and use score plots to examine biological groupings.
PCA is an exploratory tool, not a statistical test, and results depend on data scaling, missing-value handling, and the assumption of linear relationships among variables.

What PCA Does in Biological Research

Principal component analysis is a dimensionality reduction technique that transforms a large set of correlated variables into a smaller set of uncorrelated variables called principal components. Each principal component is a linear combination of the original variables, and the components are ordered so that the first component captures the greatest possible variance in the data, the second captures the next greatest variance subject to being orthogonal to the first, and so on.

In biological research, PCA is widely used for gene expression microarrays, RNA sequencing, proteomics, metabolomics, and other high-throughput assays where the number of measured variables often far exceeds the number of samples. A typical RNA-seq experiment might measure expression levels for 20,000 genes across 50 samples. Visualizing such data directly is impossible, and many statistical methods fail when the number of variables exceeds the number of observations. PCA addresses this by projecting the samples into a low-dimensional space where the dominant patterns of variation become visible.

The method is fundamentally exploratory. It does not test hypotheses, assign statistical significance, or identify differentially expressed genes. Instead, it reveals structure in the data, such as clusters of samples that share similar molecular profiles, outliers that may represent technical artifacts or biological anomalies, and gradients of variation that correlate with experimental conditions. Researchers use PCA to assess data quality, detect batch effects, identify sample mislabeling, and generate hypotheses about the biological factors driving variation.

PCA assumes that the relationships among variables are linear. When the underlying biology involves nonlinear interactions, PCA may not capture the full structure, and methods such as t-distributed stochastic neighbor embedding (t-SNE) or uniform manifold approximation and projection (UMAP) may be more appropriate for visualization. However, PCA remains the standard first step in many bioinformatics workflows because it is computationally efficient, mathematically well understood, and provides interpretable results.

The Mathematical Basis of PCA

PCA operates on a data matrix where rows represent samples and columns represent variables. For a gene expression dataset, each row is a sample and each column is a gene. The goal is to find a new set of axes, the principal components, that maximize the variance of the data when projected onto those axes.

The first principal component is the direction in the variable space along which the data vary the most. Mathematically, this is the eigenvector of the covariance matrix of the data that corresponds to the largest eigenvalue. The second principal component is the direction orthogonal to the first that captures the next largest variance, and it corresponds to the second largest eigenvalue of the covariance matrix. Each subsequent component follows the same pattern.

The eigenvalues indicate the amount of variance captured by each component. The proportion of total variance explained by a component is the eigenvalue divided by the sum of all eigenvalues. Researchers commonly report the percentage of variance explained by the first two or three components to indicate how much of the total variation is represented in the score plot.

The loadings are the coefficients of the original variables in each principal component. A loading with a large absolute value indicates that the corresponding variable contributes strongly to that component. In gene expression data, genes with high loadings on the first component are the genes that most strongly drive the separation of samples along that component. Interpreting loadings helps identify the biological processes associated with the observed sample groupings.

The scores are the coordinates of the samples in the new principal component space. Each sample has a score for each component, and plotting the scores for the first two components produces a score plot that shows the relationships among samples. Samples that are close together in the score plot have similar molecular profiles, while samples that are far apart are dissimilar.

Data Preparation Before PCA

The quality of PCA results depends heavily on how the data are prepared before analysis. Standardization is the most important preprocessing step. When variables are measured on different scales, such as gene expression counts versus protein concentrations, the variables with larger magnitudes will dominate the PCA. Standardizing each variable to have a mean of zero and a standard deviation of one ensures that all variables contribute equally to the analysis.

For gene expression data, standardization is typically applied after appropriate normalization. RNA sequencing data are often normalized using methods that account for differences in sequencing depth across samples, such as counts per million (CPM) or transcripts per million (TPM). Microarray data may be normalized using methods such as robust multi-array average (RMA). The choice of normalization method can affect PCA results, and researchers should document the normalization approach in their methods.

Missing values are a common problem in biological data. PCA requires a complete data matrix, so missing values must be handled before analysis. Options include removing samples or genes with excessive missing values, imputing missing values using methods such as k-nearest neighbors or singular value decomposition, or using PCA algorithms that can accommodate missing data. The choice of imputation method can influence the results, and the approach should be reported.

Outliers can have a disproportionate influence on PCA results because the method is based on variance, and extreme values inflate variance. Researchers should examine the data for outliers before running PCA, using methods such as box plots, principal component diagnostics, or robust PCA variants. Outliers may represent genuine biological variation, technical artifacts, or sample processing errors, and the decision to remove them should be documented and justified.

A Worked Biological Example

Consider a hypothetical gene expression experiment with 30 samples from three tissue types: liver, kidney, and muscle. The data matrix has 30 rows and 15,000 columns, one for each gene. The goal is to determine whether the samples cluster by tissue type and which genes drive the separation.

The first step is to normalize the raw count data and standardize each gene across samples. After standardization, the covariance matrix is computed, and the eigenvectors and eigenvalues are extracted. The first two components might explain 35 percent and 18 percent of the total variance, respectively. The score plot of the first two components would show the 30 samples, and if the tissue types are biologically distinct, the samples should form three clusters.

The loadings for the first component would identify the genes that contribute most to the separation between the clusters. If the first component separates breast samples from kidney and lung samples, the genes with the highest loadings on the first component are the genes most strongly associated with breast tissue identity. These genes can be examined for biological relevance, such as known tissue-specific markers.

The example illustrates the core workflow: prepare the data, run PCA, examine the score plot for sample groupings, and examine the loadings to identify the variables driving the groupings. This workflow is applicable to any high-dimensional biological dataset, including proteomics, metabolomics, and single-cell data.

At a Glance

Step	Action	Purpose	Common Output
Data preparation	Normalize and standardize the data matrix	Ensure variables contribute equally and remove technical variation	Standardized matrix with mean zero and unit variance
Missing value handling	Impute or remove missing entries	Produce a complete matrix for PCA	Complete data matrix
PCA computation	Compute covariance matrix and extract eigenvectors	Reduce dimensionality and identify variance structure	Eigenvalues, loadings, scores
Visualization	Plot scores and loadings	Examine sample groupings and variable contributions	Score plot, loading plot, biplot
Interpretation	Identify clusters and driver variables	Generate biological hypotheses	Candidate genes or features for follow-up
Validation	Check with clustering or permutation methods	Confirm that the observed structure is robust	Stability metrics or cluster assignments

Data Preparation and Quality Control

The quality of PCA results depends on the quality of the input data. Before running PCA, researchers should perform several quality control steps to ensure that the data are suitable for analysis.

First, examine the distribution of each variable. Variables with extreme values or skewed distributions may need transformation. For gene expression data, a log transformation is often applied to reduce the influence of highly expressed genes and to make the data more symmetric. The choice of transformation should be documented.

Second, check for batch effects. Batch effects are systematic technical variations that arise from processing samples in different batches, on different days, or with different reagents. PCA is a sensitive tool for detecting batch effects because samples from the same batch often cluster together in the score plot. If batch effects are present, researchers may need to apply batch correction methods before PCA or include batch as a covariate in downstream analyses.

Third, assess the number of components to retain. The goal is to capture the majority of the variance with as few components as possible. A common approach is to examine a scree plot, which shows the eigenvalues in decreasing order. The point where the eigenvalues level off, sometimes called the elbow, indicates the number of components that capture the most variance. Another approach is to retain components that explain a cumulative percentage of variance, such as 80% or 90%, though the threshold depends on the research question.

Fourth, consider the sample size. PCA requires a sufficient number of samples to produce stable estimates of the covariance matrix. When the number of samples is small relative to the number of variables, the covariance matrix may be poorly estimated, and the results may not be reproducible. In such cases, researchers may need to use regularized PCA or other methods designed for high-dimensional data.

Interpreting Loadings and Scores

The loadings and scores are the two main outputs of PCA, and both require careful interpretation.

Loadings indicate the contribution of each original variable to each principal component. A loading close to zero means the variable contributes little to that component. A loading with a large positive or negative value means the variable contributes strongly. In the context of gene expression data, genes with high loadings on a component are the genes that most strongly influence the position of samples along that component. These genes are candidates for further investigation, such as pathway analysis or validation experiments.

The sign of the loading indicates the direction of the relationship. A gene with a positive loading on the first component is positively correlated with the first component, meaning that samples with high expression of that gene will have high scores on the first component. A gene with a negative loading has the opposite relationship. The sign is arbitrary in the sense that the direction of the component can be flipped without changing the interpretation, but the relative signs of the loadings are meaningful.

Scores indicate the position of each sample in the principal component space. Samples with similar scores are similar in their molecular profiles. In the score plot, the distance between samples reflects their similarity. Samples that cluster together may share a biological condition, such as a disease state or a tissue type, or they may share a technical artifact, such as a batch effect.

The biplot combines the score plot and the loading plot in a single figure. The samples are shown as points, and the variables are shown as arrows. The direction and length of each arrow indicate the contribution of the variable to the components. A biplot allows researchers to see which variables are associated with which samples. For example, a gene with an arrow pointing toward a cluster of samples indicates that the gene is highly expressed in those samples.

Common Failure Patterns in PCA

PCA is a powerful tool, but it is often misused. Several common failure patterns can lead to incorrect conclusions.

The first failure pattern is applying PCA to unstandardized data. When variables are on different scales, the variables with larger magnitudes dominate the analysis, and the results reflect the scaling instead of the biology. Standardization is essential unless all variables are measured on the same scale and have similar variances.

The second failure pattern is overinterpreting the results. PCA is an exploratory method, and the patterns observed in the score plot may not be statistically significant. The clusters may be driven by a few outliers, or they may be an artifact of the data preparation. Researchers should validate the observed structure using other methods, such as clustering or permutation tests.

The third failure pattern is ignoring the variance explained. The first two components may explain only a small fraction of the total variance, and the score plot may not represent the data well. Researchers should report the percentage of variance explained by each component and consider whether the components capture enough variation to support the conclusions.

The fourth failure pattern is using PCA to infer causality. PCA identifies patterns of variation, but it does not identify the causes of those patterns. The separation of samples into clusters may be driven by the experimental condition, but it may also be driven by batch effects, sample processing, or other technical factors. Researchers should examine the data for potential confounders before attributing the pattern to biology.

The fifth failure pattern is using PCA on data with nonlinear relationships. PCA assumes that the relationships among variables are linear. If the biology involves nonlinear interactions, PCA may not capture the structure, and other methods may be more appropriate.

Reproducibility and Reporting

Reproducibility is a core principle of scientific research, and PCA is no exception. The results of PCA depend on the data preparation, the scaling method, the missing value handling, and the software used. To ensure that the results can be reproduced, researchers should document all of these steps in their methods.

The data should be deposited in a public repository where possible, and the analysis code should be made available. The NIH Data Management and Sharing Policy describes the expectations for sharing data and code in NIH-funded research. The policy requires researchers to plan for data management and sharing, and to describe the data and the analysis in a data management and sharing plan. The plan should address the types of data, the data standards, the data sharing, and the data preservation.

The reporting of PCA results should follow the relevant reporting guidelines. The EQUATOR Network provides a collection of reporting guidelines for different study types, and researchers should select the appropriate guideline for their study design. The guidelines help ensure that the methods and results are reported transparently and completely.

The publication ethics of the results should also be considered. The Committee on Publication Ethics (COPE) provides core practices for authorship, peer review, data, conflicts of interest, and misconduct. Researchers should ensure that the data are reported accurately, that the analysis is described honestly, and that the results are not overinterpreted.

Software Options for PCA

PCA is implemented in most statistical software packages, and the choice of software depends on the researcher's familiarity and the data type.

R is a popular choice for biological data analysis. The base R function prcomp performs PCA on a data matrix, and the princomp function is an alternative. The factoextra package provides functions for visualizing PCA results, including score plots, loading plots, and biplots. The stats package includes the prcomp function, and the ggplot2 package can be used to create custom plots.

Python is also widely used. The scikit-learn library provides the PCA class, which can be used to fit the PCA model and transform the data. The matplotlib and seaborn libraries can be used to create plots. The pandas library is used for data manipulation.

The choice of software does not affect the mathematical results, but the implementation details can affect the output. For example, the prcomp function in R uses singular value decomposition, while the princomp function uses the covariance matrix. The results are similar, but the numerical values may differ slightly.

PCA in Different Biological Data Types

PCA is applied to a wide range of biological data types, and the specific considerations vary by data type.

In gene expression data, PCA is used to identify sample clusters, detect batch effects, and identify genes that drive the variation. The data are typically log-transformed and standardized before PCA. The loadings can be used to identify the genes that are most strongly associated with the observed patterns.

In proteomics data, PCA is used to identify proteins that are differentially expressed between conditions. The data are often normalized using methods such as label-free quantification or tandem mass tag (TMT) labeling. The loadings can be used to identify the proteins that drive the separation between conditions.

In metabolomics data, PCA is used to identify metabolites that are associated with a condition. The data are often scaled to unit variance to give all metabolites equal weight. The loadings can be used to identify the metabolites that are most strongly associated with the observed patterns.

In single-cell data, PCA is often used as a preprocessing step before clustering or other downstream analyses. The data are typically normalized and log-transformed, and PCA is used to reduce the dimensionality of the data before clustering. The number of components retained is often determined by the elbow of the scree plot or by the percentage of variance explained.

Limitations of PCA

PCA is a powerful tool, but it has several limitations that researchers should be aware of.

First, PCA assumes linear relationships among variables. When the relationships are nonlinear, PCA may not capture the structure in the data. Other methods, such as t-SNE or UMAP, may be more appropriate for nonlinear data.

Second, PCA is sensitive to outliers. A few extreme samples can have a large influence on the principal components, and the results may be driven by the outliers instead of by the majority of the data. Researchers should examine the data for outliers and consider robust PCA methods.

Third, PCA does not account for the class labels or the experimental design. PCA is an unsupervised method, and it does not use the information about the sample groups. The observed clusters may not correspond to the experimental groups, and the results should be interpreted in the context of the experimental design.

Fourth, PCA does not provide a measure of statistical significance. The variance explained by a component is a descriptive measure, and it does not indicate whether the component is statistically significant. Researchers should use other methods to test the significance of the observed patterns.

Fifth, PCA is sensitive to the scaling of the data. The choice of scaling can affect the results, and the results should be interpreted in the context of the scaling method used.

Professional Escalation Criteria

PCA is a standard tool in bioinformatics, but there are situations where the results should be reviewed by a statistician or a bioinformatics specialist.

If the PCA results are unstable, meaning that the results change substantially when a few samples are removed, the analysis should be reviewed. The instability may indicate that the data are not suitable for PCA, or that the number of samples is too small.

If the score plot shows a pattern that is not consistent with the experimental design, the analysis should be reviewed. The pattern may indicate a batch effect, a technical artifact, or a problem with the data.

If the loadings are difficult to interpret, the analysis should be reviewed. The loadings may be driven by a few variables, or the variables may be highly correlated, and the interpretation may be difficult.

If the results are to be used for a publication or a regulatory submission, the analysis should be reviewed by a statistician. The statistician can ensure that the analysis is appropriate and that the results are reported correctly.

A Practical Decision Framework for PCA Component Retention and Interpretation

Choosing how many principal components to retain and how much weight to give the resulting patterns is a recurring decision point in biological data analysis. Researchers often default to the scree plot elbow or a fixed variance threshold, but these heuristics can mislead when the data contain technical noise, batch structure, or weak biological signals. This section provides a structured decision framework that connects component retention to the specific research question, a record system for tracking PCA decisions, and a troubleshooting method for common interpretation failures.

The Retention Decision Tree

The number of components you retain should depend on what you plan to do with the PCA output. A single fixed rule, such as retaining components until 80 percent of variance is explained, does not serve all research goals equally. The decision tree below organizes the choice by downstream use.

Branch 1: Visualization only. If the goal is to inspect sample relationships in a score plot, retain the first two or three components and report the cumulative variance they explain. This branch is appropriate for quality control checks, such as detecting batch effects or sample mislabeling, where the purpose is to see whether known groupings appear. The variance explained by the first two components should be reported alongside the plot so that readers can judge how much of the total variation the visualization represents. If the first two components explain less than 20 percent of the total variance, the score plot may be a poor representation of the data, and the interpretation should be cautious.

Branch 2: Downstream analysis input. If the scores will be used as input to clustering, classification, or regression, the retention decision affects the downstream results. Retaining too few components discards biological signal, while retaining too many reintroduces noise. A common approach is to retain components that explain a cumulative percentage of variance, such as 70 to 90 percent, but the threshold should be justified in the methods. An alternative is to use a permutation-based approach that compares the observed eigenvalues to those obtained from randomized data. Components whose eigenvalues exceed the null distribution are retained. This approach is more conservative and is appropriate when the downstream analysis is sensitive to noise.

Branch 3: Variable selection and interpretation. If the loadings are used to identify candidate genes, proteins, or metabolites for follow-up, the retention decision should focus on the interpretability of the components. A component that explains a small fraction of the total variance but has a clear biological interpretation may still be worth examining. Conversely, a component that explains a large fraction of variance but has loadings spread thinly across many variables may not provide useful candidates. In this branch, the decision is guided by the biological coherence of the loadings instead of by a variance threshold alone.

Branch 4: Quality control and batch detection. When PCA is used to detect batch effects or sample processing artifacts, the goal is to identify components that separate samples by technical factors instead of by biology. In this case, the score plot should be colored by batch, processing date, or reagent lot. If a component separates samples by a technical factor, that component should be examined for the variables that drive the separation. The decision to correct for batch effects should be based on the strength of the technical separation relative to the biological separation.

A Record System for PCA Decisions

PCA results are sensitive to the choices made during preprocessing and analysis. Without a record of those choices, the analysis cannot be reproduced, and the interpretation cannot be audited. A structured record system should capture the following elements for every PCA run.

Data provenance. Record the source of the data, the version of the data file, and the date the data were exported. If the data were filtered, record the filtering criteria, such as the minimum expression threshold or the minimum number of samples with nonzero counts. The NIH Data Management and Sharing Policy describes the expectations for documenting data provenance and analysis steps in NIH-funded research. The policy requires that data management plans address the types of data, the data standards, and the data preservation.

Preprocessing steps. Record the normalization method, the transformation applied, and the scaling method. For gene expression data, record whether the data were log-transformed and whether the transformation was applied to counts per million, transcripts per million, or another normalized measure. Record the standardization method, such as centering and scaling to unit variance, and whether the standardization was applied across samples or across variables.

Missing value handling. Record the number of missing values in the data, the method used to handle them, and the parameters of the method. If imputation was used, record the imputation method, such as k-nearest neighbors or singular value decomposition, and the number of neighbors or components used. If samples or variables were removed, record the criteria for removal and the number removed.

PCA parameters. Record the software and the function used, such as the prcomp function in R or the PCA class in scikit-learn. Record the number of components computed, the centering and scaling options, and the algorithm used, such as singular value decomposition or the covariance matrix method. Record the version of the software and the version of the relevant packages.

Retention decision. Record the method used to determine the number of components retained, such as the scree plot elbow, the cumulative variance threshold, or a permutation test. Record the number of components retained and the cumulative variance explained by those components. Record the rationale for the decision, including any biological considerations.

Validation results. Record the results of any validation steps, such as the stability of the components when samples are removed, the agreement with clustering results, or the results of a permutation test. Record the date of the validation and the version of the data used.

The record should be maintained in a format that is accessible to collaborators and reviewers. A plain text file, a spreadsheet, or a version-controlled script that documents each step is acceptable. The record should be stored with the analysis code and the data, and it should be referenced in the methods section of any publication.

Troubleshooting Method for Common PCA Failures

When PCA results do not match expectations, the cause is often in the preprocessing or the data structure instead of in the PCA itself. The following troubleshooting method addresses the most common failure patterns.

Failure pattern 1: The score plot shows a single dominant cluster with a few outliers. This pattern often indicates that the data is dominated by a few samples with extreme values. The outliers may be technical artifacts, such as samples with low sequencing depth or high contamination, or they may be genuine biological outliers. The first step is to examine the loadings for the first component to identify the variables that drive the separation. If the loadings are dominated by a few variables, the outliers may be driven by those variables. The next step is to examine the sample metadata to determine whether the outliers share a technical factor, such as a batch or a processing date. If the outliers are technical, they should be removed or corrected. If they are biological, they should be retained and the interpretation should account for them.

Failure pattern 2: The score plot shows a gradient instead of distinct clusters. A gradient may indicate a continuous biological process, such as a developmental time course or a disease progression, or it may indicate a technical gradient, such as a sequencing depth gradient or a time-dependent batch effect. The first step is to color the score plot by the known experimental variables, such as time point, dose, or batch. If the gradient aligns with a technical variable, the data may need batch correction. If the gradient aligns with a biological variable, the gradient may be the primary source of variation, and the analysis should focus on the variables that drive the gradient.

Failure pattern 3: The first two components explain very little variance. When the first two components explain less than 10 percent of the total variance, the score plot may not represent the data well. The data may have many independent sources of variation, or the variables may be largely uncorrelated. The first step is to examine the scree plot to see whether the eigenvalues level off gradually or whether there is a clear elbow. If the eigenvalues level off gradually, the data may not have a strong low-dimensional structure, and PCA may not be the most informative method. The next step is to consider whether the data should be transformed or filtered. For gene expression data, filtering out genes with low expression or low variance can reduce the number of uninformative variables and improve the signal.

Failure pattern 4: The loadings are difficult to interpret. When the loadings are spread across many variables with no clear pattern, the component may be capturing a technical artifact or a combination of many small effects. The first step is to examine the loadings for the top variables and to check whether they share a biological function or a pathway. If the top variables are not biologically coherent, the component may be driven by technical variation. The next step is to examine the score plot for the component to see whether the samples separate by a technical factor. If the separation is technical, the component should be examined for batch effects.

Failure pattern 5: The PCA results are not stable when samples are removed. Instability indicates that the covariance matrix is not well estimated, which can happen when the number of samples is small relative to the number of variables. The first step is to examine the number of samples and the number of variables. If the number of samples is small, the results may be unstable, and the analysis should be reviewed by a statistician. The next step is to consider whether a regularized PCA method or a different dimensionality reduction method is more appropriate.

Validation of PCA Results

PCA is an exploratory method, and the patterns observed in the score plot should be validated before they are used to support conclusions. The validation approach depends on the research question and the data type.

Stability analysis. A common validation approach is to remove a small number of samples and rerun the PCA to see whether the components and the score plot remain similar. If the results change substantially, the analysis is not stable. The stability analysis should be documented in the record system.

Agreement with clustering. The score plot can be compared with the results of a clustering method, such as k-means or hierarchical clustering. If the clusters in the score plot agree with the clusters from the clustering method, the structure is more likely to be robust. The agreement can be quantified using the adjusted Rand index or the silhouette score.

Permutation testing. A permutation test can be used to assess whether the variance explained by a component is greater than expected by chance. The data matrix is permuted by shuffling the values within each variable, and the PCA is rerun on the permuted data. The observed eigenvalues are compared with the distribution of eigenvalues from the permuted data. If the observed eigenvalue is larger than the 95th percentile of the permuted distribution, the component is considered significant. The permutation test should be described in the methods.

Reporting guidelines. The EQUATOR Network provides a collection of reporting guidelines for different study types. The appropriate guideline should be selected based on the study design, and the PCA methods and results should be reported in accordance with the guideline. The reporting should include the data preparation steps, the retention decision, the validation results, and the limitations of the analysis.

Professional Escalation Criteria

The decision framework and the troubleshooting method are intended to help researchers make informed decisions, but there are situations where the analysis should be reviewed by a statistician or a bioinformatics specialist.

The decision framework, the record system, and the troubleshooting method should be used together. The record system documents the decisions, the troubleshooting method identifies the causes of the failures, and the decision framework guides the choices. The combination of the three provides a practical approach to PCA that is reproducible and auditable.

Frequently Asked Questions

What is the difference between PCA and factor analysis?

PCA and factor analysis are both dimensionality reduction methods, but they have different goals. PCA aims to capture the maximum variance in the data, while factor analysis aims to identify the underlying latent factors that explain the correlations among the variables. PCA is a data transformation, while factor analysis is a statistical model.

How many principal components should I retain?

The number of components to retain depends on the goal of the analysis. A common approach is to examine the scree plot and retain the components before the elbow. Another approach is to retain the components that explain a cumulative percentage of variance, such as 80% or 90%. The choice should be documented in the methods.

Should I standardize the data before PCA?

Yes, standardization is recommended when the variables are measured on different scales. Standardization ensures that all variables contribute equally to the analysis. If the variables are on the same scale, standardization may not be necessary, but it is often still recommended.

Can PCA be used for differential expression analysis?

PCA is not a differential expression analysis method. It is an exploratory tool that identifies patterns in the data. Differential expression analysis requires statistical tests that compare the expression levels between groups, such as the t-test or the DESeq2 method.

What is the difference between PCA and t-SNE?

PCA is a linear method that captures the maximum variance in the data, while t-SNE is a nonlinear method that preserves the local structure of the data. PCA is faster and more interpretable, while t-SNE is better for visualizing complex nonlinear patterns.

How do I interpret the biplot?

The biplot shows the samples as points and the variables as arrows. The direction and length of each arrow indicate the contribution of the variable to the components. The samples that are close together are similar, and the variables that point toward a cluster of samples are associated with those samples.

What should I do if the PCA results are not reproducible?

If the PCA results are not reproducible, the data preparation and the analysis code should be examined. The results may be affected by the scaling method, the missing value handling, or the software version. The analysis should be documented in detail, and the code should be made available.

Is PCA appropriate for single-cell data?

PCA is often used as a preprocessing step for single-cell data. The data are normalized and log-transformed, and PCA is used to reduce the dimensionality before clustering or other methods. The number of components retained is often determined by the variance explained or by the elbow of the scree plot.

Using the Evidence

Source	Best use in this topic	Important limitation
Research Methods Resources	official guidance	Check the linked page for current local requirements
EQUATOR Network	official guidance	Check the linked page for current local requirements
Core Practices	official guidance	Check the linked page for current local requirements

Related Bioinformatics Guides

Related Clinical & Scientific Guides

References and Further Reading

Research Methods Resources. National Library of Medicine.
EQUATOR Network. EQUATOR Network.
Core Practices. Committee on Publication Ethics.
NIH Grants and Funding. National Institutes of Health.
ORCID for Researchers. ORCID.
Data Management and Sharing Policy. National Institutes of Health.
NCBI Data Resources. National Center for Biotechnology Information.
EMBL-EBI Training. European Bioinformatics Institute.
Lipidomic data analysis: tutorial, practical guidelines and applications.. Analytica chimica acta, 2015.
High-throughput single cell data analysis - A tutorial.. Analytica chimica acta, 2021.
Elastic Statistical Shape Analysis of Biological Structures with Case Studies: A Tutorial.. Bulletin of mathematical biology, 2019.

This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.