# Correlation Analysis in Python for Bioinformatics

Correlation analysis measures how two variables move together, and in bioinformatics it is the numerical backbone of co-expression networks, [biomarker discovery](/knowledge/molecular-biology/biomarker-discovery), omics integration, and validation plots. The one-sentence difference that matters most: a correlation coefficient describes shared variation between two measured signals, while causation requires an entirely separate body of evidence that correlation never provides. The rule of thumb for choosing a tool is scale plus question. For a handful of gene pairs or a small clinical table, pandas and SciPy are enough. For a full co-expression matrix across tens of thousands of genes, vectorized NumPy or pandas works with careful memory planning. For millions of features, purpose-built tools such as PyNetCor exist precisely because naive approaches fail [1]. And if batch effects threaten your network, you need a correction method designed for covariance, not just for expression values [2].

This guide walks through every major python correlation analysis option with real code, compares them on the criteria that actually decide projects, and shows where each one breaks down.

## What Correlation Analysis Does in Bioinformatics

Correlation quantifies the strength and direction of a monotonic or linear relationship between two variables. The two coefficients you will use most are:

- **Pearson correlation (r)** measures linear association. It ranges from -1 to +1. A value of 0.9 between two genes across 50 samples means their log-expression values rise and fall together almost linearly.
- **Spearman correlation (rho)** measures monotonic association on ranks. It is robust to outliers and to relationships that are monotonic but not linear, which makes it the default choice for noisy omics data.

A third metric deserves a warning label. **Matthew's Correlation Coefficient (MCC)** appears constantly in bioinformatics papers, including machine learning studies of clinical predictors [3], but it is a classification metric for confusion matrices, not a measure of association between continuous variables. Searching "correlation" and landing on MCC is one of the most common mix-ups for newcomers to python for bioinformatics.

Typical applications include:

- **Co-expression networks.** Correlations between [gene expression](/blog/guides/gene-expression) profiles across samples are used to infer co-expression networks, which then feed gene regulatory network inference and module detection [2].
- **Multi-omics integration.** Matching metabolite abundances against transcript abundances, or microbial taxa against host phenotypes.
- **Method validation.** A new measurement platform is considered acceptable when its output correlates highly with a reference standard. The TWARDIS pipeline for C. elegans image analysis reported a 0.999 correlation with manual segmentation, which is the kind of number that validates an automated method against human annotation [4].
- **Spatial and single-[cell biology](/blog/careers/cell-biology).** Correlation analysis of tumor microenvironments, computed over spatial neighborhoods, is now practical through cloud-based query systems that expose R and Python notebooks over large atlases [5].

## The Python Ecosystem for Bioinformatics

Python for bioinformatics rests on a layered stack. At the bottom, Biopython handles biological file formats, sequence objects, and NCBI data access. A practical biopython tutorial will teach you to parse FASTA and GenBank files, run BLAST queries, and fetch records from Entrez, but Biopython itself is not where correlation math lives. Correlation lives one layer up, in NumPy, pandas, SciPy, and statsmodels.

The typical modern pipeline mixes these layers freely. One published validation study of a fully automated Python reporting pipeline used pandas for data handling, scikit-learn for clustering, statsmodels for statistics, and python-docx for report generation, with correlation analysis embedded as a standard analysis step [6]. That pattern, where correlation sits alongside [preprocessing](/blog/data-analysis/what-is-data-preprocessing), clustering, and reporting inside a single Python environment, is the dominant workflow style in the field.

Domain-specific packages extend the stack. pyMeSHSim handles biomedical named entity recognition and semantic similarity between MeSH terms, which is a different kind of "correlation" over ontology concepts rather than over numeric vectors [7]. Packages for molecular dynamics, imaging, and single-cell analysis each embed correlation at the layer where their data live, which the next sections cover.

## Core Comparison Table

The table below is the core of this article. It compares the python correlation analysis options on the criteria that decide real projects: what each computes, the scale it handles, whether it gives you p-values and false discovery rate control, and where it fails.

| Option | What it computes | Practical scale | P-values / FDR | Best for | Main caveat |
|--|--|--|--|--|--|
| `pandas.DataFrame.corr()` | Pearson, Spearman, or Kendall matrix over columns | Thousands of rows and columns, limited by RAM | No | Fast exploration of sample-by-feature tables | Dense matrix output consumes memory fast, no significance testing |
| `scipy.stats.pearsonr` / `spearmanr` | One coefficient plus exact p-value per pair | Pairwise, or slow loops over many pairs | Yes, per pair | Testing a specific hypothesis about one pair | Looping over millions of pairs in Python is unusably slow |
| `numpy.corrcoef` | Pearson matrix, C-backed | Fast for matrices that fit in memory | No | Embedding in NumPy-heavy pipelines, image and signal work | Pearson only, no p-values |
| statsmodels | Correlations plus regression-based association with covariates | Small to medium tables | Yes, through test framework | Adjusting for covariates, formal inference | Heavier API, not designed for full omics matrices |
| PyNetCor [1] | Full correlation coefficient matrix or top-k correlation search, with linear-interpolation P-values and FDR control | Large-scale and high-dimensional data, millions of features | Yes, with FDR control | Genome-scale co-expression, top-k edge extraction in multi-omics | Specialized tool with its own interface, overkill for small tables |
| PyBootNet [8] | Correlation networks with bootstrap-based robustness assessment | Small to medium networks | Bootstrap-based, not analytic | Assessing whether a network is statistically robust, comparing networks between conditions | Bootstrap resampling multiplies compute time |
| COBRA [2] | Batch-corrected co-expression matrix from a conditional covariance estimate | Gene expression scale, built for batched studies | Inherited from downstream testing | Expression datasets where standard batch correction leaves spurious differential co-expression | Requires batch metadata, focused on expression covariance |

Two additional tools belong in the mental map even though they are not general-purpose correlation engines. The Membrane Kymograph Generator ships built-in correlation analysis for multi-channel live-cell imaging data behind a native Python API, which matters if your question is about signal kinetics along a cell boundary rather than gene expression [9]. The condensate analysis pipeline pairs correlation plots with PCA and scree plots to describe morphological heterogeneity in biomolecular condensates, showing how correlation slots into a broader multivariate framework [10].

## Option by Option: How Each One Works

### pandas: the default starting point

`pandas.DataFrame.corr()` computes a correlation matrix over the columns of a DataFrame. Suppose you have a log-transformed expression matrix with genes as rows and samples as columns.

```python
import pandas as pd

expr = pd.read_csv("counts_log2.tsv", sep="\t", index_col=0)

# Correlate two genes directly
r = expr.loc["BRCA1"].corr(expr.loc["BRCA2"], method="pearson")

# Full gene-by-gene matrix (transpose so genes become columns)
gene_corr = expr.T.corr(method="spearman")
```

**Pros:**
- One line for a full matrix, in either Pearson or Spearman flavor.
- Handles missing values pairwise by default, which is convenient for messy clinical tables.
- Integrates directly with seaborn clustermaps and matplotlib heatmaps.

**Cons:**
- No p-values. You get coefficients with no significance attached.
- The output is a dense square matrix. With 30,000 genes, that is a 30,000 by 30,000 float64 matrix, roughly 7.2 GB before you compute anything else. With a million features it is physically impossible on ordinary hardware, which is exactly the problem PyNetCor was built to solve [1].

### SciPy: hypothesis testing for specific pairs

`scipy.stats.pearsonr` and `scipy.stats.spearmanr` each return a coefficient and a p-value. This is what you reach for when the question is narrow: does my RT-qPCR validation fold-change correlate with the RNA-seq log2 fold-change across 20 genes?

```python
from scipy import stats

rnaseq_fc = [2.1, -0.8, 1.4, 3.2, 0.5, -1.1, 2.8, 0.9]
qpcr_fc   = [1.9, -0.6, 1.5, 2.7, 0.7, -0.9, 2.5, 1.1]

r, p = stats.pearsonr(rnaseq_fc, qpcr_fc)
rho, p_s = stats.spearmanr(rnaseq_fc, qpcr_fc)
print(f"Pearson r = {r:.3f}, p = {p:.2e}")
print(f"Spearman rho = {rho:.3f}, p = {p_s:.2e}")
```

These same two tests, Pearson and Spearman correlation, were among the inferential statistics validated in a systematic study of AI-assisted analysis, which tells you how routinely they appear in applied research workflows [11].

**Pros:**
- Exact p-values, including exact handling of tied ranks for Spearman in recent SciPy versions.
- The right tool whenever n is small enough that per-pair testing is honest and feasible.

**Cons:**
- Scaling to a full omics matrix through a Python loop is orders of magnitude slower than matrix-based approaches. If you need p-values for every pair at scale, you need a tool that estimates them efficiently, which is PyNetCor's core contribution [1].

### NumPy: the fast, silent workhorse

`numpy.corrcoef` computes a Pearson correlation matrix with C-level speed. It is the standard choice inside signal processing, imaging, and simulation code.

```python
import numpy as np

# rows are variables, columns are observations
r_matrix = np.corrcoef(expr.values)
```

**Pros:**
- Fastest general option for an in-memory Pearson matrix.
- Zero friction in NumPy-native pipelines, such as computing time-dependent correlation factors between residue pairs in molecular dynamics analysis, the approach used in steered molecular dynamics packages [12].

**Cons:**
- Pearson only. If you need Spearman at matrix scale, you rank-transform first with `scipy.stats.rankdata` along the sample axis, then call `np.corrcoef` on the ranked matrix.
- No missing-value handling. A single NaN poisons an entire row.
- No p-values, no FDR.

### statsmodels: correlation in the context of covariates

Plain correlation answers "do these two variables move together." Often the honest question is "do they move together after accounting for age, sex, batch, or treatment." statsmodels puts correlation inside a regression framework where covariates can be modeled explicitly. The automated pipeline cited earlier used statsmodels as its statistical engine alongside pandas and scikit-learn, with correlation analysis as one module among several [6].

```python
import statsmodels.api as sm

# partial out covariates, then correlate residuals
model = sm.OLS(gene_a, covariates).fit()
resid_a = model.resid
model_b = sm.OLS(gene_b, covariates).fit()
resid_b = model_b.resid
r_adjusted = np.corrcoef(resid_a, resid_b)[0, 1]
```

**Pros:**
- Formal inference with covariate adjustment.
- Connects correlation to the broader regression toolkit without switching languages.

**Cons:**
- Not built for matrix-scale omics. Residualizing 30,000 genes one at a time is slow compared to a linear-algebra approach, though the linear algebra behind partial correlation (inverting the [covariance matrix](/blog/data-analysis/what-is-covariance-matrix)) is available in NumPy if you know what you are doing.

### PyNetCor: correlation at genome scale and beyond

Modern multi-omics datasets routinely contain millions of features, and most correlation tools face computational challenges at that dimensionality. PyNetCor is a fast and scalable package for constructing correlation networks on large-scale, high-dimensional data. It provides optimized algorithms for two tasks: full correlation coefficient matrix computation, and top-k correlation search where you only retrieve the k strongest partners for each feature. It estimates P-values through a linear interpolation strategy, achieving false discovery rate control, and demonstrated a speedup of over 110 times compared to existing methods [1].

**Pros:**
- Handles feature counts where pandas and NumPy hit memory walls.
- Top-k search avoids materializing the full matrix at all, which is usually what a biological question actually needs (give me the top 50 co-expressed partners for every gene).
- Built-in FDR control, which is non-negotiable at millions of simultaneous tests.

**Cons:**
- A specialized tool with its own interface and installation path, worth learning only when scale demands it.
- For a 200-gene panel, it is unnecessary machinery.

### PyBootNet: asking whether the network itself is real

A correlation network built from 12 samples is a fragile object. PyBootNet integrates [bootstrapping](/blog/data-analysis/what-is-bootstrapping-resampling-examples) with correlation network construction in Python. It generates bootstrapped network metrics, statistically compares network metrics among datasets, and visualizes bootstrapped networks, with functions covering preprocessing, bootstrapping, correlation matrix calculation, network statistics, and visualization. It was demonstrated by comparing correlation networks from a mouse model of polycystic ovary syndrome against controls to test whether gut microbiome network structure differed between conditions [8].

**Pros:**
- Answers the question that point estimates cannot: how stable are these edges and these module statistics under resampling?
- Minimal dependencies and straightforward inputs, designed for accessibility.

**Cons:**
- Bootstrap resampling means recomputing the correlation matrix many times, so compute cost scales with the number of resamples. This is a small-to-medium network tool, not a genome-scale one.

### COBRA: when batch effects survive your batch correction

This is the subtlest failure mode in correlation-based bioinformatics, and it deserves its own entry. Standard batch correction methods adjust expression values so that each gene's mean and variance are conditionally independent of batch. That fixes differential expression analysis. It does not fix correlations. Confounders can persist in the covariance structure after standard correction, producing spurious differential co-expression, meaning artifacts that look like real changes in which genes correlate with which. COBRA (Co-expression Batch Reduction Adjustment) addresses this by estimating a conditional covariance matrix and computing a batch-corrected co-expression matrix directly, demonstrated on both synthetic and real-world gene expression data [2].

**Pros:**
- Targets the quantity you actually care about (the covariance structure) rather than the expression values.
- Solves a problem that standard pipelines silently leave in place.

**Cons:**
- Requires batch labels and a modeling step most labs do not currently run.
- Relevant specifically to expression covariance, not to every correlation problem.

## Correlation Inside Larger Workflows

Understanding the standalone tools is half the picture. The other half is seeing where correlation sits inside the workflows bioinformaticians actually run.

**Pathway-level analysis.** Single sample pathway analysis (ssPA) transforms molecule-level omics data into pathway-level scores, enabling patient-specific pathway signatures and downstream analyses such as pathway-based machine learning. Benchmarking in metabolomics compared ssGSEA, GSVA, PLAGE, z-score methods, and newer clustering and kernel approaches, showing that GSEA-based and z-score methods performed better on recall while clustering-based methods offered higher precision at moderate-to-high effect sizes [13]. The workflow implication is direct: correlating pathway scores between patients or between omics layers is often more interpretable and less noisy than correlating thousands of individual metabolites.

**Gene selection and its biases.** Differential gene selection methods typically fit each gene independently, which leaves results susceptible to systematic bias driven by co-expression patterns. TransPro is an integrated framework for benchmarking, bias correction, and reproducible visualization in differential gene selection, combining Python-side interaction-aware selection with R-side standardized analysis and visualization workflows. Its cross-dataset evaluation spanned 12 independent datasets across multiple cancer types and normal tissues [14]. The connection to correlation: because marginal gene-wise models ignore gene-gene correlation structure, an explicit awareness of the correlation matrix, including the balance of positively and negatively correlated genes, is part of doing this correctly.

**Single-cell trajectory benchmarking.** A sobering finding from benchmarking single-cell time-series methods: across eight datasets and ten state-of-the-art forecasting and optimal transport methods, many methods achieved high forecast accuracy yet failed to preserve biological signals, and most showed low lineage fidelity and underperformed a plain correlation baseline [15]. Keep that in mind whenever a complex method is proposed for a problem a correlation might already answer.

**Cloud-scale spatial analysis.** The Human Tumor Atlas Network transformed clinical and assay metadata into aggregate BigQuery tables, adapting geospatial query functions for spatial biology to enable neighborhood and correlation analysis of tumor microenvironments, demonstrated through R and Python notebooks [5]. The lesson for workflow design is that at atlas scale, correlation moves from local compute into the query layer.

**Domain-specific embedding.** Correlation appears where the data live, not only in expression tables. Steered molecular dynamics analysis computes correlation factors between residue pairs alongside time-dependent mechanical work to identify residues involved in force-induced events [12]. Biomolecular condensate pipelines combine correlation plots with PCA and scree plots, adding skewness and kurtosis for asymmetric distributions, in Jupyter-based workflows that run on desktops and HPC systems [10]. Live-cell imaging software for membrane kymographs includes built-in correlation analysis for multi-channel datasets with a native Python API for programmatic access [9].

**AI-assisted analysis.** Conversational AI with Python code interpretation has been validated for basic statistical tasks including Pearson and Spearman correlation, with accuracy improving when prompts specify the test and the variables precisely [11]. Use such assistants for drafting exploratory code, then verify every number independently.

## Worked Scenarios

![Worked calculation showing a 25000 by 25000 float64 correlation matrix needs about 5 GB, and a two million feature matrix needs about 32 terabytes.](/article-images/illustrations/python-correlation-analysis-bioinformatics-matrix-memory-size.webp)

**Scenario 1: Validating RNA-seq fold-changes with RT-qPCR.** You have log2 fold-changes for 20 genes from each platform. Scale is tiny, the question is a single association, and you need a p-value you can put in a paper. Run `scipy.stats.pearsonr` and `scipy.stats.spearmanr` on the two vectors and report both. Report Spearman if there is any chance of outlier-driven inflation.

**Scenario 2: A 25,000-gene co-expression matrix from 200 tumor samples.** The matrix is 25,000 by 25,000, about 5 GB in float64. `expr.T.corr()` in pandas will work on a machine with 32 GB of RAM but leaves little room for anything else. Switch to `np.corrcoef` on a float32 copy, or, if you only need each gene's strongest partners for network construction, run top-k search in PyNetCor and never materialize the full matrix [1]. Apply FDR control before calling any edge significant.

**Scenario 3: A metagenomic feature table with two million taxa and pathways.** A dense two-million by two-million matrix needs on the order of 32 terabytes. Naive Python options are not slow here, they are impossible. This is the exact regime PyNetCor targets, with optimized top-k search and interpolation-based P-value estimation for FDR control [1].

**Scenario 4: Comparing gut microbiome correlation networks between a disease model and controls.** Build each network, then bootstrap. PyBootNet generates bootstrapped network metrics and tests whether metrics differ between the two networks, which turns "the networks look different" into a defensible statistical claim [8].

**Scenario 5: A multi-center expression study where co-expression modules look suspiciously batch-like.** Standard batch correction on the expression values is necessary but not sufficient. Confounders can persist in the covariance matrix after correction and generate false non-biological associations. Estimate a batch-corrected co-expression matrix with COBRA before you interpret any differential co-expression [2].

## Common Mistakes and Limitations

**Treating correlation as evidence of mechanism.** A correlation of 0.95 between a metabolite and a transcript is a hypothesis, not a pathway. Every co-expression network paper rests on the understanding that these edges are putative co-regulation, which is why they feed regulatory network inference rather than close arguments about cause [2].

**Ignoring the multiple testing burden.** A 30,000-gene matrix contains roughly 450 million pairwise correlations. At the conventional 0.05 threshold you expect about 22.5 million "significant" results by chance alone. Any large-scale correlation analysis needs false discovery rate control, and tools built for this scale provide it as part of the workflow [1].

**Batch effects in the covariance, not just the means.** This is the mistake even experienced analysts make. You ran ComBat or a similar method, your PCA plot looks clean, and you conclude the data are safe. The batch can still live in the correlation structure. Confounders persisting in covariance after standard correction produce artifactual differential co-expression and false non-biological associations, even with corrected input data [2].

**Using Pearson on clearly non-linear or outlier-dominated data.** A single aberrant sample can manufacture a strong Pearson coefficient. Check scatterplots. Use Spearman when monotonicity is the plausible relationship and outliers are unavoidable.

**Confusing correlation coefficients with correlation-named metrics.** MCC is a classifier evaluation metric [3]. Semantic similarity over MeSH terms is a graph and information-content measure [7]. Neither is the Pearson-style correlation this article centers on, and mixing them up leads to wrong tool choices and wrong searches.

**Forgetting that fancy methods can lose to a correlation baseline.** In a benchmark of ten single-cell time-series methods, most underperformed a correlation baseline on lineage fidelity despite strong forecast numbers [15]. Benchmark against the simple baseline before investing in complexity.

**Memory blindness.** Compute the size of your output matrix before you run the code. Features times features times 8 bytes. If that number exceeds your RAM, choose top-k search or a sparse strategy rather than watching a kernel die.

**Compositional data in microbiome surveys.** Relative abundances sum to a constant, which induces spurious negative correlations between taxa. This is established textbook knowledge in compositional data analysis. Transform appropriately or use methods designed for compositional data before interpreting microbial correlation networks, and bootstrap them if the network will support conclusions [8].

**Overlooking pathway-level aggregation.** Molecule-level correlation drowns in noise. Aggregating to pathway scores first, as single sample pathway analysis methods enable, can produce more interpretable patient-level structure, though method choice affects precision and recall and should be benchmarked for your data type [13].

**Trusting an unvalidated automated workflow.** Reproducibility is a design goal, not a default. Validation studies that document ingestion, analysis, and reporting end to end, including the correlation step, are the model to follow when building your own pipeline [6].

One general limitation applies to every tool in this article: correlation coefficients describe the dataset you computed them on. Whether a relationship holds in your next cohort, your next species, or your next clinical scenario requires new data and, where animals or patients are concerned, appropriate professional oversight.

## Frequently Asked Questions

### What is the best Python library for correlation analysis in bioinformatics?

There is no single best library, only the best match for your scale and question. pandas and SciPy cover small-to-medium analyses with [hypothesis testing](/blog/data-analysis/what-is-hypothesis-testing), NumPy covers fast in-memory matrices, and PyNetCor is the strongest option for large-scale, high-dimensional datasets because it was designed for millions of features with built-in FDR control [1].

### How do I choose between Pearson and Spearman correlation?

Use Pearson when the relationship should be linear and your data have no influential outliers. Use Spearman when you care about any monotonic relationship or when outliers are unavoidable, which describes most noisy omics data. Reporting both on the same pairs is common practice in validation studies.

### Can pandas handle correlation for genome-scale datasets?

Not practically. A dense correlation matrix for tens of thousands of genes already requires gigabytes, and millions of features make the full matrix physically impossible on ordinary hardware. For that regime, use a tool built for large-scale correlation search such as PyNetCor, which computes full matrices or top-k correlations efficiently [1].

### How do I know if my co-expression network is trustworthy?

Bootstrap it. PyBootNet resamples your data, rebuilds the network, and produces bootstrapped network metrics that tell you which edges and statistics are stable, and whether networks from two conditions genuinely differ [8].

### Do batch effects affect correlation analysis after standard batch correction?

Yes. Standard correction makes each gene's mean and variance independent of batch, but confounders can persist in the covariance structure, producing spurious differential co-expression and false non-biological associations. Methods such as COBRA compute a batch-corrected co-expression matrix directly to address this [2].

### Is correlation analysis enough for biomarker discovery?

It is a starting point, not an endpoint. Correlation identifies candidate relationships, but discovery workflows add covariate adjustment, multiple testing control, pathway-level interpretation, and independent validation. Even in benchmark settings, simple correlation baselines are competitive with complex methods on some tasks, which argues for using correlation deliberately within a larger workflow rather than treating it as the whole answer [15].

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## Sources

1. [PyNetCor: a high-performance Python package for large-scale correlation analysis.](https://pubmed.ncbi.nlm.nih.gov/39703431/)
2. [Higher-order correction of persistent batch effects in correlation networks.](https://pubmed.ncbi.nlm.nih.gov/39226186/)
3. [Factors associated with prior pregnancy loss among multigravid women: a retrospective machine learning analysis from an African population cohort.](https://pubmed.ncbi.nlm.nih.gov/42824444/)
4. [Large vision model framework for automated C. elegans analysis: From static morphometry to dynamic neural activity.](https://pubmed.ncbi.nlm.nih.gov/42485426/)
5. [Analysis of clinical, single cell, and spatial data from the Human Tumor Atlas Network (HTAN) with massively distributed cloud-based queries.](https://pubmed.ncbi.nlm.nih.gov/41333415/)
6. [A Reproducible Python-Based Computational Pipeline for Real-Time Ingestion, Advanced Analysis, and Dynamic Reporting of Public Health Data: A Systems Validation Study.](https://pubmed.ncbi.nlm.nih.gov/41798472/)
7. [pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms.](https://pubmed.ncbi.nlm.nih.gov/32552728/)
8. [PyBootNet: a python package for bootstrapping and network construction.](https://pubmed.ncbi.nlm.nih.gov/39926032/)
9. [Membrane Kymograph Generator: a cross-platform GUI software for automated generation and analysis of kymographs along dynamic cell boundaries.](https://pubmed.ncbi.nlm.nih.gov/42316814/)
10. [A computational pipeline for image-based statistical analysis of biomolecular condensates dynamics using morphological descriptors.](https://pubmed.ncbi.nlm.nih.gov/40730560/)
11. [ChatGPT for Univariate Statistics: Validation of AI-Assisted Data Analysis in Healthcare Research.](https://pubmed.ncbi.nlm.nih.gov/39919289/)
12. [PySteMoDA: An Open-Source Python Package for the Analysis of Steered Molecular Dynamics Simulations Data.](https://pubmed.ncbi.nlm.nih.gov/41659591/)
13. [Single sample pathway analysis in metabolomics: performance evaluation and application.](https://pubmed.ncbi.nlm.nih.gov/36376837/)
14. [Comparative evaluation of gene selection approaches in transcriptomics: bias correction and visualization with TransPro.](https://pubmed.ncbi.nlm.nih.gov/42148814/)
15. [scTimeBench: a streamlined benchmarking platform for single-cell time-series analysis.](https://pubmed.ncbi.nlm.nih.gov/42635195/)