How genes, variants, expression data and biological pathways are transformed into meaningful visual evidence
Introduction: A world of data beyond patient records
When analysts hear the term healthcare analytics, they commonly think about hospital admissions, patient demographics, treatment costs, laboratory results, bed occupancy, readmission rates and clinical outcomes. These are important areas of analysis, but they represent only one layer of healthcare information.
Behind every patient record is another, less visible layer of data: genes, proteins, mutations, chromosomes, biological pathways and molecular activity. This is the world of bioinformatics.
For readers who are new to the field, the National Human Genome Research Institute provides a clear definition of bioinformatics and explains how computational methods help organize, analyze and interpret biological information.
Bioinformatics combines biology, computing and quantitative analysis to collect, organize and interpret biological information. In genomics, it is used to examine data such as DNA sequences, gene expression and annotations describing biological features. These analyses can help researchers study how biological systems function and how they may be connected with health and disease.
For many ordinary analysts, however, bioinformatics remains unfamiliar territory. Its terminology appears highly scientific, its datasets can be enormous, and many of its most important visualizations are rarely encountered in conventional business intelligence.
Yet the fundamental analytical questions are surprisingly familiar:
- What changed?
- Where did it change?
- How large was the change?
- Is the change statistically meaningful?
- Which samples behave similarly?
- Are there hidden groups?
- Which biological systems may be affected?
- Could the observed pattern be caused by poor data quality or technical variation?
The difference lies in the type of data being studied, and in the specialist visual language needed to understand it.
“Healthcare analytics describes what happened to patients. Bioinformatics can help investigate the biological signals associated with why it happened”.
Healthcare Analytics And Bioinformatics Are Related, But Not Identical
Traditional healthcare analytics generally begins with information attached to a patient, visit, hospital, procedure or clinical outcome.
A hospital analyst may investigate:
- whether readmission rates are increasing;
- which departments have the longest waiting times;
- whether treatment costs differ between facilities;
- which diagnoses are associated with longer stays;
- whether mortality or infection rates vary across groups.
Bioinformatics works at a different level. It may investigate:
- which genes are more or less active in one group than another;
- which genetic variants are associated with a disease or trait;
- whether samples form biologically meaningful clusters;
- whether a chromosomal region has gained or lost copies of DNA;
- which biological pathways appear unusually active;
- whether molecular patterns differ between treatment responders and non-responders.
Gene expression, for example, refers to the process through which information encoded in a gene is converted into a functional product, commonly through RNA transcription. Expression measurements can therefore indicate how actively particular genes are functioning under different conditions.
Bioinformatics is not a replacement for healthcare analytics. It adds another level of evidence. Clinical data may show that two groups respond differently to treatment. Bioinformatics may help researchers explore whether their gene-expression profiles, mutation patterns or biological pathways also differ.
That connection is one reason bioinformatics is becoming increasingly relevant beyond the traditional community of geneticists and computational biologists.
Why Bioinformatics Feels Inaccessible To Ordinary Analysts
The problem is not simply that bioinformatics uses unfamiliar terminology.
Ordinary analytical work often begins with a rectangular table in which:
- each row represents a transaction, patient or event;
- each column represents a known variable;
- the number of variables is manageable;
- common charts can be selected directly.
Bioinformatics datasets may behave differently. An expression dataset can contain measurements for thousands of genes across a much smaller number of biological samples. A genomic association dataset may contain hundreds of thousands or millions of tested variants distributed across chromosomes. A mutation dataset may need to show several types of genomic alteration across many genes and samples simultaneously.
Even when the underlying data are stored in ordinary rows and columns, their analytical meaning is not ordinary.A bar chart may show the ten genes with the largest fold changes, but it cannot by itself explain whether those changes are statistically reliable.
A scatterplot may show sample separation, but without suitable dimensionality reduction, thousands of molecular variables cannot be meaningfully represented on two axes. A heatmap may look impressive, but without normalization, suitable scaling and careful interpretation, its patterns may be misleading.
Bioinformatics, therefore, requires more than software capable of drawing charts. It requires visual literacy: an understanding of what each chart represents, what data it requires, and what conclusions it can and cannot support.
The Specialist Visuals Every Analyst Should Understand
1. Volcano Plot: Finding Changes That Are Both Large And Statistically Notable
A volcano plot is one of the most recognizable visuals in gene-expression analysis. Each point normally represents a gene or another measured biological feature.
The horizontal axis shows the size and direction of change, often expressed as log₂ fold change. Points to the right represent increased expression, while points to the left represent decreased expression.
The vertical axis represents statistical significance, usually expressed as −log₁₀(p), meaning the negative base-10 logarithm of the p-value. Because smaller p-values produce larger −log₁₀(p) values, points appearing higher on the plot indicate stronger statistical evidence. For example, p = 0.01 corresponds to 2, while p = 0.001 corresponds to 3. Higher points have smaller p-values and therefore stronger statistical evidence under the fitted analysis.
The most noticeable candidates often appear in the upper-left and upper-right areas: they combine relatively large changes with relatively strong statistical evidence. Volcano plots are widely used to display significance relative to the magnitude of change in differential-expression results.
Figure 1: An actual Brill-Viz Volcano Plot and its summary generated from a demo differential-expression dataset — log₂ fold change on the horizontal axis, −log₁₀(q-value) on the vertical axis, with upregulated and downregulated genes separated from non-significant points (center).
What the reader should notice
Look for:
- the number of strongly upregulated and downregulated features;
- whether the pattern is approximately balanced;
- genes that combine large effect size with strong evidence;
- labeled genes that may deserve further investigation;
- an unexpected concentration of points on one side.
What the reader should not conclude
A point high on a volcano plot is not automatically a proven biomarker, disease mechanism or therapeutic target.
Threshold-based selection from a volcano plot can introduce statistical problems, particularly when researchers treat visual cut-offs as independent confirmation. Candidate genes normally require biological context, multiple-testing control, independent validation and supporting evidence.
Readers can explore a practical example through NCBI’s GEO2R guide to volcano plots, which demonstrates how statistical significance is compared with the magnitude and direction of gene-expression change.
2. MA Plot: Checking Whether Change Depends On Expression Level
The MA plot complements the volcano plot.Its horizontal axis represents average expression or signal intensity. Its vertical axis represents the difference between groups, often as log fold change.
This enables the analyst to examine whether the magnitude or variability of expression change depends on the underlying abundance of a gene. MA plots are a standard part of gene-level differential-expression workflows.
A well-behaved MA plot commonly shows most observations distributed around zero, with greater variability sometimes appearing among low-expression features.
Figure 2: An actual Brill-Viz MA Plot along with its summary from the same demo dataset, average expression on the horizontal axis, log₂ fold change on the vertical axis.
What the reader should notice
Look for:
- systematic movement above or below zero;
- unusually wide spread at low expression levels;
- extreme genes with large fold changes;
- intensity-dependent patterns;
- whether significant features are distributed broadly or concentrated in a particular expression range.
Why it matters
The volcano plot emphasizes effect size and statistical evidence. The MA plot emphasizes effect size and underlying abundance. Together, they provide a more complete view than either chart alone.
3. Manhattan Plot: Locating Association Signals Across The Genome
A Manhattan plot is commonly used to summarize results from a genome-wide association study. The horizontal axis represents genomic position across successive chromosomes. The vertical axis shows the strength of association, usually as as −log₁₀(p), meaning the negative base-10 logarithm of the p-value. Each point represents a tested genetic variant.
The chart receives its name because strong groups of association signals can resemble the skyline of a city. Alternating chromosome colors are not merely decorative. They help readers distinguish the genomic boundaries between neighboring chromosomes.
Figure 3: An actual Brill-Viz Manhattan Plot along with its interpretive from the same demo dataset
What the reader should notice
Look for:
- peaks that exceed an established significance threshold;
- several nearby variants forming the same peak;
- chromosomes containing unusually strong signals;
- isolated points that may require quality review;
- whether signals are widely distributed or concentrated in a few regions.
A vital caution
The threshold itself has a conventional value: in human genome-wide association studies, a signal is typically considered genome-wide significant at p < 5×10⁻⁸, a stringent bar that accounts for the huge number of variants tested simultaneously.
A Manhattan plot requires genuine genomic ingredients:
- chromosome;
- physical position;
- a tested variant;
- a valid association statistic or p-value.
A dataset containing gene names and p-values alone does not automatically support a scientifically meaningful Manhattan plot. Genomic position is essential because the central question is where association signals occur across the genome.
4. CNV Profile: Identifying Gains And Losses Of Genomic Material
Most human genes are normally present in two copies, one inherited from each parent but sections of DNA can sometimes occur in greater or fewer numbers. These changes are known as copy-number variations. They can include gains or losses affecting parts of a chromosome or, in some cases, much larger regions.
A CNV profile visualizes copy-number measurements across genomic position. Values above a baseline may indicate gains, while values below it may indicate losses. In cancer genomics, such profiles can reveal regions of amplification or deletion that may be associated with tumor development or progression.
Figure 4: An actual Brill-Viz CNV Plot along with its interpretive summary from the same demo dataset
What the reader should notice
Look for:
- sustained segments above or below the baseline;
- repeated gains or losses across samples;
- broad chromosomal changes versus narrow local events;
- highly amplified regions;
- whether neighbouring measurements support the same segment.
Why the required data matters
A field labeled “CNV” is not sufficient by itself. A valid CNV chart generally requires a genuine numeric copy-number measure, such as:
- copy number;
- segment mean;
- copy ratio;
- log₂ copy ratio;
- total, major or minor copy number.
Expression values or differential-expression fold changes should not be substituted for copy-number measurements simply to make a CNV-shaped plot.
An absent CNV chart is preferable to a scientifically incorrect one.
5. Expression Heatmap: Seeing Patterns Across Genes And Samples
An expression heatmap displays a matrix in which:
- rows often represent genes;
- columns represent samples;
- color represents relative expression.
Clustering can rearrange genes and samples so that similar patterns appear next to one another.
This can reveal:
- sets of genes behaving similarly;
- samples with related molecular profiles;
- possible treatment groups;
- outlying samples;
- potential technical or biological subgroups.
The visual is powerful because it shows many relationships simultaneously. But it can also be deceptive. Colors depend on normalization, scaling, selected genes, distance measures and clustering methods.
A heatmap is therefore best treated as an exploratory map, not as independent proof that identified clusters represent genuine biological classes.
Figure 5: An actual Brill-Viz Expression Heatmap, genes by row, samples by column, colored by row z-score, with hierarchical clustering applied to both axes.
6. PCA: Reducing Thousands Of Variables Into Major Patterns
Gene-expression and other omics datasets are often described as high-dimensional because every gene, protein or metabolite may act as a separate variable.
A two-dimensional screen cannot display thousands of dimensions directly.Principal component analysis, or PCA, transforms the original variables into new components that capture major directions of variation. A PCA plot commonly displays the first two components.
Samples positioned close together have similar profiles according to the variation represented by those components. Samples far apart are more dissimilar.
Figure 6: An actual Brill-Viz PCA chart from the same dataset as other charts shown above
What PCA may reveal
PCA can help identify:
- broad separation between experimental groups;
- major sources of variability;
- outlier samples;
- batch effects;
- treatment-related patterns;
- samples that may have been mislabeled.
What PCA does not prove
Separated groups do not automatically demonstrate biological causation.
The separation may result from treatment, disease, sex, age, laboratory batch, sequencing platform, sample quality or another uncontrolled factor.
PCA is a visual clue that asks a question; metadata and statistical testing must help answer it.
7. UMAP And T-SNE: Exploring Complex Local Structures
UMAP and t-SNE are nonlinear dimensionality-reduction techniques commonly used to represent complex biological datasets in two dimensions. They are especially familiar in single-cell analysis, where thousands of cells may each contain measurements across thousands of genes.
These methods can reveal local groupings that may be difficult to see with a linear method such as PCA. Dimensionality reduction is widely used to visualize broad trends and explore cellular distributions in high-dimensional data. However, the geometry of a UMAP or t-SNE plot must be interpreted carefully.
The presence of visible islands does not prove the existence of distinct biological populations. Results can be influenced by preprocessing, parameter choices, initialization, selected features and the number of dimensions used before embedding. Comparative evaluations have shown that dimensionality-reduction methods differ in how well they preserve local and global structure and in their sensitivity to analytical choices.
Figure 7: The same demo sample set projected three ways by Brill-Viz: PCA (left), UMAP (center), and t-SNE (right). Notice how the same samples can appear differently grouped depending on the projection method, exactly the caution described above.
A practical interpretation
- PCA is useful for broad, linear patterns and explained variation.
- t-SNE emphasizes local neighbourhood relationships.
- UMAP also emphasizes neighbourhood structure and may retain more broader organization in some applications.
- None should be interpreted without considering preprocessing and metadata.
8. Oncoprint: Summarizing Alterations Across Genes And Samples
An oncoprint is a compact visualization of genomic alterations across multiple genes and samples. Rows usually represent genes, columns represent patients or biological samples, and colored marks indicate alteration types such as:
- mutation;
- amplification;
- deletion;
- fusion;
- multiple alterations.
Oncoprints are particularly useful for observing mutation frequency, co-occurrence and apparent mutual exclusivity. They provide a concise summary of genomic alterations across a cohort.
Figure 8: An actual Brill-Viz Oncoprint Summary: genes by row, samples by column, colored marks indicating an alteration.
What the reader should notice
Look for:
- genes altered in a large proportion of samples;
- samples with unusually high alteration burden;
- alterations that frequently occur together;
- patterns that appear mutually exclusive;
- differences between clinical or treatment groups.
The apparent relationships should normally be tested statistically rather than judged only by eye.
9. Pathway Enrichment: Moving From A List Of Genes To Biological Meaning
Differential-expression analysis may produce hundreds or thousands of genes.Even when each result is statistically correct, a long gene list can be difficult to interpret.Pathway enrichment analysis groups genes according to known biological functions or processes. Instead of asking only, “Which genes changed?”, the analyst begins asking:
- Are immune-response pathways affected?
- Is cell-cycle regulation altered?
- Are metabolic pathways overrepresented?
- Are DNA-repair systems involved?
- Do the changing genes point toward a coherent biological process?
Figure 9: An actual Brill-Viz Pathway / Gene Network chart, connecting genes to the biological pathways they contribute to. Every chart in Brill-Viz can be opened to this expanded view, which also includes the underlying data table and a full interpretive summary alongside the chart itself, as also appended here.
This is often where bioinformatics moves from statistical detection toward biological interpretation.
A pathway chart may display:
- pathway name;
- enrichment score;
- gene ratio;
- number of contributing genes;
- p-value or adjusted p-value;
- direction of enrichment.
A vital caution
A statistically enriched pathway is not automatically activated, inhibited or causally responsible for a disease.
Pathway databases overlap, annotations are incomplete, and several pathways may contain many of the same genes. Enrichment should be interpreted alongside effect direction, study design and biological knowledge.
The Charts That Protect The Analysis
Figure 10: Three actual Brill-Viz quality-control charts from the same demo run: sample-correlation heatmap, batch-effect distribution, and library-size distribution. Quality control comes before biological interpretation.
The most visually dramatic bioinformatics charts often receive the most attention.
However, quality-control visuals may be even more important.
Library-Size Distribution
In sequencing studies, library size broadly reflects how many reads or counts were recorded for each sample.
Large differences may indicate uneven sequencing depth, sample-quality issues or technical variation requiring normalization and review.
Sample-Correlation Heatmap
This chart shows how strongly samples resemble one another across many measured features.
It can help detect:
- duplicate samples;
- outliers;
- unexpected groupings;
- samples that do not resemble their labeled group.
Batch-Effect Chart
A batch is a technical grouping caused by differences such as processing date, laboratory, plate, instrument or sequencing run.
If samples separate mainly by batch rather than biological group, technical effects may be influencing the results.
Missingness Profile
Missing data may be concentrated in particular variables, samples or experimental groups. That pattern can be more informative than the overall missing percentage.
Distribution And Count Checks
Unexpected count ranges, extreme values, zero inflation and skewed measurements can affect downstream analysis.
These charts may not be as attractive as a volcano or Manhattan plot, but they help answer a fundamental question: Are we looking at biology, or at an artifact created by the data-generation process?
Why Every Dataset Should Not Generate Every Specialist Chart
A chart registry may contain dozens of visual types, but this does not mean that every chart should appear for every dataset.
Each bioinformatics visual has minimum data requirements.
When the required information is absent, three approaches are possible:
- display an empty chart;
- use unrelated variables as substitutes;
- do not generate the chart.
The third option is normally the most responsible.An empty chart adds clutter and confuses the reader. A substitute-variable chart is more dangerous because it may look convincing while representing the wrong scientific concept.
This distinction is central to responsible specialist analytics: A technically successful plot is not necessarily a scientifically valid plot.
From Separate Charts To A Biological Story
A major challenge in bioinformatics is that no single visual tells the complete story.
Imagine a study comparing diseased and healthy samples. A responsible visual sequence might look like this:
Step 1: Check data quality
Library-size and missingness charts identify unusual samples.
A sample-correlation heatmap checks whether biological replicates behave consistently.
PCA examines whether samples separate by condition, batch or another variable.
Step 2: Identify molecular changes
The MA plot shows how expression differences behave across abundance levels.
The volcano plot highlights genes combining meaningful effect size with statistical evidence.
A ranked bar chart identifies the strongest upregulated and downregulated genes.
Step 3: Examine pattern consistency
An expression heatmap shows whether the candidate genes consistently distinguish the groups or whether the result depends on only a few samples.
Step 4: Connect genes to biology
Pathway enrichment identifies biological processes represented by the changing genes.
Step 5: Add genomic context
Where relevant, Manhattan, CNV and oncoprint visuals reveal association locations, structural changes or mutation patterns.
The conclusion does not come from one colorful chart. It emerges from the agreement, or disagreement, between several forms of evidence.
Making Bioinformatics Visualization More Accessible With Brill-Viz
Specialist bioinformatics environments are powerful, but they can demand programming, package management, knowledge of data structures and familiarity with several analytical workflows.
That technical depth is appropriate for expert research. However, it creates a considerable entry barrier for analysts, healthcare professionals, students, managers and researchers who want to explore prepared biological results without developing a full computational pipeline.
Brill-Viz approaches the challenge from the perspective of guided visual intelligence. Its Healthcare and Bioinformatics Snapshot environment can:
- identify relevant healthcare and biological roles in a dataset;
- recognize whether gene, fold-change, p-value, chromosome, position, expression and variant fields are available;
- organize applicable visuals into specialist Snapshot Packs;
- avoid plotting unsupported or empty charts;
- present all applicable charts together on a single scrollable page;
- provide basic statistics and interpretive summaries;
- help readers understand what a specialist visual may indicate;
- make visible why certain charts could not be responsibly generated.
The template includes specialist visual families covering differential expression, dimensionality reduction, genomic association, copy-number variation, mutation patterns, pathway analysis and quality control.
This does not turn a general analyst into a molecular biologist, nor should it attempt to replace specialist bioinformatics pipelines; Its value lies elsewhere.
It can reduce the distance between:
- having biological results and being able to explore them;
- seeing a specialist chart and understanding its basic message;
- creating an attractive visual and checking whether it is scientifically supported;
- reviewing several charts independently and interpreting them as a connected analytical story.
Brill-Viz can therefore serve as a bridge between ordinary analytical familiarity and specialist biological visualization.
Responsible Interpretation Remains Essential
Accessible software does not remove the need for scientific judgment.
Bioinformatics visuals should be interpreted in the context of:
- study design;
- sample size;
- biological replication;
- normalization and preprocessing;
- statistical method;
- multiple-testing correction;
- batch effects;
- clinical and experimental metadata;
- external validation;
- existing biological knowledge.
A visual summary can help analysts recognize patterns, generate questions and communicate findings. It should not be used to transform an exploratory signal into a clinical conclusion without appropriate evidence.
This is especially important in healthcare, where overinterpretation may influence research direction, resource allocation or clinical expectations.
The objective of accessible bioinformatics visualization should therefore not be to make complex science appear simple.
It should be to make the complexity more understandable, more transparent and easier to investigate responsibly.
Key Takeaways
- Bioinformatics extends healthcare analytics beyond patients and services to genes, variants, expression patterns, chromosomes, proteins and biological pathways.
- Specialist charts answer different scientific questions. Volcano and MA plots highlight expression changes, Manhattan plots locate genomic associations, CNV plots show gains and losses, while PCA, UMAP and t-SNE reveal sample structure.
- A visually attractive chart is not automatically scientifically valid. Each specialist visual requires appropriate biological variables, sufficient observations and meaningful statistical inputs.
- Quality control must come before biological interpretation. Missingness, library size, sample correlation, batch effects and outliers can reveal whether a pattern reflects biology or a technical artifact.
- No single bioinformatics chart provides a complete conclusion. Stronger interpretation comes from connecting quality-control, dimensionality-reduction, differential-expression, heatmap and pathway results.
- Dimensionality-reduction plots require caution. Visible clusters in PCA, UMAP or t-SNE may reflect biological groups, but they may also arise from preprocessing, batch effects or parameter choices.
- Statistical significance is not the same as biological importance. Candidate genes, variants and pathways require context, validation and expert review.
- Unsupported charts should be skipped rather than forced. An absent chart is more responsible than a convincing-looking visual created from unsuitable substitute data.
- Bioinformatics visual literacy is becoming valuable beyond specialist research teams. Ordinary analysts can benefit from understanding what these visuals show, even when they are not building full genomic pipelines.
- Brill-Viz can help bridge the accessibility gap by identifying relevant biological fields, selecting applicable visuals, preventing unsupported plots and providing interpretive guidance—without claiming to replace bioinformatics expertise.
Closing Remarks
Bioinformatics is often presented as a highly specialized discipline accessible only through coding, advanced statistics and molecular expertise. Its methods certainly require care.
But its central questions are not beyond the reach of ordinary analysts:
Which biological features changed?
How strong was the evidence?
Where in the genome did the signal occur?
Which samples behaved similarly?
Could the pattern be technical rather than biological?
Which genes may work together?
Which pathways may help explain the observation?
Specialist visualizations such as Volcano, MA, Manhattan, CNV, heatmap, PCA, UMAP, t-SNE, oncoprint and pathway-enrichment plots translate these questions into visible patterns.
Learning to read them does not make someone a bioinformatician overnight. It does something equally valuable: it gives analysts the vocabulary to engage with a field that is becoming increasingly important to biomedical research, personalized medicine and data-driven healthcare.
And when software combines relevant chart selection, scientific data safeguards, basic statistics and interpretive guidance, bioinformatics visualization becomes less mysterious—without pretending that the underlying science is simple.
That is the opportunity for tools such as Brill-Viz:
Not to replace bioinformatics expertise, but to make biological visual evidence more accessible, understandable and responsibly explorable.
FAQs
Q: Do I need to know how to code to use these visuals?
Answer: No. Reading and interpreting the charts in this guide does not require programming. Generating them from your own data with Brill-Viz also does not. Building custom pipelines, running alignment or variant-calling software, or developing new statistical methods still requires specialist bioinformatics tools and expertise. Brill-Viz works on data that has already been processed into a tabular result.
Q: What data does a Manhattan plot actually require?
Answer: A genuine chromosome field, a genomic position field, a tested variant, and a valid association statistic or p-value. Gene names and p-values alone are not sufficient, since the entire point of the chart is showing where in the genome a signal occurs.
Q: Can PCA, UMAP, or t-SNE prove that two groups are biologically different?
Answer: No. All three can reveal that samples separate into visible groups, but that separation may reflect treatment, disease, batch, sequencing platform, or another uncontrolled factor. A dimensionality-reduction plot raises a question; metadata and statistical testing are needed to answer it.
Q: Why would a chart be skipped instead of generated anyway?
Answer: Because a technically successful plot is not the same as a scientifically valid one. Substituting an unrelated variable to force a chart to appear, for example, using expression values in place of copy-number data for a CNV profile can look convincing while representing the wrong scientific concept. An absent chart is more responsible than a misleading one.
Q: Does a statistically significant result mean a gene, variant, or pathway is important?
Answer: Not by itself. Statistical significance should be interpreted alongside effect size, multiple-testing correction, sample design, quality-control evidence, and independent biological validation. A candidate result is a starting point for further investigation, not a conclusion on its own.
Q: Does Brill-Viz replace a dedicated bioinformatics pipeline?
Answer: No, and it is not designed to. It does not process raw sequence files or run alignment, variant-calling, or differential-expression algorithms. Its role is downstream: turning an already-processed result table into relevant, correctly gated visuals with interpretive summaries, reducing the distance between having biological results and being able to explore them.
Read more from Brill-Viz blog series: Please read Beyond Dashboards to learn how to transform raw data into decision intelligence
