RESEARCH DISCIPLINE
Statistical Analysis for Bioinformatics Research
Consulting for Omics & High-Dimensional Studies
Bioinformatics statistical analysis for expression, proteomic and metabolomic studies, with normalization, batch and validation examined on your own data, so the signatures and rankings you report are supported and ready for your journal.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
Statistical Analysis for Bioinformatics Research
Bioinformatics statistical analysis is the statistical design, validation and reporting of analyses of high-dimensional biological data, such as gene expression, proteomic and metabolomic tables in which thousands of measured features describe a few dozen or a few hundred samples. With far more features than samples, rankings of features are unstable, any step that uses the outcome can leak information into the result, and batch can outweigh the biology, so a signature that looks accurate on the data that built it often fails on new samples. OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, provides bioinformatics statistics consulting through in-house PhD-level statisticians, who review the analysis of the feature table your pipeline produced, and the validation behind it, before results are reported.
WHEN TO ENGAGE
Statistical Support at Different Stages of Bioinformatics Research
Researchers usually contact us once a feature table exists and a decision about analysis, validation or reporting is due.
Choosing the Analysis for a Feature Table Your count or abundance matrix is ready, and the normalization, the model for each feature and the multiplicity plan need to be chosen before differential results are generated.
Validating a Signature or Classifier A set of features separates your groups, and the performance on samples that were not used to build it needs to be estimated honestly, with every building step kept inside the validation.
Untangling Batch From Biology Groups differ by run, site or extraction date, and you need to know how far the batch structure limits what can be concluded and what the analysis can legitimately adjust.
Writing Up Omics Results The analysis is complete, and the methods, the false discovery rate statement and the figures need to be prepared and reported to the standard of the journal and its data repository.
RESEARCH CONTEXT
Research Designs and Data We Work With in Bioinformatics
Omics data analysis starts from large feature tables with sample metadata, and the replication, the batch layout and the question decide which analysis is defensible.
01
Bulk RNA-Seq Count Matrices
Gene-by-sample counts from a pipeline, with differing library sizes, low-count filtering and only a handful of replicates in each group.
02
Proteomic and Metabolomic Tables
Abundance tables in which values are absent because a signal fell below detection, and in which intensity drifts across the run order.
03
Multi-Omics Studies
Several feature tables measured on the same samples, on different scales and with missing layers, where selecting features after seeing the outcome is the main risk.
04
Biomarker and Signature Studies
Panels proposed as a classifier or prognostic signature, to be tested on samples that were held out from every step used to build them.
05
Multi-Site and Public Cohort Data
Samples pooled from several laboratories, platforms or public repositories, in which site and platform may differ between the groups being compared.
06
Time-Course and Perturbation Series
Features measured at several times in the same cultures or participants, so the measurements are correlated within each unit and not independent.
METHODOLOGICAL CONSIDERATIONS
Statistical Issues We Consider in Bioinformatics Research
These are the checks made before a feature list or signature is reported, because thousands of features give many ways to find a pattern that will not hold in new samples.
Leakage Inside the Pipeline Feature selection that uses the outcome, and normalization or batch correction estimated on all samples, belong inside each training fold, or the performance of a signature is overstated.
What FDR Control Limits False discovery rate control limits the expected share of false findings among those reported. The family of features is defined in advance, and enrichment tests carry their own multiplicity.
Batch Confounded With Group Where batch fully confounds group, no correction can separate them. Partial overlap is measured first, batch enters the model as a term where possible, and corrected values are not tested as raw data.
Samples, Not Features, Are the Replicates Thousands of features do not enlarge the sample. Power and the stability of a feature ranking are judged from the number of independent samples in each group.
Values Missing Below Detection Abundances that are absent because they fell below detection are not missing at random, so the imputation or censored model is chosen for that reason and its influence on the findings is compared.
Validation on Independent Samples A signature is judged on an external cohort or by nested cross-validation, with discrimination and calibration reported with intervals, and not on the samples that were used to select it.
STATISTICAL METHODS
Methods Selected for the Research Question and Study Design
Each method family is chosen for the feature type and the replication in your study, and the line under each title explains why it is used with high-dimensional biological data.
Bayesian & Computational Methods
Bootstrap resampling shows how stable a ranked feature list is, and multiple imputation handles absent abundances where the reason for their absence supports it.
Multivariate, Scale & Reliability
Ordination by components or scaling, colored by run and by group, reveals batch structure before features are tested, and clustering checks whether proposed subtypes are stable.
Regression & Predictive Modeling
Linear models fitted feature by feature carry batch and covariate terms, logistic models relate a signature to a binary outcome, and smoothers describe nonlinear trends in time or dose.
Longitudinal & Multilevel Analysis
Time-course and paired designs give correlated measurements within one culture or participant, which mixed models represent with a random term for that unit.
Comparative & Hypothesis Testing
Validation measurements on a shortlist of features in small groups use rank-based tests where the outcome is ordinal or its distribution is doubtful, and Welch-type t-tests otherwise, with intervals.
Evidence Synthesis & Meta-Research
Per-cohort effects from public datasets are pooled by meta-analysis, and meta-regression asks whether platform or tissue explains the differences between cohorts.
DELIVERABLES
Statistical Outputs Prepared for Your Research
The outputs follow the structure of an omics study, from the review of the feature table and metadata to the scripts that regenerate every figure.
Data & Study Review The feature table, sample metadata, batch and run structure, filtering rules and the version of the pipeline that produced the matrix are reviewed before any feature is tested.
Statistical Analysis Normalization checks, the agreed feature-level models or classifier with nested validation, and sensitivity analyses to the batch handling and the rule for missing values.
Effect Estimates & Uncertainty Effect estimates with confidence intervals, adjusted values with the false discovery rate level stated, and classifier performance with intervals from held-out data.
Tables & Figures Volcano plots and heat maps, ordination plots colored by batch and by group, ranked feature tables and the sample counts remaining after each filtering step.
Statistical Interpretation Each result is explained as a statistical finding about features, with its stability and the limits of the sample stated, and biological claims are left to follow-up experiments.
Analysis Files R or Python scripts with package versions and random seeds recorded, so that every figure and table regenerates from the feature table you supplied.
REPORTING
Reporting Bioinformatics Research
Omics reporting is set by the platform that produced the data and by whether a signature is offered as a prediction model.
Sequencing-based experiments report to MINSEQE and microarray experiments to MIAME, and mass spectrometry proteomics follows the MIAPE modules; each asks for the sample annotation, the processing steps and deposition of the data in a public repository. A signature or classifier offered to predict an outcome follows TRIPOD+AI, with the validation design stated. The methods name the pipeline version that produced the feature table, the filtering rule, the batch handling and the false discovery rate level, and the figures show the sample counts that survived each step. Effects are reported with confidence intervals beside the adjusted values, and repository accession numbers are prepared for the data availability statement.
ILLUSTRATIVE EXAMPLE
How the Analysis May Look in Practice
An illustration of how a proteomic classifier confounded with run week is validated without leakage and reported with its limits.
ILLUSTRATIVE EXAMPLE · PLASMA PROTEOMICS BIOMARKER RESEARCH · UNITED STATES
A rheumatology research team at a teaching hospital wanted to know whether plasma protein abundances measured by mass spectrometry could distinguish two subtypes of a chronic disease.
The samples of one subtype had been run in earlier weeks than the other, and many abundances were missing below detection. Before any model was fitted, we fixed the plan: ordination colored by run week to see how far batch separated the groups, a rule for missing values with a comparison across alternatives, and a classifier built and tuned entirely inside nested cross-validation, so that selection and normalization never saw the held-out samples. Features were tested as one family with false discovery rate control. Because subtype and run week overlapped, the report stated how much the design limited the conclusion.
Outcome: The team received effect estimates with 95% confidence intervals, cross-validated discrimination with its interval, the batch diagnostics, the batch-by-subtype table, a TRIPOD+AI item table and the scripts.
This example does not describe a real client or project. We never share details of actual client work, in line with the confidentiality we promise every researcher. This illustrative example is included to show the type, scope, and standard of work involved in this kind of analysis.
HOW WE ASSESS YOUR PROJECT
We Review Your Actual Research
Your assessment is based on a review of your actual research materials and analytical requirements, not a generic project category.
You Submit Your Project Details
Share whatever is available at the current stage, such as the research aim, protocol or proposal, dataset, variable list, manuscript or statistical requirements.
We Review Your Research and Define the Scope
Your statistically trained Research Coordinator reviews the materials to understand the study design, research questions, data structure, analytical requirements and expected deliverables. If clarification is needed, additional documents or information can be requested before the assessment is finalized.
Your Analysis Is Completed and Delivered
Once the scope and fixed project fee are agreed, the in-house statistician completes the work and the Research Coordinator remains the communication point through delivery, with the analysis, deliverables and timeline as agreed.
SOFTWARE
Statistical Software for Bioinformatics Research
Bioinformatics analyses are run in the software your team already uses, with scripts provided where applicable.
Statistical Support Within Your Research OnlineSPSS provides the agreed methodological, analytical, and statistical reporting support. The researcher or research team retains the scientific question, substantive interpretation, conclusions, and overall responsibility for the research.
FREQUENTLY ASKED QUESTIONS
Questions About Statistical Analysis for Bioinformatics
Short answers to what bioinformatics researchers ask before sharing a study, from scope and false discovery control to validation, batch and confidentiality.
Where does your work start in a bioinformatics project?
With the feature table your pipeline produced. The statistical work covers the high-dimensional biological data analysis that follows: normalization choices, feature-level models, multiplicity, batch handling and validation. Alignment, variant calling and other upstream processing are documented by your team or your core facility, and the assessment confirms which steps fall within the agreed scope.
Is false discovery rate control enough for my omics results?
FDR control limits the expected proportion of false findings among the features you report, and it does not make each individual finding true. It is also only as sound as the family of tests it covers and the independence of the samples. Reporting how FDR control for omics data was applied, with effect estimates and intervals, tells a reader far more than a list of adjusted values.
How do you check that a classifier is not overfitted?
Every step that learns from the data, including feature selection, tuning, normalization and batch correction, is repeated inside each training fold of a nested cross-validation, and performance is reported with an interval. An external cohort is stronger evidence where one exists. A classifier that has already been built can be reviewed for exactly these leaks.
Can batch effects in my dataset be corrected?
It depends on the design. If every sample of one group was run in one batch, the batch and the group cannot be separated by any correction, and the report states what remains uncertain. If groups are spread across batches, batch can be modeled as a term and its effect checked before differential results are interpreted.
How is confidential omics research data handled?
Feature tables, sample metadata and analysis files are handled confidentially, and project information is not published. An NDA is available where required and can be arranged before sensitive materials are shared. Read more about confidentiality and data security.
What happens when I request an assessment, and how long does it take?
You share your research question, design and relevant materials, and a statistically trained Research Coordinator reviews the request and responds the same day. The analysis, deliverables, timeline and one fixed project fee are then confirmed before any work begins, so the timeline follows from the agreed scope.
Still have a question? Find more answers in our full FAQ.
NEXT STEP
Discuss Your
Bioinformatics Research With Us
Share your research question, study design and available data, even if only a feature table or an analysis plan exists. We review the sample and batch structure and confirm the approach before any work begins.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014