RESEARCH DISCIPLINE
Statistical Analysis for Microbiology Research
Consulting for Culture & Sequencing Studies
Microbiology statistical analysis for colony counts, susceptibility testing, growth experiments and microbiome sequencing, with the specimen treated as the replicate, zeros and detection limits handled and results prepared to your journal's standard.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
Statistical Analysis for Microbiology Research
Microbiology statistical analysis is the statistical design, analysis and reporting of studies that count, culture, type or sequence microorganisms, producing colony counts, minimum inhibitory concentrations, growth measurements and read counts per taxon. These data are counts or compositions that are skewed, full of zeros and bounded by a counting range, and many colonies or reads come from the same specimen, so a workflow that runs t-tests on raw proportions, or counts every colony or read as an observation, misstates both the difference and its precision. OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, provides microbiology data analysis through in-house PhD-level statisticians, who define the specimen as the unit and choose count or compositional models with you before groups are compared.
WHEN TO ENGAGE
Statistical Support at Different Stages of Microbiology Research
Most microbiology projects come to us around the sampling plan, the first count or sequencing tables, or a reviewer's doubt about how specimens were compared.
Planning Sampling and Replication You are designing a culture, susceptibility or sequencing study and need the number of specimens, the dilution scheme and the allocation of samples to runs settled before laboratory work begins.
Analyzing Counts and Abundances Colony counts, minimum inhibitory concentrations or taxon tables are ready, and the model for zeros and skewed values and the treatment of detection limits need to be chosen and checked.
Analyzing a Microbiome Dataset Sequencing is complete, and the handling of differing depth, compositional structure and batch needs to be decided before diversity and differential abundance results are reported.
Responding to Reviewer Comments A reviewer has questioned pseudoreplication, the use of t-tests on proportions or the absence of any correction for depth, and the analysis needs a defensible and documented answer.
RESEARCH CONTEXT
Research Designs and Data We Work With in Microbiology
Microbiology data come as counts, titers and abundance tables, and the way specimens, plates and runs are nested decides which model is defensible.
01
Culture and Colony-Count Experiments
Colony-forming unit counts from plated dilution series, with several plates for each specimen and a countable range outside which counts are unusable.
02
Antimicrobial Susceptibility Testing
Minimum inhibitory concentrations from doubling dilutions or inhibition zone diameters, recorded in discrete steps and often at the end of the tested range.
03
16S and Shotgun Microbiome Studies
Read counts per taxon in every specimen, compositional by nature, with sequencing depth that differs across samples and extraction or sequencing batches.
04
Growth Curves and Kill Assays
Optical density or viable counts followed over time in each culture, where successive readings from one culture are correlated.
05
Isolate and Surveillance Collections
Isolates from patients, surfaces or sites, sometimes several from the same source, with resistance recorded as present or absent or as a titer.
06
Infection and Colonization Models
Bacterial burden measured in host cells or tissue samples, with many zeros, heavy right tails and values below the detection limit.
METHODOLOGICAL CONSIDERATIONS
Statistical Issues We Consider in Microbiology Research
These are the checks made before groups are compared, because the unit of replication and the shape of count data can change a conclusion more than the test does.
The Specimen as the Replicate Colonies on a plate and reads from a sample come from one specimen. They are summarized or modeled within it, so the sample size is the number of specimens, not colonies or reads.
Compositional Abundance Relative abundances sum to a constant, so a rise in one taxon forces others down. Compositional methods or ratios are used, and absolute change is claimed only where it was measured.
Zeros in Counts and Reads A zero colony count or an absent taxon may mean true absence or too shallow sampling, and the spread usually exceeds the Poisson. Negative binomial or zero-aware models replace t-tests on raw proportions.
Sequencing Depth and Batch Depth differs between samples, and extraction or sequencing runs can separate groups. Depth is modeled or normalized, and batch is checked against group before any comparison is made.
Counting Range and Detection Limit Counts are reliable only within a countable range. A plate with no growth bounds the concentration by the dilution plated, and a too-numerous plate is a lower bound; both are carried into the model, not deleted.
MIC Values and Breakpoints Doubling-dilution MICs are interval-censored on a log2 scale, and values at the ends of the range are bounds. They are analyzed on that scale and categorized by breakpoint only where a standard applies.
STATISTICAL METHODS
Methods Selected for the Research Question and Study Design
Each method family is chosen for the type of count and the nesting in your study, and the line under each title explains why it is used with microbiological data.
Regression & Predictive Modeling
Poisson and negative binomial models fit colony and read counts with an offset for dilution or depth, and logistic models describe the presence of an organism or resistance.
Longitudinal & Multilevel Analysis
Growth curves and repeated sampling of one culture or host are correlated, and plates nested in specimens and specimens in sites need a random term for each level.
Multivariate, Scale & Reliability
Community composition is explored by ordination and clustering with distances suited to compositional data, and group differences are tested afterward with their dispersion examined.
Comparative & Hypothesis Testing
Counts or abundances are compared on specimen-level summaries with rank-based tests, resistant and susceptible proportions with exact tests, and each with an effect size and interval.
Bayesian & Computational Methods
Counts below the detection limit are handled by censored models or multiple imputation, and bootstrap intervals describe diversity indices whose sampling distribution is not standard.
Clinical, Survival & Epidemiological
Time to culture conversion or clearance of an organism is a time-to-event outcome, censored at the end of follow-up, and is analyzed with survival methods that use the censored cases.
DELIVERABLES
Statistical Outputs Prepared for Your Research
The outputs follow the structure of a microbiology study, from the review of specimens and dilutions to the scripts that regenerate every table.
Data & Study Review The specimen and plate structure, dilution records, counting ranges, sequencing depth and batch labels are reviewed before any group comparison is made.
Statistical Analysis The agreed count, compositional or survival models with offsets for dilution or depth, diagnostics for overdispersion and zeros, and the planned sensitivity analyses.
Effect Estimates & Uncertainty Rate ratios or log reductions with confidence intervals, differential abundance estimates with adjusted values, and diversity measures with intervals.
Tables & Figures Count and MIC summary tables, ordination plots colored by group and batch, abundance plots by specimen and a table of specimens entering each analysis.
Statistical Interpretation Each result is explained at the level of the specimen, compositional findings are worded as relative changes, and limits from sequencing depth and sampling are stated.
Analysis Files R or Python scripts, SPSS syntax or GraphPad Prism notes, with filtering thresholds, offsets and software versions recorded so that each table can be regenerated.
REPORTING
Reporting Microbiology Research
Microbiome sequencing and culture-based work are reported against different standards, and susceptibility data follow the breakpoint body the laboratory used.
Human microbiome studies report to STORMS, and environmental and non-human host-associated microbiome studies to STREAMS, which ask for the sampling, sequencing, bioinformatic and statistical steps. Sequence metadata are described with the MIxS standards where the data are deposited. Susceptibility results are reported against the breakpoint standard, EUCAST or CLSI, that the laboratory used. The methods give the number of specimens, plates, reads and isolates at each step, the rule for zeros and detection limits and the depth normalization, and each comparison carries its effect size and confidence interval. Raw reads are deposited with accession numbers ready for the data availability statement.
ILLUSTRATIVE EXAMPLE
How the Analysis May Look in Practice
An illustration of how swab counts with zeros and overgrown plates are turned into a comparison between two sanitizers.
ILLUSTRATIVE EXAMPLE · FOOD SAFETY SURFACE SANITATION RESEARCH · UNITED STATES
A food safety research laboratory compared bacterial counts on processing surfaces cleaned with two sanitizers, using swabs plated at several dilutions.
Many swabs showed no growth, others exceeded the countable range, and several swabs came from the same production line on the same day. Before any comparison was made, we fixed the plan: the swab was the unit of analysis with line and day as grouping terms, counts were modeled with a dilution offset and a negative binomial distribution, not a t-test on raw counts, no-growth plates entered the count model as observed zeros for the volume plated, not as an arbitrary small value on the log scale, and plates beyond the countable range were treated as lower bounds. A sensitivity analysis varied the rule for swabs beyond the countable range.
Outcome: The laboratory received the rate ratio with its 95% confidence interval, a table of swabs entering each analysis, the sensitivity results 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 Microbiology Research
Microbiology analyses are run in the software your team already uses, with syntax or 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 Microbiology
Short answers to what microbiologists ask before sharing a study, from replication and zeros to microbiome data, review and confidentiality.
What is the replicate in a colony-count experiment?
The specimen, culture or sample that was independently prepared is the replicate, not the plate, the colony or the read. Plates from one specimen show measurement precision and are summarized or modeled within it. Microbial count analysis then compares specimens, so the sample size reflects the number of independent preparations.
Why not compare relative abundances with a t-test?
Relative abundances are compositional, skewed and often zero-heavy, so a t-test on raw proportions can mislead and a rise in one taxon shows as a fall in others. Microbiome statistics use count models with a depth offset or compositional methods, with batch and depth examined first. The choice depends on whether absolute or relative change is the question.
How do you treat colony counts that are zero or too numerous to count?
A plate with no growth means the concentration in the specimen is below the limit set by the dilution plated, so on the concentration scale it is a censored value, not a true zero. A plate beyond the countable range is also a bound. These values are modeled as censored or analyzed under stated rules with a sensitivity check, not deleted or replaced silently.
Can you analyze MIC data from susceptibility tests?
Yes. MICs are recorded in doubling dilutions, so they are analyzed on a log2 scale as interval-censored values, with values at the ends of the range kept as bounds. Categories such as susceptible or resistant are applied by the breakpoint standard you used, and comparisons between groups are reported with intervals. Laboratory microbiology statistics of this kind are reviewed against your protocol.
How is confidential microbiology research data handled?
Raw counts, sequence-derived tables 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
Microbiology Research With Us
Share your research question, study design and available data, even if only a protocol or a set of plate records exists. We review how specimens were collected and counted 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