Publication-ready statistics and RNA-seq — no coding required, no data uploaded.
macOS support is coming soon for both Apple Silicon and Intel Macs.
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- 30+ statistical tests — ANOVA, regression, survival, pharmacology, mediation, and more
- Complete RNA-seq pipeline — from raw counts to differential expression with QC plots
- Runs 100% locally — your data never leaves your machine
- Publication-ready output — interactive plots, results tables, and built-in citations
Download easyCris · Visit easycris.com · Contact: hello@easycris.com
Your analysis data, files, and results stay on your machine — nothing is uploaded or shared. The in-app updater checks release metadata and only downloads update files when you choose to install. No usage data, analysis data, or file contents are sent to external servers.
easyCris Community is an open-source desktop application for scientific data analysis — covering classical statistics, pharmacology, bulk RNA-seq differential expression, and data cleaning tools, all in one place. Core workflows are implemented with established statistical methods and manuscript references listed below, giving you publication-ready output without writing a single line of code. All computation runs locally using an embedded analysis engine; no external software installation is required for official installer builds.
Official install options:
- Official signed installer (recommended): easycris.com
- Official website: easycris.com
Windows x64 installer (.exe). Once installed, the app supports in-app updates through signed release packages.
The Community edition is available under Apache-2.0. Commercial and Pro offerings may provide additional capabilities, support, or official services.
Core statistical workflows and the RNA-seq pipeline follow published methods listed in the Citations section. References are provided to support manuscript-ready reporting.
easyCris covers 30+ statistical tests across seven analysis groups. Most tests produce a results table and auto-generated interactive plots.
| Test | Accepted Data Format |
|---|---|
| Independent Samples T-Test | Wide or Long |
| Paired Samples T-Test | Wide or Long (wide preferred) |
| One Sample T-Test | Wide (single column) |
| One-Way ANOVA | Wide or Long |
| Two-Way ANOVA | Long only |
| Multifactorial ANOVA (3-way) | Long only |
Post-hoc corrections available for ANOVA: Tukey, Bonferroni, Holm, Holm-Sidak, Sidak, Dunnett, FDR-BH. Two-Way and Multifactorial ANOVA include interaction plots; simple effects analysis is available as an optional step.
| Test | Accepted Data Format | Parametric equivalent |
|---|---|---|
| Mann-Whitney U Test | Wide or Long | Independent T-Test |
| Wilcoxon Signed-Rank Test | Wide or Long (wide preferred) | Paired T-Test |
| Kruskal-Wallis H Test | Wide or Long | One-Way ANOVA |
| Scheirer-Ray-Hare Test | Long only | Two-Way ANOVA |
Wide format — one column per variable, one row per observation.
| Test | Input |
|---|---|
| Simple Linear Regression | 1 outcome column + 1 predictor column |
| Multiple Linear Regression | 1 outcome column + 2 or more predictor columns |
| Binary Logistic Regression | 1 binary outcome column + 1 or more predictor columns |
| Multinomial Logistic Regression | 1 multi-class outcome column + 1 or more predictor columns |
| Pearson / Spearman / Kendall Tau Correlation | 2 or more numeric columns |
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| ROC Curve | Box Plot | Violin Plot |
Wide format — one column per variable, one row per observation.
| Test | Input |
|---|---|
| Chi-Square Independence Test | 2 categorical columns |
| Chi-Square Goodness of Fit | 1 categorical column |
| Fisher's Exact Test | 2 categorical columns (2 categories each) |
| McNemar's Test | Before column + After column (paired) |
Wide format — select one or more numeric columns.
| Test | Description |
|---|---|
| Shapiro-Wilk | Normality test (small to medium samples) |
| Kolmogorov-Smirnov | Normality test against a specified distribution |
| Anderson-Darling | Normality test with emphasis on distribution tails |
| Cramer-von Mises | Goodness-of-fit normality test |
| Jarque-Bera | Normality test based on skewness and kurtosis |
| Normality (All Tests) | Run all 5 normality tests simultaneously on a selected column |
| Descriptive Statistics | Mean, median, SD, quartiles, and outliers for 1 or more columns |
| Outlier Detection | Identify outliers across 1 or more numeric columns |
Wide format — separate columns for dose and response values.
| Model | Description |
|---|---|
| 3-Parameter Logistic (3PL) | Fits IC50 / EC50 with fixed bottom (0) |
| 4-Parameter Logistic (4PL) | Fits IC50 / EC50 with variable Hill slope |
Wide format — separate columns for time-to-event, event status, and grouping or predictor variables.
| Test | Input |
|---|---|
| Kaplan-Meier Analysis | Time column + event column + group column |
| Cox Proportional Hazards | Time column + event column + 1 or more predictor columns |
| Nelson-Aalen Estimator | Time column + event column + group column |
Wide format — one column per variable, one row per observation.
| Analysis | Variables |
|---|---|
| Mediation Analysis (Baron & Kenny Model 4) | Exposure (X), Mediator (M), Outcome (Y) |
| Simple Moderation (Model 1) | Predictor (X), Moderator (W), Outcome (Y) |
| Moderated Mediation (Model 7) | Predictor (X), Mediator (M), Moderator (W), Outcome (Y) |
💡 Data format tip: easyCris accepts both wide and long formats where noted above. Use the built-in Pivot Wider and Pivot Longer tools in Data Cleaning to convert between formats before running your analysis.
easyCris includes a complete bulk RNA-seq differential expression workflow, from raw count matrix to annotated results and QC plots.
| Step | Details |
|---|---|
| Count matrix | Raw integer counts (CSV) from featureCounts, HTSeq, STAR, GEO, or recount3 |
| Sample metadata | CSV with experimental factors — Treatment, Batch, Cell Line, Time Point, and more |
| Gene ID lookup | Ensembl, Entrez, UniProt, UniProt Swiss-Prot IDs → gene symbols |
| Duplicate genes | Sum duplicates or keep first occurrence |
| Model | Use case |
|---|---|
Simple ~condition |
Compare two groups (e.g., Treated vs Control) |
Multi-factor ~condition + batch |
Adjust for batch effects or continuous covariates |
Interaction ~genotype * treatment |
Test whether treatment effect varies by genotype or cell line |
| Multi-run comparator | Run multiple contrasts in the same project and review results side by side |
| Output | Description |
|---|---|
| Results table | gene, baseMean, log2FoldChange, lfcSE, pvalue, padj (Benjamini-Hochberg) |
| PCA biplot | Samples colored by experimental factor — identify batch effects and outliers |
| Volcano plot | log2 fold change vs adjusted p-value — quick overview of the DE landscape |
| Heatmap | Significant genes filtered by adjusted p-value |
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| PCA Biplot | Significant Gene Heatmap |
easyCris includes a set of data preparation tools to reshape, filter, and summarise your data before analysis. Each tool has a built-in reference guide with before-and-after examples accessible from the Help menu.
| Tool | When to use | What changes |
|---|---|---|
| Pivot Wider | Repeated measures are stacked in rows and you need them as separate columns — e.g., Pre/Post in one column → two separate columns | Row count decreases; column count increases |
| Pivot Longer | Each measurement is a separate column and you need them stacked for analysis — e.g., preparing data for repeated-measures tests that expect long format | Row count increases; column count decreases |
| Tool | When to use | What changes |
|---|---|---|
| Advanced Filter | Keep only rows matching specific criteria — supports multiple conditions with AND / OR logic and parenthesized grouping | Non-matching rows removed; column structure preserved |
| Sort | Reorder rows by a specific variable — ascending or descending | Row order changes; no rows or columns added or removed |
| Tool | When to use | What changes |
|---|---|---|
| Group & Aggregate | Compute group means, sums, counts, or other statistics from raw data — e.g., mean score per treatment group | One row per unique group combination; values replaced by computed aggregate |
| Outline | Scan data by category without permanently reshaping — expand or collapse row groups to focus on one subset at a time | Data values unchanged; display only |
The easyCris grid formula engine supports spreadsheet-style formulas with dependency tracking, autocomplete, and backend-assisted evaluation for large ranges.
Autocomplete is intentionally limited to these categories (from the current allowed formula set):
| Category | Count | Examples |
|---|---|---|
| Math & Trigonometry | 61 | SUM, ABS, ROUND, SQRT, MOD, LOG, SIN, COS, POWER |
| Statistical | 99 | AVERAGE, STDEV.S, COUNT, CORREL, PERCENTILE, NORM.DIST, T.DIST |
| Date & Time | 25 | TODAY, NOW, DATE, DATEDIF, NETWORKDAYS, YEAR, MONTH, DAY |
| Financial | 55 | NPV, IRR, PMT, FV, RATE |
| Engineering | 54 | BIN2DEC, HEX2DEC, CONVERT, COMPLEX, ERF, DELTA, GESTEP |
Formulas use familiar spreadsheet syntax and run directly in the grid (no scripting required).
All plots are interactive — hover for values, zoom, pan, and export as PNG.
Plots are auto-generated based on the test you run:
| Category | Plot types |
|---|---|
| Hypothesis testing | Bar, box, violin with significance brackets |
| ANOVA | Interaction plots, faceted grouped bar |
| Regression | Scatter, residual, forest, ROC |
| Categorical | Grouped bar, mosaic, heatmap |
| Distribution | Histogram, Q-Q plots, column scatter |
| Survival | Kaplan-Meier curves, cumulative hazard, forest |
| RNA-seq | PCA biplot, volcano, heatmap |
| Pharmacology | Dose-response curves |
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| ANOVA + Tukey | Interaction Plot | Kaplan-Meier |
easyCris ships with three built-in reference guides accessible from the Help menu:
| Guide | Contents |
|---|---|
| 📊 Statistical Tests Guide | Quick reference for every test — required inputs, parameters, and the plots that will be generated |
| 🧬 RNA-seq Guide | Step-by-step walkthrough from count matrix import to differential expression results |
| 🧹 Data Cleaning Guide | Reference for every reshape, filter, and aggregate tool with before-and-after examples |
| OS | Windows 10 / 11 (x64) |
| RAM | 4 GB minimum, 8 GB recommended |
| Disk | ~200 MB |
| Internet | Not required for analysis; required only for update checks and downloads |
macOS and Linux builds are planned for a future release.
- Download the installer from easycris.com
- Run the
.exeinstaller (admin rights may depend on your system policy) - Open easyCris and import your CSV data
- Select a statistical test or analysis workflow
- Review your results table and auto-generated plots
- Install Node.js, Python 3.12, and Rust toolchain (
rustup+ MSVC build tools on Windows) - Run
npm ci - Run
pwsh -ExecutionPolicy Bypass -File scripts/bootstrap-python.ps1 -IncludeRnaseqfor full source functionality (use without-IncludeRnaseqfor stats-only setup) - Run
npm run -s typecheck - Run
npm run tauri dev
If you use easyCris in published research, please cite the underlying methods:
| Module | Citation |
|---|---|
| t-Tests & ANOVA | Student (1908). The probable error of a mean. Biometrika, 6(1), 1-25. https://doi.org/10.1093/biomet/6.1.1; Fisher (1925). Statistical Methods for Research Workers. Oliver and Boyd. |
| Rank-Based Nonparametric Tests | Wilcoxon (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80-83; Mann & Whitney (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18(1), 50-60. https://doi.org/10.1214/aoms/1177730491; Kruskal & Wallis (1952). Use of ranks in one-criterion variance analysis. JASA, 47(260), 583-621. https://doi.org/10.1080/01621459.1952.10483441; Dunn (1964). Multiple comparisons using rank sums. Technometrics, 6(3), 241-252. https://doi.org/10.1080/00401706.1964.10490181 |
| Regression & Correlation | Pearson (1895). Note on regression and inheritance in the case of two parents. Proceedings of the Royal Society of London, 58, 240-242. https://doi.org/10.1098/rspl.1895.0041; Spearman (1904). The proof and measurement of association between two things. American Journal of Psychology, 15(1), 72-101. https://doi.org/10.2307/1412159; Kendall (1938). A new measure of rank correlation. Biometrika, 30(1/2), 81-93. https://doi.org/10.1093/biomet/30.1-2.81; Cox (1958). The regression analysis of binary sequences. JRSS Series B, 20(2), 215-242. https://doi.org/10.1111/j.2517-6161.1958.tb00292.x |
| Survival Analysis | Kaplan & Meier (1958). Nonparametric estimation from incomplete observations. JASA, 53(282), 457-481. https://doi.org/10.1080/01621459.1958.10501452; Cox (1972). Regression models and life-tables. JRSS Series B, 34(2), 187-220. https://doi.org/10.1111/j.2517-6161.1972.tb00899.x; Nelson (1969). Hazard plotting for incomplete failure data. Journal of Quality Technology, 1(1), 27-52. https://doi.org/10.1080/00224065.1969.11980344 |
| Dose-Response (3PL / 4PL) | Hill (1910). The possible effects of the aggregation of the molecules of haemoglobin on its dissociation curves. Journal of Physiology, 40(Suppl), iv-vii. https://doi.org/10.1113/jphysiol.1910.sp001386; Sebaugh (2011). Guidelines for accurate EC50/IC50 estimation. Pharmaceutical Statistics, 10(2), 128-134. https://doi.org/10.1002/pst.426 |
| Mediation & Moderation | Baron & Kenny (1986). The moderator-mediator variable distinction in social psychological research. Journal of Personality and Social Psychology, 51(6), 1173-1182. https://doi.org/10.1037/0022-3514.51.6.1173; Sobel (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290-312. https://doi.org/10.2307/270723; Hayes (2022). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (3rd ed.). Guilford Press (PROCESS framework); Preacher, Rucker, & Hayes (2007). Addressing moderated mediation hypotheses. Multivariate Behavioral Research, 42(1), 185-227. https://doi.org/10.1080/00273170701341316 |
| RNA-seq Differential Expression | Love, Huber, & Anders (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15, 550. https://doi.org/10.1186/s13059-014-0550-8; Zhu, Ibrahim, & Love (2019). Heavy-tailed prior distributions for sequence count data. Bioinformatics, 35(12), 2084-2092. https://doi.org/10.1093/bioinformatics/bty895 |
easyCris Community is licensed under the Apache License, Version 2.0.
See LICENSE for the full license text.
The easyCris name, logo, and official signed releases are not licensed for unrestricted trademark use by the Apache-2.0 code license.
Commercial inquiries: hello@easycris.com
See CONTRIBUTING.md, SECURITY.md, and CODE_OF_CONDUCT.md.
easyCris is intended for research purposes only. It is not a medical device and should not be used for clinical diagnosis or treatment decisions.