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easyCris\n\n**Publication-ready statistics and RNA-seq — no coding required, no data uploaded.**\n\n![Platform](https://img.shields.io/badge/platform-Windows-0078d4?logo=windows\u0026logoColor=white)\n![Version](https://img.shields.io/github/v/tag/easyCris-software/easyCris?sort=semver\u0026color=brightgreen)\n\n\n\u003ctable\u003e\n  \u003ctr\u003e\n    \u003ctd\u003e\u003cimg src=\"assets/screenshots/anova_bar_tukey.png\" alt=\"One-Way ANOVA with Tukey post-hoc brackets\" width=\"420\"/\u003e\u003c/td\u003e\n    \u003ctd\u003e\u003cimg src=\"assets/screenshots/rnaseq_pca_biplot.png\" alt=\"RNA-seq PCA biplot\" width=\"420\"/\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/table\u003e\n\n### Why researchers choose easyCris\n\n- **30+ statistical tests** — ANOVA, regression, survival, pharmacology, mediation, and more\n- **Complete RNA-seq pipeline** — from raw counts to differential expression with QC plots\n- **Runs 100% locally** — your data never leaves your machine\n- **Publication-ready output** — interactive plots, results tables, and built-in citations\n\n**[Download easyCris](https://github.com/easyCris-software/easyCris/releases/latest)** · **[Visit easycris.com](https://www.easycris.com)** · **[Join the Discussion](https://github.com/easyCris-software/easyCris/discussions)**\n\n---\n\n## 🔒 Privacy\n\nYour analysis data, files, and results stay on your machine — nothing is uploaded or shared.\nThe in-app updater checks release metadata and only downloads update files when you choose to install.\nNo usage data, analysis data, or file contents are sent to external servers.\n\n---\n\n## 🧪 What is easyCris?\n\neasyCris is 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.\n\n---\n\n## ⬇️ Download\n\n**[Download Latest Release](https://github.com/easyCris-software/easyCris/releases/latest)**\n\nWindows x64 installer (`.exe`). Once installed, the app supports in-app updates through signed release packages.\n\n\n---\n\n## ✅ Statistical Methods\n\nCore statistical workflows and the RNA-seq pipeline follow published methods listed in the Citations section.\nReferences are provided to support manuscript-ready reporting.\n\n---\n\n## 📊 Statistical Analysis\n\neasyCris covers 30+ statistical tests across seven analysis groups. Most tests produce a results table and auto-generated interactive plots.\n\n### 🔬 Parametric Tests\n\n| Test | Accepted Data Format |\n|---|---|\n| Independent Samples T-Test | Wide or Long |\n| Paired Samples T-Test | Wide or Long *(wide preferred)* |\n| One Sample T-Test | Wide (single column) |\n| One-Way ANOVA | Wide or Long |\n| Two-Way ANOVA | Long only |\n| Multifactorial ANOVA (3-way) | Long only |\n\nPost-hoc corrections available for ANOVA: Tukey, Bonferroni, Holm, Holm-Sidak, Sidak, Dunnett, FDR-BH.\nTwo-Way and Multifactorial ANOVA include interaction plots; simple effects analysis is available as an optional step.\n\n### 📉 Nonparametric Tests\n\n| Test | Accepted Data Format | Parametric equivalent |\n|---|---|---|\n| Mann-Whitney U Test | Wide or Long | Independent T-Test |\n| Wilcoxon Signed-Rank Test | Wide or Long *(wide preferred)* | Paired T-Test |\n| Kruskal-Wallis H Test | Wide or Long | One-Way ANOVA |\n| Scheirer-Ray-Hare Test | Long only | Two-Way ANOVA |\n\n### 📐 Regression \u0026 Correlation\n\nWide format — one column per variable, one row per observation.\n\n| Test | Input |\n|---|---|\n| Simple Linear Regression | 1 outcome column + 1 predictor column |\n| Multiple Linear Regression | 1 outcome column + 2 or more predictor columns |\n| Binary Logistic Regression | 1 binary outcome column + 1 or more predictor columns |\n| Multinomial Logistic Regression | 1 multi-class outcome column + 1 or more predictor columns |\n| Pearson / Spearman / Kendall Tau Correlation | 2 or more numeric columns |\n\n\u003ctable\u003e\n  \u003ctr\u003e\n    \u003ctd\u003e\u003cimg src=\"assets/screenshots/logistic_regression_roc.png\" alt=\"Binary Logistic Regression ROC curve\" width=\"280\"/\u003e\u003c/td\u003e\n    \u003ctd\u003e\u003cimg src=\"assets/screenshots/plot_box.png\" alt=\"Box plot with significance brackets\" width=\"280\"/\u003e\u003c/td\u003e\n    \u003ctd\u003e\u003cimg src=\"assets/screenshots/plot_violin.png\" alt=\"Violin plot\" width=\"280\"/\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd align=\"center\"\u003e\u003cem\u003eROC Curve\u003c/em\u003e\u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\u003cem\u003eBox Plot\u003c/em\u003e\u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\u003cem\u003eViolin Plot\u003c/em\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/table\u003e\n\n### 🗂️ Categorical Analysis\n\nWide format — one column per variable, one row per observation.\n\n| Test | Input |\n|---|---|\n| Chi-Square Independence Test | 2 categorical columns |\n| Chi-Square Goodness of Fit | 1 categorical column |\n| Fisher's Exact Test | 2 categorical columns (2 categories each) |\n| McNemar's Test | Before column + After column (paired) |\n\n\u003cimg src=\"assets/screenshots/mcnemar_grouped_bar.png\" alt=\"McNemar grouped bar chart\" width=\"480\"/\u003e\n\n### 📏 Distribution \u0026 Descriptive\n\nWide format — select one or more numeric columns.\n\n| Test | Description |\n|---|---|\n| Shapiro-Wilk | Normality test (small to medium samples) |\n| Kolmogorov-Smirnov | Normality test against a specified distribution |\n| Anderson-Darling | Normality test with emphasis on distribution tails |\n| Cramer-von Mises | Goodness-of-fit normality test |\n| Jarque-Bera | Normality test based on skewness and kurtosis |\n| Normality (All Tests) | Run all 5 normality tests simultaneously on a selected column |\n| Descriptive Statistics | Mean, median, SD, quartiles, and outliers for 1 or more columns |\n| Outlier Detection | Identify outliers across 1 or more numeric columns |\n\n### 💊 Pharmacology \u0026 Dose-Response\n\nWide format — separate columns for dose and response values.\n\n| Model | Description |\n|---|---|\n| 3-Parameter Logistic (3PL) | Fits IC50 / EC50 with fixed bottom (0) |\n| 4-Parameter Logistic (4PL) | Fits IC50 / EC50 with variable Hill slope |\n\n### ⏱️ Survival Analysis\n\nWide format — separate columns for time-to-event, event status, and grouping or predictor variables.\n\n| Test | Input |\n|---|---|\n| Kaplan-Meier Analysis | Time column + event column + group column |\n| Cox Proportional Hazards | Time column + event column + 1 or more predictor columns |\n| Nelson-Aalen Estimator | Time column + event column + group column |\n\n\u003cimg src=\"assets/screenshots/kaplan_meier_survival.png\" alt=\"Kaplan-Meier survival curve\" width=\"480\"/\u003e\n\n### 🔗 Mediation \u0026 Moderation\n\nWide format — one column per variable, one row per observation.\n\n| Analysis | Variables |\n|---|---|\n| Mediation Analysis (Baron \u0026 Kenny Model 4) | Exposure (X), Mediator (M), Outcome (Y) |\n| Simple Moderation (Model 1) | Predictor (X), Moderator (W), Outcome (Y) |\n| Moderated Mediation (Model 7) | Predictor (X), Mediator (M), Moderator (W), Outcome (Y) |\n\n---\n\n\u003e 💡 **Data format tip:** easyCris accepts both wide and long formats where noted above.\n\u003e Use the built-in **Pivot Wider** and **Pivot Longer** tools in Data Cleaning to convert between formats before running your analysis.\n\n---\n\n## 🧬 RNA-seq Analysis\n\neasyCris includes a complete bulk RNA-seq differential expression workflow, from raw count matrix to annotated results and QC plots.\n\n### Data Preparation\n\n| Step | Details |\n|---|---|\n| Count matrix | Raw integer counts (CSV) from featureCounts, HTSeq, STAR, GEO, or recount3 |\n| Sample metadata | CSV with experimental factors — Treatment, Batch, Cell Line, Time Point, and more |\n| Gene ID lookup | Ensembl, Entrez, UniProt, UniProt Swiss-Prot IDs → gene symbols |\n| Duplicate genes | Sum duplicates or keep first occurrence |\n\n### Model Configuration\n\n| Model | Use case |\n|---|---|\n| Simple `~condition` | Compare two groups (e.g., Treated vs Control) |\n| Multi-factor `~condition + batch` | Adjust for batch effects or continuous covariates |\n| Interaction `~genotype * treatment` | Test whether treatment effect varies by genotype or cell line |\n| Multi-run comparator | Run multiple contrasts in the same project and review results side by side |\n\n### Results \u0026 Visualizations\n\n| Output | Description |\n|---|---|\n| Results table | gene, baseMean, log2FoldChange, lfcSE, pvalue, padj (Benjamini-Hochberg) |\n| PCA biplot | Samples colored by experimental factor — identify batch effects and outliers |\n| Volcano plot | log2 fold change vs adjusted p-value — quick overview of the DE landscape |\n| Heatmap | Significant genes filtered by adjusted p-value |\n\n\u003ctable\u003e\n  \u003ctr\u003e\n    \u003ctd\u003e\u003cimg src=\"assets/screenshots/rnaseq_pca_biplot.png\" alt=\"RNA-seq PCA biplot\" width=\"420\"/\u003e\u003c/td\u003e\n    \u003ctd\u003e\u003cimg src=\"assets/screenshots/rnaseq_heatmap.png\" alt=\"RNA-seq significant gene heatmap\" width=\"420\"/\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd align=\"center\"\u003e\u003cem\u003ePCA Biplot\u003c/em\u003e\u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\u003cem\u003eSignificant Gene Heatmap\u003c/em\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/table\u003e\n\n---\n\n## 🧹 Data Cleaning\n\neasyCris 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.\n\n### Reshape\n\n| Tool | When to use | What changes |\n|---|---|---|\n| **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 |\n| **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 |\n\n### Filter \u0026 Sort\n\n| Tool | When to use | What changes |\n|---|---|---|\n| **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 |\n| **Sort** | Reorder rows by a specific variable — ascending or descending | Row order changes; no rows or columns added or removed |\n\n### Summarise\n\n| Tool | When to use | What changes |\n|---|---|---|\n| **Group \u0026 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 |\n| **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 |\n\n---\n\n## 🧮 Formula Engine\n\nThe easyCris grid formula engine supports spreadsheet-style formulas with dependency tracking, autocomplete, and backend-assisted evaluation for large ranges.\n\nAutocomplete is intentionally limited to these categories (from the current allowed formula set):\n\n| Category | Count | Examples |\n|---|---:|---|\n| **Math \u0026 Trigonometry** | 61 | SUM, ABS, ROUND, SQRT, MOD, LOG, SIN, COS, POWER |\n| **Statistical** | 99 | AVERAGE, STDEV.S, COUNT, CORREL, PERCENTILE, NORM.DIST, T.DIST |\n| **Date \u0026 Time** | 25 | TODAY, NOW, DATE, DATEDIF, NETWORKDAYS, YEAR, MONTH, DAY |\n| **Financial** | 55 | NPV, IRR, PMT, FV, RATE |\n| **Engineering** | 54 | BIN2DEC, HEX2DEC, CONVERT, COMPLEX, ERF, DELTA, GESTEP |\n\n\u003e Formulas use familiar spreadsheet syntax and run directly in the grid (no scripting required).\n\n---\n\n## 📈 Interactive Plots\n\nAll plots are interactive — hover for values, zoom, pan, and export as PNG.\n\nPlots are auto-generated based on the test you run:\n\n| Category | Plot types |\n|---|---|\n| Hypothesis testing | Bar, box, violin with significance brackets |\n| ANOVA | Interaction plots, faceted grouped bar |\n| Regression | Scatter, residual, forest, ROC |\n| Categorical | Grouped bar, mosaic, heatmap |\n| Distribution | Histogram, Q-Q plots, column scatter |\n| Survival | Kaplan-Meier curves, cumulative hazard, forest |\n| RNA-seq | PCA biplot, volcano, heatmap |\n| Pharmacology | Dose-response curves |\n\n\u003ctable\u003e\n  \u003ctr\u003e\n    \u003ctd\u003e\u003cimg src=\"assets/screenshots/anova_bar_tukey.png\" alt=\"ANOVA bar plot with Tukey brackets\" width=\"280\"/\u003e\u003c/td\u003e\n    \u003ctd\u003e\u003cimg src=\"assets/screenshots/anova_two_way_interaction.png\" alt=\"Two-Way ANOVA interaction plot\" width=\"280\"/\u003e\u003c/td\u003e\n    \u003ctd\u003e\u003cimg src=\"assets/screenshots/kaplan_meier_survival.png\" alt=\"Kaplan-Meier survival curve\" width=\"280\"/\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd align=\"center\"\u003e\u003cem\u003eANOVA + Tukey\u003c/em\u003e\u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\u003cem\u003eInteraction Plot\u003c/em\u003e\u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\u003cem\u003eKaplan-Meier\u003c/em\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/table\u003e\n\n---\n\n## 📖 In-App Help\n\neasyCris ships with three built-in reference guides accessible from the Help menu:\n\n| Guide | Contents |\n|---|---|\n| 📊 **Statistical Tests Guide** | Quick reference for every test — required inputs, parameters, and the plots that will be generated |\n| 🧬 **RNA-seq Guide** | Step-by-step walkthrough from count matrix import to differential expression results |\n| 🧹 **Data Cleaning Guide** | Reference for every reshape, filter, and aggregate tool with before-and-after examples |\n\n---\n\n## 💻 System Requirements\n\n| | |\n|---|---|\n| OS | Windows 10 / 11 (x64) |\n| RAM | 4 GB minimum, 8 GB recommended |\n| Disk | ~200 MB |\n| Internet | Not required for analysis; required only for update checks and downloads |\n\n\u003e macOS and Linux builds are planned for a future release.\n\n---\n\n## 🚀 Getting Started\n\n1. Download the installer from the [Releases page](https://github.com/easyCris-software/easyCris/releases/latest)\n2. Run the `.exe` installer (admin rights may depend on your system policy)\n3. Open easyCris and import your CSV data\n4. Select a statistical test or analysis workflow\n5. Review your results table and auto-generated plots\n\n---\n\n## 📚 Citations\n\nIf you use easyCris in published research, please cite the underlying methods:\n\n| Module | Citation |\n|---|---|\n| t-Tests \u0026 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. |\n| Rank-Based Nonparametric Tests | Wilcoxon (1945). Individual comparisons by ranking methods. *Biometrics Bulletin*, 1(6), 80-83; Mann \u0026 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 \u0026 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 |\n| Regression \u0026 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 |\n| Survival Analysis | Kaplan \u0026 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 |\n| 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 |\n| Mediation \u0026 Moderation | Baron \u0026 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, \u0026 Hayes (2007). Addressing moderated mediation hypotheses. *Multivariate Behavioral Research*, 42(1), 185-227. https://doi.org/10.1080/00273170701341316 |\n| RNA-seq Differential Expression | Love, Huber, \u0026 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, \u0026 Love (2019). Heavy-tailed prior distributions for sequence count data. *Bioinformatics*, 35(12), 2084-2092. https://doi.org/10.1093/bioinformatics/bty895 |\n\n---\n\n## ⚖️ License\n\nCopyright © easyCris Software. Unauthorized copying, distribution, or modification is prohibited.\nBy downloading and using easyCris you agree to the Terms of Use included with the installer.\n\n## ⚠️ Disclaimer\n\neasyCris is intended for research purposes only.\nIt is not a medical device and should not be used for clinical diagnosis or treatment decisions.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feasycris-software%2Feasycris","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feasycris-software%2Feasycris","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feasycris-software%2Feasycris/lists"}