Merge pull request #17 from uclahs-cds/jyhan

Jyhan

NEWS.md

@@ -1,30 +1,17 @@
-# OutSeekR 1.0.0 - 2024-11-07 
-
-## Features
-
-### Core Functionality
-- **Outlier Detection Algorithm**: Implements a statistical approach for detecting transcript-level outliers in RNA-seq or related data types, leveraging normalized data (e.g., FPKM) and several statistical metrics.
-- **Multiple Statistics for Robust Analysis**: Utilizes five distinct statistics for each transcript to robustly assess outliers:
-  - Z-scores using mean and standard deviation.
-  - Z-scores using median and median absolute deviation.
-  - Z-scores with 5%-trimmed mean and standard deviation.
-  - Fraction of observations in the smaller cluster from K-means (K=2).
-  - Cosine similarity between extreme observed values and theoretical distribution quantiles.
-
-### Null Data Simulation
-- **Comprehensive Null Simulation**: Generates null datasets mimicking the observed data distribution, without outliers, through generalized additive modeling of four potential distributions.
+# Unreleased
 
-### Iterative Outlier Testing
-- **P-value and Rank Product Calculation**: Calculates outlier p-values by comparing rank products from observed and null data across multiple rounds, refining the detection by iteratively removing the most extreme outliers.
-- **FDR Adjustment**: Applies False Discovery Rate (FDR) correction to control for multiple testing.
-
-### Performance Optimization
-- **Parallel Processing Support**: Optimized for high-performance analysis using `future.apply` to enable parallelization, compatible with various computing environments.
-
-### Output
-- **Results**: Provides  p-values, FDR values, the count of detected outliers, and the preferred statistical threshold for initial selection (`p-value` or `FDR`).
-
-### Example Data and Parameters
-- **Sample Data and Example Workflow**: Includes `outliers` sample data and demonstrates usage with the `detect.outliers()` function.
-- **Adjustable Parameters**: Allows customization of p-value and FDR thresholds, the number of null data iterations, and the initial screen method for flexible analysis.
+# OutSeekR 1.0.0 - 2024-11-07 
 
+## Added
+* Implementation of core *Outlier Detection Algorithm*, a statistical approach for detecting transcript-level outliers in RNA-seq or related data types, leveraging normalized data (e.g., FPKM) and several statistical metrics.
+* Five distinct statistics for robustly assessing outliers:
+  * Z-scores using mean and standard deviation.
+  * Z-scores using median and median absolute deviation.
+  * Z-scores with 5%-trimmed mean and standard deviation.
+  * Fraction of observations in the smaller cluster from K-means (K=2).
+  * Cosine similarity between extreme observed values and theoretical distribution quantiles.
+* Comprehensive null simulation functionality. Generates null datasets mimicking the observed data distribution (without outliers) through generalized additive modeling of four potential distributions.
+* Outlier p-value calculation by comparing rank products from observed and null data across multiple rounds, refining the detection by iteratively removing the most extreme outliers.
+* Support for *false discovery rate* (FDR) correction to control for multiple testing.
+* Optimization for high-performance analysis using `future.apply` to enable parallelization, compatible with various computing environments.
+* Sample `outliers` data and usage demonstration.