Companion repository to Lause, Berens & Kobak (2021): "Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data", Genome Biology
-
Updated
May 6, 2022 - Jupyter Notebook
Companion repository to Lause, Berens & Kobak (2021): "Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data", Genome Biology
Probabilistic outlier identification for bulk RNA sequencing data
Various Fortran codes
Code for fitting a negative binomial distribution in Python
Negative binomial distributed pseudorandom numbers.
Geographically Weighted Negative Binomial Regression in Python - local spatial modeling for over dispersed count data.
The DOTNB repository is a collection of code files that implement DOTNB across several programming languages. The DOTNB is the distribution for the Difference Of Two Negative Binomial distributions, i.e., Z=X-Y ~ DOTNB (λ_1,λ_2,p_1,p_2), where X ~ NB(λ_1,p_1 ) and Y ~ NB(λ_2,p_2 ).
High-precision ribosome pause detection tool utilizing Negative Binomial modeling to optimize Z-scores and extract ML-ready contextual features from Ribo-seq data.
Research code and reproducibility materials for a mobility-informed SIR model using subway ridership data, particle smoothing, and mobility-reduction scenarios to evaluate influenza transmission and the instantaneous reproduction number.
Understanding RNA-seq differential expression from Poisson to DESeq2, reproduced by simulation
纯 Python 标准库从零实现 RNA-seq 差异表达分析(归一化/离散度收缩/负二项 GLM/Wald/BH),与官方 DESeq2 对拍:log2FC r=0.9997、显著集 Jaccard=0.90。
Football prop forecasting engine. Post-competition review found the calibration layer was making forecasts worse, removing it is documented, measured, and enforced by tests.
RNS-Seq Count Model Explorer
The DEGage2 package works to identify differentially expressed genes (DEGs) on bulk RNA-seq data through utilization of DOTNB
Create an iterator for generating pseudorandom numbers drawn from a negative binomial distribution.
Create an array containing pseudorandom numbers drawn from a negative binomial distribution.
DEGage is a novel model-based method for gene differential expression analysis between two groups of scRNA-seq count data. It employs a novel family of discrete distributions for describing the difference of two NB distributions (named DOTNB).
Julia hurdle and zero-inflated negative-binomial models for sequencing counts (hurdle_nb, zinb), cross-checked against R pscl. Pre-release: the formal proofs its spec requires do not exist yet.
Optional presentation for the "Sistemi Complessi" course.
To associate your repository with the negative-binomial topic, visit your repo's landing page and select "manage topics."