Sean Maden (metamaden)

metamaden

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Company:Department of Biostatistics, JHSPH

Location:remote

Home Page:https://metamaden.github.io/

Twitter:@MadenSean

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Sean Maden's starred repositories

openai-cookbook

Examples and guides for using the OpenAI API

Svelvet

🎛 A Svelte library for building dynamic, infinitely customizable node-based user interfaces and flowcharts

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awesome-multi-omics

List of software packages for multi-omics analysis

containers

Bioinformatics containers

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sctransform

R package for modeling single cell UMI expression data using regularized negative binomial regression

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sc_mixology

This contains the dataset for comparing scRNA-seq analysis methods

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flowdec

TensorFlow Deconvolution for Microscopy Data

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extreme-deconvolution

Density estimation using Gaussian mixtures in the presence of noisy, heterogeneous and incomplete data

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voyager

From geospatial to spatial -omics

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CIDR

Clustering through Imputation and Dimensionality Reduction

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NSForest

A machine learning method for the discovery of the minimum marker gene combinations for cell type identification from single-cell RNA sequencing

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steinbock

A toolkit for processing multiplexed tissue images

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DropletQC

Nuclear fraction QC metric to detect empty droplets and damaged cells in scRNA-seq data

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MTG_celltypes

Human cortical cell types

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hello

Classic hello world script in Nextflow language

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SimBu

Simulate pseudo-bulk RNAseq samples from scRNAseq expression data

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DSA

Digital Sorting Algorithm

SPEAQeasy

SPEAQeasy: portable LIBD RNA-seq pipeline using Nextflow. Check http://research.libd.org/SPEAQeasy-example/ for an example on how to use this pipeline and analyze the resulting output files.

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DLPFC_snRNAseq

snRNA_seq portion of the spatialDLPFC project

bioc_team_ds

R/Bioconductor-powered Team Data Science by Leonardo Collado Torres

bluster

Clone of the Bioconductor repository for the bluster package.

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A-Geometric-Perspective-on-the-Power-of-Principal-Component-Association-Tests-in-Multiple-Phenotype-

A Geometric Perspective on the Power of Principal Component Association Tests in Multiple Phenotype Studies, by Zhonghua Liu & Xihong Lin

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intronomer

Tool for calculating intron persistence, as defined in https://doi.org/10.1186/s13059-022-02789-6

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