MichaelZH24's repositories

10x-scATAC-2019

Publication Page for Satpathy*, Granja* et al 2019

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AmpUMI

Toolkit for the design and analysis of amplicon sequencing experiments utilizing unique molecular identifiers (UMIs)

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Bioinformatics

Bioinformatics Workflows

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bpipes

bioinformatic pipelines

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c9-python-getting-started

Sample code for Channel 9 Python for Beginners course

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ChIP-seq-analysis

ChIP-seq analysis notes from Ming Tang

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coursera-ml-py

Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera

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CRISPResso2

Analysis of deep sequencing data for rapid and intuitive interpretation of genome editing experiments

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dropseqRunner

Pipeline for processing single-cell RNA-seq data (drop-seq)

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eoulsan

A pipeline and a framework for NGS analysis (RNA-Seq and soon Chip-Seq)

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lab-website-template

An easy-to-use, flexible website template for labs!

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MIPGEN

One stop MIP design and analysis

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mlcourse.ai

Open Machine Learning Course

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pyprobml

Python code for "Machine learning: a probabilistic perspective" (2nd edition)

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pySCENIC-test

pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-Cell rEgulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data.

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PythonSIFT

A clean and concise Python implementation of SIFT (Scale-Invariant Feature Transform)

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scCATCH

Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data

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SCENICprotocol

A scalable SCENIC workflow for single-cell gene regulatory network analysis

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SelfTarget

Scripts for processing and predicting CRISPR/Cas9-generated mutations

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single-cell-tutorial

Single cell current best practices tutorial case study for the paper:Luecken and Theis, "Current best practices in single-cell RNA-seq analysis: a tutorial"

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SingleCellLineage

Updated scripts and pipelines for processing GESTALT data at single-cell resolution

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tinyatlas

A tiny cell atlas for commonly sequenced organisms.

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udlbook

Understanding Deep Learning - Simon J.D. Prince

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