Using self-organizing maps for visualization and interpretation of cytometry data

FlowSOM algorithm in Python, using self-organizing maps and minimum spanning tree for visualization and interpretation of cytometry data

Using self-organizing maps for visualization and interpretation of cytometry data

The complete FlowSOM package known from R, now available in Python!

Worked example showing how to cluster and visualize flow/mass cytometry data using FlowSOM and Rtsne

Useful resources for creating apps and working with flow.

Integration of multi-batch cytometry datasets

Pipeline for flow cytometry anlaysis

Temporal clustering and tracking algorithm which fuses on FlowSOM and ChronoClust, specifically designed for analysing temporal cytometry data

Analysis of high-dimensional cytometry data using tSNE and FlowSOM

R code to demonstrate the FlowSOM analysis pipeline.

Example usage of the FlowSOM package

View the Minimum Spanning Tree based on FlowSOM clustering.

R scripts to use various flow cytometry analyses

No description available

No description available

No description available

Python implementation of the C-optimized FlowSOM library

R code to replicate the tSNE and FlowSOM figures

This directory contains my code for the implementation of FlowSOM on myeloid malignancy patients

A conda-smithy repository for flowsom.

An MCMICRO module for clustering cell types using the flowSOM algorithm

No description available

FlowSOM wrapper

FlowSOM library for python.

Analysisingflow cytometry data using Self Organising Maps

Minimal framework for building and executing data workflows on a single machine

Flow cytometry data clustering using Self-Organizing Maps.

YDAゲームジャムの流しソーメン

No description available