Pathway enrichment analysis / visualize for Single Cell Data
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Updated
Aug 20, 2024 - R
Pathway enrichment analysis / visualize for Single Cell Data
manage ordinations and render biplots in a tidyverse workflow
The main goal of this project is to use various Clustering Methods for Bank Customer Segmentation.
pca: A Python Package for Principal Component Analysis.
Your one-stop solution for comprehensive genotype × environment interaction and stability analysis (AMMI model) using the metan package. Save time, perform analyses for all traits at once, and gain insightful visualizations effortlessly!"
A ggplot2 based biplot for principal components-like methods
Package for conducting PCA incl. scree-plot and bi-plot
Exploratory Data Analysis using machine learning techniques as an exercise for GLY6932 (Data Science and Machine Learning Methods in the Geosciences) at the University of Florida.
VTubers as influencers might sound naive back then, but nowadays their presence is all around us. So, what are they?
A fast and scalable phylogenetic tree viewer for microbiome data analysis
Worked with fellow peers at University of Pittsburgh under supervision of Dr. Junshu Bao, Department of Statistics, University of Pittsburgh, to do a Differential Gene Expression Analysis on a Dexamethasone treatment data set and incorporated Machine Learning into project.
Text Mining and Analysis with Biplots.
This repository contains materials associated to the course "Multivariate Analysis" taught at the Faculty of Mathematics and Statistics (FME), UPC under the MESIO-UPC-UB Interuniversity Program under the instructors "Ferran Revertar", "Miguel Salicru" and "Jan Graffelman"
R package, creation of customizable biplots, both in two and three dimensions. Tested in Windows 7 and Mac OS X 10.9.5.
Example of Biplot Graph using R
Machine Learning Nano-degree Project : To identify customer segments hidden in product spending data collected for customers of a wholesale distributor
Biplots, Volcano plots, PCA plots, Heatmaps and more Computational Genomics data created and visualized during University of Pittsburgh course, Computational Biology (BIOSC1540), with Dr. Miler Lee
Machine Learning Engineer Nanodegree, Unsupervised Learning, Creating Customer Segments
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