32 skills found · Page 1 of 2
tiroshlab / 3caCode for reproducing the analysis in Gavish et al. "The transcriptional hallmarks of intra-tumor heterogeneity across a thousand tumors".
UniprJRC / FSDAFlexible Statistics and Data Analysis (FSDA) extends MATLAB for a robust analysis of data sets affected by different sources of heterogeneity. It is open source software licensed under the European Union Public Licence (EUPL). FSDA is a joint project by the University of Parma and the Joint Research Centre of the European Commission.
stscl / GdverseAnalysis of Spatial Stratified Heterogeneity
raphael-group / THetATumor Heterogeneity Analysis (THetA) and THetA2 are algorithms that estimate the tumor purity and clonal/subclonal copy number aberrations directly from high-throughput DNA sequencing data. This repository includes the updated algorithm, called THetA2.
FredHutch / Galeano Nino Bullman Intratumoral Microbiota 2022Analysis code used in Galeano Nino et al., Impact of Intratumoral Microbiota on Spatial and Cellular Heterogeneity in human cancer. 2022
evarol / HYDRAHeterogeneity through discriminative analysis
3dem / DynaMightTool for reconstruction and analysis of continuous heterogeneity of a cryo-EM dataset
KristinaUlicna / DeepTreeJupyter notebooks for deep lineage analysis of single-cell heterogeneity and cell cycling duration heritability.
franklinhuanglab / ScRNA Seq Analysis Of Prostate Cancer SamplesCode for the manuscript "Single-cell analysis of human primary prostate cancer reveals the heterogeneity of tumor-associated epithelial cell states"
chunhuiz / MiTSformerOfficial implementations of "Addressing Spatial-Temporal Heterogeneity: General Mixed Time Series Analysis via Latent Continuity Recovery and Alignment (NeurIPS 2024)"
lawrenson-lab / AtlasEndometriosisA single-cell transcriptomic analysis of endometriosis, endometriomas, eutopic endometrial samples and uninvolved ovary tissues highlights cell populations characteristic of these tissue types. Transcriptional and cellular heterogeneity across tissues suggests novel therapeutic targets and biomarkers for this disease.
wangjun-hub / CODEX SCLCThis repository includes codes and data example used in the "Integrative Spatial Analysis Reveals Tumor Heterogeneity and Immune Colony Niche Related to Clinical-outcomes in Small Cell Lung Cancer" paper.
anbai106 / MAGICMAGIC: Multi-scAle heteroGeneity analysIs and Clustering
janinemelsen / Single Cell Analysis Flow CytometryTo reveal the cellular heterogeneity within flow cytometry data, clustering and pseudotime analysis can be performed. Here, we demonstrate how to import (clustered) data, how to apply clustering and dimensionality reduction, and how to apply pseudotime analysis by Slingshot.
Zhudogsi / MCL MCFHierarchical Alleviation of Heterogeneity in Multimodal Sentiment Analysis
WuLabMDA / Habitat AnalysisHabitat Analysis is a MATLAB code for identifying intratumoral habitats with distinct heterogeneity using radiographics scans.
pancancer / MyeloidCode repository site for manuscript: Unraveling the Heterogeneity of Tumor-Infiltrating Myeloid Cells in Immune Checkpoint Blockade: A Single-Cell Pan-cancer analysis
yuntianf / LongcellSingle cell isoform heterogeneity and differential alternative splicing analysis
marcgarnica13 / Ml Interpretability European FootballUnderstanding gender differences in professional European football through Machine Learning interpretability and match actions data. This repository contains the full data pipeline implemented for the study *Understanding gender differences in professional European football through Machine Learning interpretability and match actions data*. We evaluated European male, and female football players' main differential features in-match actions data under the assumption of finding significant differences and established patterns between genders. A methodology for unbiased feature extraction and objective analysis is presented based on data integration and machine learning explainability algorithms. Female (1511) and male (2700) data points were collected from event data categorized by game period and player position. Each data point included the main tactical variables supported by research and industry to evaluate and classify football styles and performance. We set up a supervised classification pipeline to predict the gender of each player by looking at their actions in the game. The comparison methodology did not include any qualitative enrichment or subjective analysis to prevent biased data enhancement or gender-related processing. The pipeline had three representative binary classification models; A logic-based Decision Trees, a probabilistic Logistic Regression and a multilevel perceptron Neural Network. Each model tried to draw the differences between male and female data points, and we extracted the results using machine learning explainability methods to understand the underlying mechanics of the models implemented. A good model predicting accuracy was consistent across the different models deployed. ## Installation Install the required python packages ``` pip install -r requirements.txt ``` To handle heterogeneity and performance efficiently, we use PySpark from [Apache Spark](https://spark.apache.org/). PySpark enables an end-user API for Spark jobs. You might want to check how to set up a local or remote Spark cluster in [their documentation](https://spark.apache.org/docs/latest/api/python/index.html). ## Repository structure This repository is organized as follows: - Preprocessed data from the two different data streams is collecting in [the data folder](data/). For the Opta files, it contains the event-based metrics computed from each match of the 2017 Women's Championship and a single file calculating the event-based metrics from the 2016 Men's Championship published [here](https://figshare.com/collections/Soccer_match_event_dataset/4415000/5). Even though we cannot publish the original data source, the two python scripts implemented to homogenize and integrate both data streams into event-based metrics are included in [the data gathering folder](data_gathering/) folder contains the graphical images and media used for the report. - The [data cleaning folder](data_cleaning/) contains descriptor scripts for both data streams and [the final integration](data_cleaning/merger.py) - [Classification](classification/) contains all the Jupyter notebooks for each model present in the experiment as well as some persistent models for testing.
alncat / OpusTomostructural heterogeneity analysis and reconstruction for cryo-ET subtomogram
