Jonathan Ronen
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View article: Decoding single-cell multiomics: scMaui - A deep learning framework for uncovering cellular heterogeneity in presence of batch Effects and missing data
Decoding single-cell multiomics: scMaui - A deep learning framework for uncovering cellular heterogeneity in presence of batch Effects and missing data Open
The recent advances in high-throughput single-cell sequencing has significantly required computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are re…
View article: Identifying tumor cells at the single-cell level using machine learning
Identifying tumor cells at the single-cell level using machine learning Open
Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Single-cell sequencing enables molecular characterization of single cells within the tumor. However, cell annot…
View article: Additional file 2 of Identifying tumor cells at the single-cell level using machine learning
Additional file 2 of Identifying tumor cells at the single-cell level using machine learning Open
Additional file 2. Tables with data description and results.
View article: Additional file 3 of Identifying tumor cells at the single-cell level using machine learning
Additional file 3 of Identifying tumor cells at the single-cell level using machine learning Open
Additional file 3. Results of statistical analysis.
View article: Identifying tumor cells at the single cell level
Identifying tumor cells at the single cell level Open
Tumors are highly complex tissues composed of cancerous cells, surrounded by a heterogeneous cellular microenvironment. Tumor response to treatments is governed by an interaction of cancer cell intrinsic factors with external influences of…
View article: Multi-omics and deep learning provide a multifaceted view of cancer
Multi-omics and deep learning provide a multifaceted view of cancer Open
Cancer is a complex disease with a large financial and healthcare burden on society. One hallmark of the disease is the uncontrolled growth and proliferation of malignant cells. Unlike Mendelian diseases which may be explained by a few gen…
View article: Functional interplay of Epstein-Barr virus oncoproteins in a mouse model of B cell lymphomagenesis
Functional interplay of Epstein-Barr virus oncoproteins in a mouse model of B cell lymphomagenesis Open
Epstein-Barr virus (EBV) is a B cell transforming virus that causes B cell malignancies under conditions of immune suppression. EBV orchestrates B cell transformation through its latent membrane proteins (LMPs) and Epstein-Barr nuclear ant…
View article: Evaluation of colorectal cancer subtypes and cell lines using deep learning
Evaluation of colorectal cancer subtypes and cell lines using deep learning Open
Colorectal cancer (CRC) is a common cancer with a high mortality rate and a rising incidence rate in the developed world. Molecular profiling techniques have been used to better understand the variability between tumors and disease models …
View article: paris-control-network-stats.csv
paris-control-network-stats.csv Open
Network statistics for paris control set
View article: paris-control-network-stats-excl-verified.csv.stats.csv
paris-control-network-stats-excl-verified.csv.stats.csv Open
Network statistics for paris control set (excluding verified accounts)
View article: france-coords.csv
france-coords.csv Open
CSV file with coordinates of all geo-tagged tweets sent by france control set
View article: 30_Main text and SI tables and figures 1 (R).ipynb
30_Main text and SI tables and figures 1 (R).ipynb Open
Jupyter notebook for analysis and figures in main text (original ipynb file)
View article: paris-geostats.csv
paris-geostats.csv Open
CSV file with proportions of tweets which are geotagged for each user in the paris control set
View article: france-control.nodesdata
france-control.nodesdata Open
Metadata for france control set network
View article: 40_Supporting Information Figures (Py).ipynb
40_Supporting Information Figures (Py).ipynb Open
Jupyter notebook for analysis and figures in SI (original ipynb file)
View article: run-all.sh
run-all.sh Open
Script that runs the complete analysis
View article: M_global.mtx
M_global.mtx Open
Adjacency matrix of users and the most-followed-accounts they follow
View article: charlie-hebdo-tweets-per-user-around-march.mtx
charlie-hebdo-tweets-per-user-around-march.mtx Open
Sparse matrix [users X days] with counts of tweets sent by user i on day
View article: 40_Supporting Information Figures (Py).pdf
40_Supporting Information Figures (Py).pdf Open
Jupyter notebook for analysis and figures in SI (PDF format)
View article: france-coords.agg.csv
france-coords.agg.csv Open
CSV file with coordinates of all geo-tagged tweets sent by the france control set users aggregated by date
View article: france-control-network-stats.csv
france-control-network-stats.csv Open
Network statistics for france control set
View article: attenders-coords.agg.csv
attenders-coords.agg.csv Open
CSV file with coordinates of all geo-tagged tweets sent by protest attenders aggregated by date
View article: charlie-hebdo-replication.tar.gz
charlie-hebdo-replication.tar.gz Open
In order to execute the replication materials, a comprehensive archive is supplied. Please extract this file, as it contains all necessary files and the correct directory structure.
View article: 30_Main text and SI tables and figures 1 (R).html
30_Main text and SI tables and figures 1 (R).html Open
Jupyter notebook for analysis and figures in main text (HTML format)
View article: france-control.adjlist
france-control.adjlist Open
Adjacency list of the france control set (see readme)
View article: attenders-network-stats.csv
attenders-network-stats.csv Open
Network statistics for attenders dataset
View article: 10_find_most_followed_accounts.py
10_find_most_followed_accounts.py Open
Python script which identifies the most followed accounts (see readme, run-all.sh, and the script itself for more info)
View article: readme.pdf
readme.pdf Open
Start here.
View article: attenders-network-stats-excl-verified.csv
attenders-network-stats-excl-verified.csv Open
Network statistics for attenders dataset (excluding verified accounts)