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Information Theoretic Feature Selection Methods for Single Cell RNA-Sequencing

https://doi.org/10.1101/646919
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/11744023_6openalex-snapshot
Conditional Infomax Learning: An Integrated Framework for Feature Extraction and Fusion
inferredcites →https://doi.org/10.1007/978-0-387-84858-7openalex-snapshot
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
inferredcites →https://doi.org/10.1007/bf01588971openalex-snapshot
An analysis of approximations for maximizing submodular set functions—I
inferredcites →https://doi.org/10.1016/j.cell.2015.05.002openalex-snapshot
Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
inferredcites →https://doi.org/10.1016/j.cell.2015.11.013openalex-snapshot
Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors
inferredcites →https://doi.org/10.1016/j.devcel.2018.07.025openalex-snapshot
A Comprehensive Roadmap of Murine Spermatogenesis Defined by Single-Cell RNA-Seq
inferredcites →https://doi.org/10.1016/s0019-9958(75)80004-0openalex-snapshot
Linear dependence structure of the entropy space
inferredcites →https://doi.org/10.1016/s0304-3975(97)00115-1openalex-snapshot
On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
inferredcites →https://doi.org/10.1038/nbt.3192openalex-snapshot
Spatial reconstruction of single-cell gene expression data
inferredcites →https://doi.org/10.1038/ncomms14049openalex-snapshot
Massively parallel digital transcriptional profiling of single cells
inferredcites →https://doi.org/10.1038/nmeth.1253openalex-snapshot
Imaging individual mRNA molecules using multiple singly labeled probes
inferredcites →https://doi.org/10.1038/nmeth.2869openalex-snapshot
Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry
inferredcites →https://doi.org/10.1038/nmeth.4179openalex-snapshot
Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput
inferredcites →https://doi.org/10.1038/s41586-019-1049-yopenalex-snapshot
Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+
inferredcites →https://doi.org/10.1101/258566openalex-snapshot
Identification of transcriptional signatures for cell types from single-cell RNA-Seq
inferredcites →https://doi.org/10.1109/72.298224openalex-snapshot
Using mutual information for selecting features in supervised neural net learning
inferredcites →https://doi.org/10.1109/tpami.2005.159openalex-snapshot
Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
inferredcites →https://doi.org/10.1126/science.1198704openalex-snapshot
Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum
inferredcites →https://doi.org/10.1126/science.aaa1934openalex-snapshot
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
inferredcites →https://doi.org/10.1186/s13059-017-1382-0openalex-snapshot
SCANPY: large-scale single-cell gene expression data analysis
inferredcites →https://doi.org/10.12688/f1000research.15666.1openalex-snapshot
A systematic performance evaluation of clustering methods for single-cell RNA-seq data
inferredcites →https://doi.org/10.12688/f1000research.15666.2openalex-snapshot
A systematic performance evaluation of clustering methods for single-cell RNA-seq data
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python

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inferred← citeshttps://doi.org/10.1093/bib/bbab295openalex-snapshot
Selecting gene features for unsupervised analysis of single-cell gene expression data
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