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Differential expression of single-cell RNA-seq data using Tweedie models
Himel Mallick
, Suvo Chatterjee
,
Shrabanti Chowdhury
, Saptarshi Chatterjee
, Ali Rahnavard
, Stephanie C. Hicks
Icahn School of Medicine at Mount Sinai
Genetics and Genomic Sciences
Research output
:
Contribution to journal
›
Article
›
peer-review
21
Scopus citations
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Dive into the research topics of 'Differential expression of single-cell RNA-seq data using Tweedie models'. Together they form a unique fingerprint.
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Computer Science
Sparsity
100%
Open Source Software
100%
Computational Method
100%
Statistical Property
100%
False Discovery
100%
Individual Cell
100%
Evaluation Benchmark
100%
Probability Mass
100%
Keyphrases
Differential Expression
100%
Single-cell RNA Sequencing (scRNA-seq)
100%
Expression Method
28%
Zero-inflated
28%
Gene Expression
14%
Expression Profile
14%
Differentially Expressed
14%
Gene-specific
14%
Individual Cells
14%
Statistical Power
14%
Computational Methods
14%
Sparsity
14%
Expression Features
14%
Plate-like
14%
R Software
14%
Normalization Method
14%
Large Dynamic Range
14%
Library Preparation
14%
Expression Distribution
14%
False Discovery Rate Control
14%
Computational Software
14%
Heavy Tails
14%
Statistical Properties
14%
Droplet-based
14%
Zero Counts
14%
Excessive Zeros
14%
Zero Probability
14%
Benchmark Evaluation
14%
Technological Variability
14%
Tweedie Generalized Linear Model
14%
Tweedie Distribution
14%
Tweedie Model
14%
Platform-based
14%
R/Bioconductor Package
14%
Neuroscience
RNA Sequence
100%
RNA-Seq
100%
Gene Expression
33%
False Discovery Rate
16%
Generalized Linear Model
16%
Biochemistry, Genetics and Molecular Biology
RNA Sequence
100%
RNA Sequencing
100%
Gene Expression
33%
Bioconductor
16%
Mathematics
Heavy Tail
20%