Skip to main navigation
Skip to search
Skip to main content
Icahn School of Medicine at Mount Sinai Home
Help & FAQ
Home
Profiles
Research units
Publications & Research Outputs
Press/Media
Search by expertise, name or affiliation
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
17
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Differential expression of single-cell RNA-seq data using Tweedie models'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Benchmark Evaluation
20%
Bioconductor Package
20%
Computational Methods
20%
Computational Software
20%
Differential Expression
100%
Differentially Expressed
20%
Droplet-based
20%
Excessive Zeros
20%
Expression Distribution
20%
Expression Features
20%
Expression Method
40%
Expression Profile
20%
False Discovery Rate Control
20%
Gene Expression
40%
Gene-specific
20%
Heavy Tails
20%
Individual Cells
20%
Large Dynamic Range
20%
Library Preparation
20%
Normalization Method
20%
Plate-like
20%
R Software
20%
Single-cell RNA Sequencing (scRNA-seq)
80%
Single-cell RNA Sequencing Data
40%
Single-cell RNA-seq Data
100%
Sparsity
20%
Statistical Power
20%
Statistical Properties
20%
Technological Variability
20%
Tweedie Distribution
20%
Tweedie Generalized Linear Model
20%
Tweedie Model
20%
Zero Counts
20%
Zero Probability
20%
Zero-inflated
40%
Computer Science
Computational Method
100%
Evaluation Benchmark
100%
False Discovery
100%
Individual Cell
100%
Open Source Software
100%
Probability Mass
100%
Sparsity
100%
Statistical Property
100%
Engineering
Dynamic Range
33%
Experimental Platform
100%
Gene Expression Profile
33%
Individual Cell
33%
Rate Control
33%
Sparsity
33%
Statistical Property
33%
Neuroscience
False Discovery Rate
16%
Gene Expression
33%
Generalized Linear Model
16%
RNA Sequence
100%
RNA-Seq
100%
Biochemistry, Genetics and Molecular Biology
Bioconductor
16%
Gene Expression
33%
RNA Sequence
100%
RNA Sequencing
100%