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Negative binomial regression
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Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of molecules detected in each cell, which can confound biological heterogeneity with technical effects. To address this, we present a modeling framework for the normalization and variance stabilization of molecular count data from scRNA-seq experiments.
15p
vielonmusk
30-01-2022
8
0
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Identifying frequently mutated regions is a key approach to discover DNA elements influencing cancer progression.However, it is challenging to identify these burdened regions due to mutation rate heterogeneity across the genome and across different individuals.
25p
vikentucky2711
24-11-2020
9
1
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Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated.
13p
vioklahoma2711
19-11-2020
12
1
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Inference of gene regulatory network structures from RNA-Seq data is challenging due to the nature of the data, as measurements take the form of counts of reads mapped to a given gene. Here we present a model for RNA-Seq time series data that applies a negative binomial distribution for the observations, and uses sparse regression with a horseshoe prior to learn a dynamic Bayesian network of interactions between genes.
12p
viconnecticut2711
28-10-2020
19
0
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Deep sequencing of transposon mutant libraries (or TnSeq) is a powerful method for probing essentiality of genomic loci under different environmental conditions. Various analytical methods have been described for identifying conditionally essential genes whose tolerance for insertions varies between two conditions.
15p
vicolorado2711
23-10-2020
16
1
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With the rapid development of single-cell genomics, technologies for parallel sequencing of the transcriptome and genome in each single cell is being explored in several labs and is becoming available. This brings us the opportunity to uncover association between genotypes and gene expression phenotypes at singlecell level by eQTL analysis on single-cell data.
12p
vicolorado2711
22-10-2020
19
0
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In this article, our primary interest is to compare and discuss about the criteria for selecting model and its applications. The authors provide approaches and procedures of these methods and apply to the tra c violation data where we look for the most appropriate model among Poisson regression.
11p
vioishi2711
01-07-2019
20
0
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Regional determinants of FDI location in Vietnam. This paper examines empirically determinants of foreign direct investment (FDI) location in Vietnam. Based on a panel dataset of 63 provinces and cities in Vietnam from 2008 to 2012, linear regression models for panel data (fixed-effects and random-effects) and negative binomial models are applied in analysis.
19p
tranminhluanluan
28-05-2018
50
6
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Lecture "Advanced Econometrics (Part II) - Chapter 6: Models for count data" presentation of content: Poisson regression model, goodness of fit, overdispersion, negative binomial regression model, too many zeros data.
7p
nghe123
06-05-2016
77
3
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