Paper Citation Record · LEDGER

Group-wise normalization in differential abundance analysis of microbiome samples

As of 13 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2411.15400.

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pith.paper-citation-record.v1
2411.15400 v1

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Outbound references

Observation 347e11bf-4908-456e-8ac7-2398d1dec392 · outbound

This paper cites Gut microbiome and health: mechanistic insights.

Group-wise normalization in differential abundance analysis of microbiome samples Gut microbiome and health: mechanistic insights

Reference 1

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Observation 8f316521-84a8-4e2b-b43d-febe85af5ed2 · outbound

This paper cites The Human Microbiome: Our Second Genome.

Group-wise normalization in differential abundance analysis of microbiome samples The Human Microbiome: Our Second Genome

Reference 2

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Observation 4a15e482-d7ef-43d7-ac6b-55da9596ea7a · outbound

This paper cites Compositional data analysis of the microbiome: fundamentals, tools, and challenges.

Group-wise normalization in differential abundance analysis of microbiome samples Compositional data analysis of the microbiome: fundamentals, tools, and challenges

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Observation 32642ee3-434a-4091-a0c3-729800fa3c5d · outbound

This paper cites Microbiome Datasets Are Compositional: And This Is Not Optional.

Group-wise normalization in differential abundance analysis of microbiome samples Microbiome Datasets Are Compositional: And This Is Not Optional

Reference 4

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This paper cites Analysis of compositions of microbiomes with bias cor- rection.

Group-wise normalization in differential abundance analysis of microbiome samples Analysis of compositions of microbiomes with bias cor- rection

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This paper cites A comprehensive evaluation of microbial differential abun- dance analysis methods: current status and potential solutions.

Group-wise normalization in differential abundance analysis of microbiome samples A comprehensive evaluation of microbial differential abun- dance analysis methods: current status and potential solutions

Reference 6

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Observation 0e67b4f6-04f1-4ff7-a21e-a99bd8e59b52 · outbound

This paper cites edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

Group-wise normalization in differential abundance analysis of microbiome samples edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

Reference 7

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This paper cites Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.

Group-wise normalization in differential abundance analysis of microbiome samples Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

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This paper cites Differential abundance analysis for microbial marker-gene surveys.

Group-wise normalization in differential abundance analysis of microbiome samples Differential abundance analysis for microbial marker-gene surveys

Reference 9

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Observation 39aab974-86e6-47c5-b794-9a522d4ee72b · outbound

This paper cites A review of normal- ization and differential abundance methods for microbiome counts data.

Group-wise normalization in differential abundance analysis of microbiome samples A review of normal- ization and differential abundance methods for microbiome counts data

Reference 10

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This paper cites LinDA: linear models for differential abundance analysis of microbiome compositional data.

Group-wise normalization in differential abundance analysis of microbiome samples LinDA: linear models for differential abundance analysis of microbiome compositional data

Reference 11

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Observation 0f4dc129-7e64-41ef-b5c6-2427d20220ff · outbound

This paper cites Differential expression analysis for sequence count data.

Group-wise normalization in differential abundance analysis of microbiome samples Differential expression analysis for sequence count data

Reference 12

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Observation 687c4f19-81d9-4039-9d38-2ab6dd35f3bf · outbound

This paper cites A Survey of Statis- tical Methods for Microbiome Data Analysis.

Group-wise normalization in differential abundance analysis of microbiome samples A Survey of Statis- tical Methods for Microbiome Data Analysis

Reference 13

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This paper cites A robust approach for identifying differentially abundant features in metagenomic samples.

Group-wise normalization in differential abundance analysis of microbiome samples A robust approach for identifying differentially abundant features in metagenomic samples

Reference 14

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This paper cites GMPR: A robust nor- malization method for zero-inflated count data with application to microbiome sequencing data.

Group-wise normalization in differential abundance analysis of microbiome samples GMPR: A robust nor- malization method for zero-inflated count data with application to microbiome sequencing data

Reference 15

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This paper cites Analysis and correction of compositional bias in sparse sequencing count data.

Group-wise normalization in differential abundance analysis of microbiome samples Analysis and correction of compositional bias in sparse sequencing count data

Reference 16

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This paper cites A scaling normalization method for differential expression analysis of RNA-seq data.

Group-wise normalization in differential abundance analysis of microbiome samples A scaling normalization method for differential expression analysis of RNA-seq data

Reference 17

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This paper cites Analysis of microbial compositions: a review of normalization and differential abundance analysis.

Group-wise normalization in differential abundance analysis of microbiome samples Analysis of microbial compositions: a review of normalization and differential abundance analysis

Reference 18

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This paper cites Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.

Group-wise normalization in differential abundance analysis of microbiome samples Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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This paper cites Bayesian variable selec- tion for multivariate zero-inflated models: Application to microbiome count data.

Group-wise normalization in differential abundance analysis of microbiome samples Bayesian variable selec- tion for multivariate zero-inflated models: Application to microbiome count data

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This paper cites The Poisson-Lognormal Model as a Versatile Framework for the Joint Analysis of Species Abundances.

Group-wise normalization in differential abundance analysis of microbiome samples The Poisson-Lognormal Model as a Versatile Framework for the Joint Analysis of Species Abundances

Reference 21

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This paper cites Multi-Variate Probit Analysis.

Group-wise normalization in differential abundance analysis of microbiome samples Multi-Variate Probit Analysis

Reference 22

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Group-wise normalization in differential abundance analysis of microbiome samples Unresolved cited work

Reference 23

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Group-wise normalization in differential abundance analysis of microbiome samples The association between oral disease and type of antiretroviral therapy among perinatally HIV-infected youth

Reference 24

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Group-wise normalization in differential abundance analysis of microbiome samples Sali- vary metabolite levels in perinatally HIV-infected youth with periodontal disease

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This paper cites Global patterns of 16S rRNA diversity at a depth of millions 17 of sequences per sample.

Group-wise normalization in differential abundance analysis of microbiome samples Global patterns of 16S rRNA diversity at a depth of millions 17 of sequences per sample

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Group-wise normalization in differential abundance analysis of microbiome samples Microbiomes of Endodontic-Periodontal Lesions before and after Chemomechanical Preparation

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Group-wise normalization in differential abundance analysis of microbiome samples The Human Oral Microbiome

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This paper cites Dietary lignans, plasma enterolactone levels, and metabolic risk in men: exploring the role of the gut microbiome.

Group-wise normalization in differential abundance analysis of microbiome samples Dietary lignans, plasma enterolactone levels, and metabolic risk in men: exploring the role of the gut microbiome

Reference 29

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Group-wise normalization in differential abundance analysis of microbiome samples Plant-Based Diet Index and Metabolic Risk in Men: Exploring the Role of the Gut Microbiome

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Group-wise normalization in differential abundance analysis of microbiome samples Stability of the human faecal microbiome in a cohort of adult men

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Group-wise normalization in differential abundance analysis of microbiome samples The Mind–Body Study: study design and reproducibility and interrela- tionships of psychosocial factors in the Nurses’ Health Study II

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Group-wise normalization in differential abundance analysis of microbiome samples Gut feelings: associations of emotions and emotion regulation with the gut microbiome in women

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Group-wise normalization in differential abundance analysis of microbiome samples Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4

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Group-wise normalization in differential abundance analysis of microbiome samples Inte- gration of epidemiological and blood biomarker analysis links haem iron intake to increased type 2 diabetes risk

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Group-wise normalization in differential abundance analysis of microbiome samples Moderate alco- hol consumption, types of beverages and drinking pattern with cardiometabolic biomarkers in three cohorts of US men and women

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This paper cites Comparison of normal- ization methods for the analysis of metagenomic gene abundance data.

Group-wise normalization in differential abundance analysis of microbiome samples Comparison of normal- ization methods for the analysis of metagenomic gene abundance data

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