PyDESeq2: a python package for bulk RNA-seq differential expression analysis

Muzellec, Boris; Teleńczuk, Maria; Cabeli, Vincent; Andreux, Mathieu

doi:10.1101/2022.12.14.520412

Cited by 36 publications

(36 citation statements)

References 10 publications

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“…Tukey’s test, a post hoc analysis performed following an ANOVA test, was carried out with the Statsmodels (31) Python module. The calculation of log2FoldChange with Wald tests between groups, which provides insight into the magnitude and direction of changes, was performed using the PyDESeq2(36) package.…”

Section: Methodsmentioning

confidence: 99%

MetaX: A peptide centric metaproteomic data analysis platform using Operational Taxa-Functions (OTF)

Wu,

Ning,

Zhang

et al. 2024

Preprint

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Metaproteomics analyzes the functional dynamics of microbial communities by identifying peptides and mapping them to the most likely proteins and taxa. The challenge in this field lies in seamlessly integrating taxonomic and functional annotations to accurately represent the contributions of individual microbial taxa to functional diversity. We introduce MetaX, a comprehensive tool for analyzing taxa-function relationships in metaproteomics by mapping peptides to their lowest common ancestors and assigning functions based on proportional thresholds, ensuring accurate peptide-level mappings. Importantly, MetaX introduces the Operational Taxa-Functions (OTF), a new conceptual unit for exploring microbial roles and interactions within ecosystems. Additionally, MetaX extends traditional taxonomic classification by adding a genome level below the species level, enhancing the accuracy of function attribution to specific genomes. We demonstrated MetaX by reanalyzing metaproteomic data from gut microbiomes exposed to various sweeteners, achieving results similar to traditional protein analysis. Furthermore, using the peptide-centric approach and OTF, we observed that Parabacteroides distasonis significantly responds to certain sweeteners, highlighting its role in modifying specific metabolic functions. With its intuitive, user-friendly interface, MetaX facilitates detailed study of the complex interactions between microbial taxa and their functions in metaproteomics. It enhances our understanding of microbial roles in ecosystems and health.

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Section: Methodsmentioning

confidence: 99%

MetaX: A peptide centric metaproteomic data analysis platform using Operational Taxa-Functions (OTF)

Wu,

Ning,

Zhang

et al. 2024

Preprint

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“…For all remaining datasets, for which DESeq2 results were not already available, the authors did provide mapped count data on GEO and/or they were available on GREIN. For these count-based results we implemented DESeq2 ourselves, using the PyDESeq2 package, using default settings, comparing knockout samples against the matched controls from their respective studies 104 .…”

Section: Methodsmentioning

confidence: 99%

Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells

Mellis

Bodkin²,

Goyal³

2023

Preprint

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Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene can trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, to date, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and contexts. Moreover, how the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear. Here we combine computational analysis of existing datasets with stochastic mathematical modeling and machine learning to uncover the widespread prevalence of transcriptional adaptation in mammalian systems and the diverse single-cell manifestations of minimal compensatory gene networks. Regulon gene expression analysis of a pooled single-cell genetic perturbation dataset recapitulates our model predictions. Our integrative approach uncovers several putative hits-genes demonstrating possible transcriptional adaptation-to follow up on experimentally, and provides a formal quantitative framework to test and refine models of transcriptional adaptation.

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“…To identify genes that are differentially expressed between cell types, we generated pseudo-bulk transcriptome of each annotated cell type in individual sample id. We used pyDESEQ2 46 to compare two clusters or types using the Wald test and identified genes specifically expressed in each cluster or type. Differentially expressed genes are identified under q-value < 0.05.…”

Section: Differentially Expressed Gene Analysismentioning

confidence: 99%

Comprehensive single-cell atlas of the mouse retina

Li,

Choi,

Cheng

et al. 2024

Preprint

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Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of cellular heterogeneity at the single-cell resolution by classifying and characterizing cell types in multiple tissues and species. While several mouse retinal scRNA-seq reference datasets have been published, each dataset either has a relatively small number of cells or is focused on specific cell classes, and thus is suboptimal for assessing gene expression patterns across all retina types at the same time. To establish a unified and comprehensive reference for the mouse retina, we first generated the largest retinal scRNA-seq dataset to date, comprising approximately 190,000 single cells from C57BL/6J mouse whole retinas. This dataset was generated through the targeted enrichment of rare population cells via antibody-based magnetic cell sorting. By integrating this new dataset with public datasets, we conducted an integrated analysis to construct the Mouse Retina Cell Atlas (MRCA) for wild-type mice, which encompasses over 330,000 single cells. The MRCA characterizes 12 major classes and 138 cell types. It captured consensus cell type characterization from public datasets and identified additional new cell types. To facilitate the public use of the MRCA, we have deposited it in CELLxGENE, UCSC Cell Browser, and the Broad Single Cell Portal for visualization and gene expression exploration. The comprehensive MRCA serves as an easy-to-use, one-stop data resource for the mouse retina communities.

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PyDESeq2: a python package for bulk RNA-seq differential expression analysis

Cited by 36 publications

References 10 publications

MetaX: A peptide centric metaproteomic data analysis platform using Operational Taxa-Functions (OTF)

MetaX: A peptide centric metaproteomic data analysis platform using Operational Taxa-Functions (OTF)

Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells

Comprehensive single-cell atlas of the mouse retina

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