Differential abundance analysis microbiome r

  • Microbiome differential abundance analysis (MDA) is a direct analogy to differential expression analysis for gene expression and RNA-seq data, however, the distinct nature of microbiome data renders classic differential expression analysis methods such as DESeq (Anders and Huber, 2010)...
Differential abundance analysis of predicted microbial functions (using PICRUSt [phylogenetic investigation of communities by reconstruction of unobserved states]) revealed that 3 pathways had higher abundance (log2 fold change, 0.10 to 0.23) whereas 12 pathways had lower abundance (log2 fold change, −0.36 to −0.20) in SS.

Differential abundance analysis is probably the most common objective of microbiome profiling studies and genomics studies in general. The objective is to identify microbial taxa, anywhere on the tree of life, that are over- or underabundant in some condition relative to a reference condition.

Figure 3. Example analysis of amplicon microbiome data. A. Principal coordinates analysis showing microbial community structure between root compartments using Bray-Curtis dissimilarities. Differential abundance analyses were carried out using DESeq2.
  • The mixOmics R package is organised into three main parts: Statistical methodologies to analyse high throughput data (s)PCA: (sparse) Principal Component Analysis as proposed by Shen and Huang 2008. (s)IPCA: independent Principal Component Analysis (r)CCA: (regularized) Canonical Correlation Analysis as implemented in Gonzales et al 2008.
  • OTU differential abundance testing is commonly used to identify OTUs that differ between two mapping file sample categories (i.e. Palm and Tongue body We would recommend having at least 5 samples in each category. methods can be used in comparison to group_significance.pyon a rarefied matrix,
  • Dec 30, 2020 · Introduction: The oral cavity harbors an abundant and diverse microbial community (i.e. the microbiome), whose composition and roles in health and disease have been the focus of intense research.

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    The Integrative Human Microbiome Project: dynamic analysis of microbiome-host omics profiles during periods of human health and disease. Cell Host Microbe 2014 Sep 10. PMID: 25211071

    Rationale The nasopharyngeal (NP) microbiota of newborns and infants plays a key role in modulating airway inflammation and respiratory symptoms during viral infections. Premature (PM) birth modifies the early NP environment and is a major risk factor for severe viral respiratory infections. However, it is currently unknown if the NP microbiota of PM infants is altered relative to full-term ...

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    Apr 21, 2020 · Analysis of differential abundance showed increased abundance for several fungi species in the persistent group compared to the intermittent and non-carriers . However, the species Candida orthopsilosis was the only one with significant difference in abundance between the persistent and non-carrier groups ( p<0.05, Kruskal-Wallis test ...

    RNA-Seq (named as an abbreviation of "RNA sequencing") is a technology-based sequencing technique which uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample at a given moment, analyzing the continuously changing cellular transcriptome.

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    To facilitate the analysis of microbiome metabolic network, mmnet provides func-tions for sequence annotation linking R with MG-RAST. MG-RAST [Glass et al., 2010] is a stable, extensible, online analysis platform for annotation and statistic metagenomes based on sequence data. Moreover, MG-RAST is freely available to all researchers. For

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    16s rRNA Short read libraries target variable V3 and V4 regions of 16s rRNA genes. Although, 16s rRNA sequencing is an amplicon sequencing technique, usually the environment or clinical samples are as clean and need expert hands to process and amplify 16s rRNA genes.

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    Aug 19, 2020 · To visualise the differences in microbial composition between gut contents and tissue, a taxonomic profile was generated by conducting differential abundance analysis using balances in gneiss. v. To identify the features characterising the differences between groups, the LEfSe method of analysis was performed to compare abundances of all bacterial clades [ 53 ].

    Mar 15, 2020 · For each feature, the assay produces a value that is a proxy for the relative abundance of that feature. For genes, this is the number of RNA transcripts that are expressed. Thus I will refer to ...

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    Differential Abundance. Uses DESeq2 to identify differentially abundant taxa, at any taxonomic level, for a user-selected pairwise comparison. Results are displayed as a bubble chart that incorporates statistics.

    In this exploratory analysis, we found similar results to our main analysis in the larger dataset with regard to the differences in genus abundance and richness. The nasal microbiome significantly differed by the Bray-Curtis index at both the genus and OTU levels, and richness remained significantly lower during acute RSV infection ( P < 0.05 for all estimates).

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    Differential abundance testing: univariate data. This section covers basic univariate tests for two-group comparison, covering t-test, Wilcoxon test, and multiple testing. The following example compares the abundance of a selected bug between two conditions. Let us assume that the data is already properly normalized. Let us load example data

    The three highest values of R RS obtained after each dose were averaged to construct dose–response curves. 2.5 Bronchoalveolar lavage. The lungs were lavaged twice with 1 ml of ice‐cold PBS. These two lavages were pooled and centrifuged at 400g at 4°C for 10 min. The supernatant was stored at −80°C until further analysis.

Oct 22, 2020 · Linear discriminant analysis (LDA) effect size (LEfSe) analysis, a method for biomarker discovery, was used to identify differentially abundant bacterial taxa or fungi that best characterize the populations of these groups.
Identify samples across two or more studies for a cross-study meta-analysis. Samples for analysis can be identified by either sample details or taxon abundance. Caution: Informative meta-analysis requires a clear understanding of individual study designs, parameters and methods.
Statistical Modeling of the Microbiome. I work with Daniela Witten and Amy Willis to develop methods for proper statistical analysis of the microbiome. I am the developer of corncob, a regression model and hypothesis testing procedure developed to address the challenges of microbiome data, implemented as an R software package.