About this Research Topic
The last few years have seen a simultaneous explosion of high-throughput single-cell as well as metagenomics sequencing technologies for quantifying DNA/RNA levels within individual cells/species. These breakthroughs together with other ‘omics studies (such as proteomics, metabolomics, etc.) pave the way for exploring biological systems at an unprecedented level of detail. While single-cell technologies allow us to look closely into inter-cellular variations and interactions and intra-tissue heterogeneity in biological samples, metagenomics sequencing enables in-depth studies of microbial ecosystems in much finer resolution than previously envisioned. Both these technologies have independently led to the development of novel computational and statistical methods that encompass data preprocessing, modeling, and inference. Despite the progress, there is still much work to be done to meet the challenges and make use of the opportunities posed by the resulting new data types.
Although there are special issues in statistical and computational biology journals focusing on each individual data analysis, researchers from these fields as well as practitioners of these technologies would greatly benefit from a combined issue, where it is possible to exchange ideas, raise new questions, and form future collaborations, building upon the lessons learned in one field and reverse-translating the gained knowledge from one field to the other. This is particularly relevant as both these technologies generate data that have very similar downstream characteristics: they are typically noisy, heterogeneous, sparse, and zero-inflated, with confounding effects unique to each individual layer. Due to these apparent similarities, many of the methods developed in the microbiome field has been successfully transported to the single-cell field (with necessary modifications) and vice versa and in recent years many researchers have made the seamless transition from one field to the other.
As a continuation of the successful first edition of the microbiome research topic, through this special issue we aim to bring together the best of both worlds by combining cutting-edge technological and computational advancements in each of these respective fields, to (i) identify new challenges in data analysis and modeling, (ii) provide a platform for interdisciplinary dialogue, and (iii) help shape future directions for these burgeoning fields, including but not limited to:
• Novel statistical and computational approaches for single-cell data analysis
• Novel statistical and computational approaches for microbiome data analysis
• Unified methods for microbiome and single-cell data analysis
• Longitudinal modeling of the microbiome and single-cell sequencing data, possibly in combination with other ‘omics data (e.g. metabolomics, proteomics)
• Integrative analysis methods combining multiple ‘omics with at least one microbiome or single-cell data modality
Topic Editor Himel Malick is employed by Merck. All other Topic Editors declare no competing interests with regard to the Research Topic subject.
Although there are special issues in statistical and computational biology journals focusing on each individual data analysis, researchers from these fields as well as practitioners of these technologies would greatly benefit from a combined issue, where it is possible to exchange ideas, raise new questions, and form future collaborations, building upon the lessons learned in one field and reverse-translating the gained knowledge from one field to the other. This is particularly relevant as both these technologies generate data that have very similar downstream characteristics: they are typically noisy, heterogeneous, sparse, and zero-inflated, with confounding effects unique to each individual layer. Due to these apparent similarities, many of the methods developed in the microbiome field has been successfully transported to the single-cell field (with necessary modifications) and vice versa and in recent years many researchers have made the seamless transition from one field to the other.
As a continuation of the successful first edition of the microbiome research topic, through this special issue we aim to bring together the best of both worlds by combining cutting-edge technological and computational advancements in each of these respective fields, to (i) identify new challenges in data analysis and modeling, (ii) provide a platform for interdisciplinary dialogue, and (iii) help shape future directions for these burgeoning fields, including but not limited to:
• Novel statistical and computational approaches for single-cell data analysis
• Novel statistical and computational approaches for microbiome data analysis
• Unified methods for microbiome and single-cell data analysis
• Longitudinal modeling of the microbiome and single-cell sequencing data, possibly in combination with other ‘omics data (e.g. metabolomics, proteomics)
• Integrative analysis methods combining multiple ‘omics with at least one microbiome or single-cell data modality
Topic Editor Himel Malick is employed by Merck. All other Topic Editors declare no competing interests with regard to the Research Topic subject.
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