標題: Titlebook: Bioinformatic and Statistical Analysis of Microbiome Data; From Raw Sequences t Yinglin Xia,Jun Sun Book 2023 Springer Nature Switzerland A [打印本頁] 作者: incompatible 時間: 2025-3-21 18:22
書目名稱Bioinformatic and Statistical Analysis of Microbiome Data影響因子(影響力)
書目名稱Bioinformatic and Statistical Analysis of Microbiome Data影響因子(影響力)學(xué)科排名
書目名稱Bioinformatic and Statistical Analysis of Microbiome Data網(wǎng)絡(luò)公開度
書目名稱Bioinformatic and Statistical Analysis of Microbiome Data網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Bioinformatic and Statistical Analysis of Microbiome Data被引頻次
書目名稱Bioinformatic and Statistical Analysis of Microbiome Data被引頻次學(xué)科排名
書目名稱Bioinformatic and Statistical Analysis of Microbiome Data年度引用
書目名稱Bioinformatic and Statistical Analysis of Microbiome Data年度引用學(xué)科排名
書目名稱Bioinformatic and Statistical Analysis of Microbiome Data讀者反饋
書目名稱Bioinformatic and Statistical Analysis of Microbiome Data讀者反饋學(xué)科排名
作者: Talkative 時間: 2025-3-21 22:46 作者: 健壯 時間: 2025-3-22 01:30
Bioinformatic and Statistical Analysis of Microbiome Data978-3-031-21391-5作者: Excise 時間: 2025-3-22 04:57 作者: Peak-Bone-Mass 時間: 2025-3-22 11:34 作者: 特別容易碎 時間: 2025-3-22 16:18 作者: 得意人 時間: 2025-3-22 20:32 作者: 舞蹈編排 時間: 2025-3-23 01:03 作者: febrile 時間: 2025-3-23 05:10 作者: 發(fā)牢騷 時間: 2025-3-23 08:30
978-3-031-21393-9Springer Nature Switzerland AG 2023作者: 粗糙濫制 時間: 2025-3-23 12:03 作者: 多節(jié) 時間: 2025-3-23 15:28
http://image.papertrans.cn/b/image/187141.jpg作者: 的’ 時間: 2025-3-23 18:21
https://doi.org/10.1007/978-3-8350-9661-5This chapter describes the foundations of the QIIME 2 approach for bioinformatic and biostatistical analyses of microbiome data. It first provides an overview of QIIME 2, and then introduces the core concepts in QIIME 2 and the installation of QIIME 2. Next it introduces how to store, track, and extract data in QIIME 2.作者: 喃喃而言 時間: 2025-3-23 22:32 作者: itinerary 時間: 2025-3-24 03:45
Karriereziel HochschulprofessurThis chapter introduces two specifically designed zero-inflated beta regression models for analyzing zero-inflated count microbiome data. First, it briefly introduces the zero-inflated beta modeling microbiome data. Then, it introduces the zero-inflated beta regression (ZIBSeq). Next, it introduces the zero-inflated beta-binomial model (ZIBB).作者: elucidate 時間: 2025-3-24 07:59
Introduction to QIIME 2,This chapter describes the foundations of the QIIME 2 approach for bioinformatic and biostatistical analyses of microbiome data. It first provides an overview of QIIME 2, and then introduces the core concepts in QIIME 2 and the installation of QIIME 2. Next it introduces how to store, track, and extract data in QIIME 2.作者: arterioles 時間: 2025-3-24 11:01 作者: 失敗主義者 時間: 2025-3-24 17:45
Zero-Inflated Beta Models for Microbiome Data,This chapter introduces two specifically designed zero-inflated beta regression models for analyzing zero-inflated count microbiome data. First, it briefly introduces the zero-inflated beta modeling microbiome data. Then, it introduces the zero-inflated beta regression (ZIBSeq). Next, it introduces the zero-inflated beta-binomial model (ZIBB).作者: DEBT 時間: 2025-3-24 21:40 作者: 脆弱吧 時間: 2025-3-25 01:22 作者: Palpable 時間: 2025-3-25 03:42 作者: 北京人起源 時間: 2025-3-25 08:12 作者: 云狀 時間: 2025-3-25 15:28 作者: Feckless 時間: 2025-3-25 15:57
https://doi.org/10.1007/978-3-8350-9661-5clustering-based OTU methods and the purposes of using OTUs and definitions of species and species-level analysis in microbiome studies. Then, it introduces the OTU-based methods that move toward single-nucleotide resolution. Third, it describes moving beyond the OTU methods. Finally, it discusses t作者: 容易做 時間: 2025-3-25 23:42 作者: FUME 時間: 2025-3-26 00:55
Isabelle Kürschner,Astrid Nelkeiversity measures the difference between two samples or communities. Beta diversity analysis requires a distance or dissimilarity measure matrix as input. This chapter first introduces abundance-based and phylogenetic beta diversity metrics, respectively; then introduces ordination methods and ordin作者: 預(yù)測 時間: 2025-3-26 05:11
https://doi.org/10.1007/978-3-531-94352-7cribes the general nonparametric methods for multivariate analysis of variance in ecological and microbiome data. Then, it mainly introduces two statistical hypothesis tests of beta diversity: analysis of similarity (ANOSIM) and permutational MANOVA (PERMANOVA), respectively. Next, it introduces ana作者: 結(jié)束 時間: 2025-3-26 08:32 作者: 積云 時間: 2025-3-26 14:19
Karriereziel Hochschulprofessurtreated as compositional, Aitchison simplex, challenges of analysis of compositional data, fundamental principles of CoDA, and the family of log-ratio transformations. Then, it introduces three methods/models of CoDA: ANOVA-like compositional differential abundance analysis (ALDEx2), analysis of com作者: Trabeculoplasty 時間: 2025-3-26 20:52
https://doi.org/10.1007/978-3-658-26093-4 Univariate analysis analyzes the change of one taxon or alpha diversity over time. Multivariate analysis directly analyzes the change of multiple taxa simultaneously or distance/dissimilarity (beta diversities) over time. This chapter introduces using the classical univariate linear mixed-effects m作者: 陳腐思想 時間: 2025-3-26 22:07 作者: Exonerate 時間: 2025-3-27 01:43
https://doi.org/10.1007/978-3-319-12850-4 take account for correlated observations with random effects while considering over-dispersion and zero-inflation. First, it reviews and discusses some general issues of GLMMs in microbiome research. Then, it introduces three GLMMs that model over-dispersed and zero-inflated longitudinal microbiome作者: 咆哮 時間: 2025-3-27 07:39 作者: 錫箔紙 時間: 2025-3-27 11:53
Introduction to R for Microbiome Data,r microbiome data analysis (e.g., phyloseq and microbiome). Next it briefly describes three R packages for analysis of phylogenetics (ape, phytools, and castor). Following that it introduces the BIOM format and the biomformat package and illustrates creating a microbiome dataset for longitudinal data analysis.作者: 圓木可阻礙 時間: 2025-3-27 16:02 作者: Grandstand 時間: 2025-3-27 17:54 作者: 縫紉 時間: 2025-3-27 23:59 作者: 急急忙忙 時間: 2025-3-28 04:17 作者: 消毒 時間: 2025-3-28 10:18
Building Feature Table and Feature Representative Sequences from Raw Reads,ng amplicon sequence variants (ASVs) or sub-OTUs. QIIME 2 has warped the two most widely used denoising packages DADA2 and Deblur to generate ASVs and sub-OTUs with 100% identities to clinical variation. This chapter describes and illustrates their uses to generate ASVs or sub-OTUs. First, it introd作者: commodity 時間: 2025-3-28 13:18
Assigning Taxonomy, Building Phylogenetic Tree,ysis of microbiome data. Chapter . described and illustrated how to generate feature table and feature data (i.e., representative sequences). This chapter describes and illustrates two more core bioinformatic analyses: assigning taxonomy and building phylogenetic tree.作者: cocoon 時間: 2025-3-28 18:34
Clustering Sequences into OTUs,minary procedures of clustering sequences into OTUs. Then it describes VSEARCH and q2-vsearch. Next three sections introduce and illustrate three approaches of clustering sequences into OTUs using q2-vsearch: closed-reference clustering, .?clustering, and open-reference clustering.作者: Nuance 時間: 2025-3-28 21:16
OTU Methods in Numerical Taxonomy,ntroduces the principles and aims of numerical taxonomy. Third, it briefly describes the philosophy of numerical taxonomy. Fourth, it introduces the formation and characteristics of commonly clustering-based OTU methods, which have large impact on microbiome study in both concept and methodology. Fi作者: heterogeneous 時間: 2025-3-29 01:37
Moving Beyond OTU Methods,clustering-based OTU methods and the purposes of using OTUs and definitions of species and species-level analysis in microbiome studies. Then, it introduces the OTU-based methods that move toward single-nucleotide resolution. Third, it describes moving beyond the OTU methods. Finally, it discusses t作者: compose 時間: 2025-3-29 05:43
Alpha Diversity,y indicate that an environment of human body has undergone a change (e.g., from a healthy to a disease condition), or the environment is disrupted by some factors (e.g., antibiotic use or a change of immune response). Thus, one task of microbiome community analysis is to analyze diversity of the gut作者: 子女 時間: 2025-3-29 09:30
Beta Diversity Metrics and Ordination,iversity measures the difference between two samples or communities. Beta diversity analysis requires a distance or dissimilarity measure matrix as input. This chapter first introduces abundance-based and phylogenetic beta diversity metrics, respectively; then introduces ordination methods and ordin作者: Deject 時間: 2025-3-29 13:22 作者: 指派 時間: 2025-3-29 17:34 作者: ELUDE 時間: 2025-3-29 22:21 作者: Indolent 時間: 2025-3-30 03:39
Linear Mixed-Effects Models for Longitudinal Microbiome Data, Univariate analysis analyzes the change of one taxon or alpha diversity over time. Multivariate analysis directly analyzes the change of multiple taxa simultaneously or distance/dissimilarity (beta diversities) over time. This chapter introduces using the classical univariate linear mixed-effects m作者: 有抱負者 時間: 2025-3-30 06:07
Introduction to Generalized Linear Mixed Models, an extension of linear mixed models to allow response variables from different distributions, such as binary responses. First, it reviews the brief history of generalized linear models (GLMs) and generalized nonlinear models (GNLMs). Then it describes the generalized linear mixed models (GLMMs). Ne作者: analogous 時間: 2025-3-30 09:22
Generalized Linear Mixed Models for Longitudinal Microbiome Data, take account for correlated observations with random effects while considering over-dispersion and zero-inflation. First, it reviews and discusses some general issues of GLMMs in microbiome research. Then, it introduces three GLMMs that model over-dispersed and zero-inflated longitudinal microbiome作者: 百科全書 時間: 2025-3-30 12:34 作者: DAMN 時間: 2025-3-30 20:20 作者: myalgia 時間: 2025-3-30 22:03
Alpha Diversity,. This chapter focuses on alpha diversity analysis. First, it introduces abundance-based alpha diversity metrics and phylogenetic metrics. Then, it explores alpha diversity and abundance by some common plots. Next, it describes statistical hypothesis testing of alpha diversity using R and QIIME 2.作者: 阻止 時間: 2025-3-31 01:53 作者: 兵團 時間: 2025-3-31 08:32 作者: GRIN 時間: 2025-3-31 12:33 作者: Override 時間: 2025-3-31 15:53
Isabelle Kürschner,Astrid Nelkeput. This chapter first introduces abundance-based and phylogenetic beta diversity metrics, respectively; then introduces ordination methods and ordination plots. Next, it illustrates the beta diversity metrics and ordination in QIIME 2. Finally, it conducts some general remarks on ordination and clustering.作者: Left-Atrium 時間: 2025-3-31 17:55
Karriereziel Hochschulprofessur transformations. Then, it introduces three methods/models of CoDA: ANOVA-like compositional differential abundance analysis (ALDEx2), analysis of composition of microbiomes (ANCOM), analysis of composition of microbiomes-bias correction (ANCOM-BC). Next, it provides some remarks on CoDA approach.作者: bizarre 時間: 2025-4-1 00:52
OTU Methods in Numerical Taxonomy,ormation and characteristics of commonly clustering-based OTU methods, which have large impact on microbiome study in both concept and methodology. Fifth, it introduces statistical hypothesis testing of OTUs. Sixth, it describes the characteristics of clustering-based OTU methods.作者: SKIFF 時間: 2025-4-1 02:47
Beta Diversity Metrics and Ordination,put. This chapter first introduces abundance-based and phylogenetic beta diversity metrics, respectively; then introduces ordination methods and ordination plots. Next, it illustrates the beta diversity metrics and ordination in QIIME 2. Finally, it conducts some general remarks on ordination and clustering.
