Normalization and microbial differential abundance strategies depend upon data characteristics

BackgroundData from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses.ResultsEffects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size. Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10×) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets (<20 samples per group) but tends towards a higher false discovery rate with more samples, very uneven (~10×) library sizes, and/or compositional effects. For drawing inferences regarding taxon abundance in the ecosystem, analysis of composition of microbiomes (ANCOM) is not only very sensitive (for >20 samples per group) but also critically the only method tested that has a good control of false discovery rate.ConclusionsThese findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study

Aktivitas Antibakteri Ekstrak Dan Fraksi Karang Lunak Lobophytum SP. Terhadap Bakteri Escherichia Coli Dan Staphylococcus Aureus

AKTIVITAS ANTIBAKTERI EKSTRAK DAN FRAKSI KARANG LUNAK Lobophytum sp. TERHADAP BAKTERI Escherichia coli DAN Staphylococcus aureus Queen Novika Hermina Loing1), Defny Silvia Wewengkang1), Jemmy Abidjulu 1) 1)Program Studi Farmasi FMIPA UNSRAT Manado ABSTRACT Indonesia is a tropical country which is have so many abundant biodiversity, and one of them is soft coral. This research aims to test the antibacterial activity of soft coral Lobophytum sp. fractions against the Escherichia coli and Staphylococcus aureus. Extraction was done by maceration and fractionation using n-hexane, chloroform, methanol and water as a solvent. The test of antibacterial activity was done by using agar diffusion method. The result shows that extract and fractions have an antibacterial activity. The results conclude that the extract and fractions of Lobophytum sp. effectively inhibit the Escherichia coli and Staphylococcus aureus, in medium and strong category. The Strong inhibitory capacity showed by chloroform fraction. Key words : Lobophytum sp, antibacterial activity, Escherichia coli, Staphylococcus aureus, fractionation, agar diffusion. ABSTRAK Indonesia merupakan negara tropis, yang mempunyai keanekaragaman hayati berlimpah salah satunya ialah karang lunak. Penelitian ini bertujuan untuk menguji aktivitas antibakteri fraksi karang lunak Lobophytum sp. terhadap bakteri Escherichia coli dan Stapylococcus aureus. Ekstraksi menggunakan metode maserasi dengan pelarut etanol dan fraksinasi menggunakan pelarut n-heksan, kloroform, metanol dan air. Pengujian antibakteri mengunakan metode difusi agar. Hasil penelitian menunjukkan ekstrak dan fraksi uji memiliki aktivitas sebagai antibakteri. Berdasarkan hasil penelitian, disimpulkan bahwa ekstrak dan fraksi karang lunak Lobophytum sp efektif dalam menghambat pertumbuhan bakteri Escherichia coli serta bakteri Staphylococus aureus dengan kategori sedang dan kuat. Daya hambat terbesar ditunjukkan oleh fraksi kloroform

Normalization and microbial differential abundance strategies depend upon data characteristics.

BackgroundData from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses.ResultsEffects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size. Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10×) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets (<20 samples per group) but tends towards a higher false discovery rate with more samples, very uneven (~10×) library sizes, and/or compositional effects. For drawing inferences regarding taxon abundance in the ecosystem, analysis of composition of microbiomes (ANCOM) is not only very sensitive (for >20 samples per group) but also critically the only method tested that has a good control of false discovery rate.ConclusionsThese findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study

Additional file 8: Figure S7. of Normalization and microbial differential abundance strategies depend upon data characteristics

Pseudocount addition to avoid zero increases FDR. The same data as Fig. 7. a Uneven library sizes, FDR p < 0.05. b Uneven library sizes, FDR p < 0.01. Pseudo indicates a pseudocount of one was added to the matrix prior to analysis. (PDF 104 kb

Additional file 2: Figure S2. of Normalization and microbial differential abundance strategies depend upon data characteristics

All normalization techniques on key microbiome datasets, Bray Curtis distance. Rows of panels show (from top to bottom) data from 88 soils [62], body sites [63], and moving pictures [64]. 88 soils are colored according to a color gradient from low to high pH. The Costello et al. body sites’ dataset is colored according to body site feces (blue) and oral cavity (purple); the rest of the colors are external auditory canal, hair, nostril, skin, and urine. Moving pictures dataset: left and right palm (red/blue), tongue (green), and feces (orange). It is important to note that all the samples in these datasets are approximately the same depth, and there are very strong driving gradients. (PNG 1357 kb

Excisional treatment comparison for in situ endocervical adenocarcinoma (EXCISE): A phase 2 pilot randomized controlled trial to compare histopathological margin status, specimen size and fragmentation after loop electrosurgical excision procedure and cold knife cone biopsy

© 2020 Elsevier Inc. Objective: Adenocarcinoma in situ (AIS) of the cervix is a precursor to cervical adenocarcinoma. When AIS is detected by cervical screening an excision biopsy is mandatory to exclude invasion. We aimed to compare margins status, specimen size and fragmentation after loop electrosurgical excision procedure (LEEP) and ‘cold knife cone biopsy’ (CKC). Methods: The EXCISE Trial was an investigator-initiated, multicenter, open-label, parallel-group, phase 2, randomized study. Patients were enrolled at seven hospitals in Australia and New Zealand. We randomly assigned women aged ≥ 18 to ≤ 45 years with screen detected AIS to LEEP or CKC. Co-primary endpoints were margin status, specimen size and fragmentation. Analysis was by intention-to-treat. Results: Between August 2, 2017 and September 6, 2019, 40 patients were randomly assigned 2:1 to LEEP or CKC. Margin status was evaluable in 36 cases. The proportion of patients with involved margins did not differ between groups. 25 of 26 LEEP and all 14 CKC biopsies were excised as single specimens (p = 1·00). There were no differences in specimen dimensions. Patients in the CKC group had more post-operative complications (64.3% compared to 15.4% for LEEP p = ·00). There were no differences in grade three complications (p = ·65). Conclusions: LEEP was not associated with a greater likelihood of positive margins, specimen fragmentation or smaller excision compared to CKC when performed according to a standardized protocol. However, the study was not powered to establish non-inferiority of LEEP and a definitive phase 3 trial to compare margin status and rates of treatment failure after LEEP and CKC is warranted