PSII-6 Computational Tools and Resources for Analysis and Exploration of Single-Cell Rnaseq Data in Agriculture
Notice bibliographique
Résumé
Abstract The agriculture genomics community has numerous data submission standards available but little experience describing and storing single-cell (e.g., scRNAseq) data. Other single-cell genomics infrastructure efforts, such as the Human Cell Atlas Data Coordination Platform (HCA DCP), have resources that could benefit our community. For example, the HCA DCP is integrated with Terra, a cloud-native workbench for computational biology developed by Broad, Verily, and Microsoft that houses tools for scGenomics analysis. We will describe a pilot-scale project to determine if our current metadata standards for livestock and crops can be used to ingest scRNAseq datasets in a manner consistent with HCA DCP standards and if established resources (e.g., Terra) can be used to analyze the ingested data. Currently, the most comprehensive data ingestion portal for high throughput sequencing datasets from plants, fungi, protists, and animals/humans is Annotare (located at EMBL-European Bioinformatics Institute), ensures that sufficient metadata are collected to enable re-analysis and dissemination via the Single Cell Expression Atlas (SCEA). Annotare supports user-directed annotation and processing of their data, as well as search tools via the SCEA and transferred to the Galaxy analysis space. For animal datasets, another EMBL-EBI portal, the FAANG portal, has been developed that provides bulk and scRNAseq data access. scRNAseq data/metadata can be submitted to FAANG using a semi-automated process. We have extended this tool for scRNAseq data so that files can be validated using the HCA DCP metadata and data validation service. These files are also incorporated using EMBL-EBI’s HCA DCP ingestion service and transferred to Terra for further analysis. Once incorporated, datasets will augment the DCP resource for the scientific community. In an extension of these efforts, we have also created a Shiny-based web application, called Shiny-PIGGI, for the single cell-level transcriptomic study of pig immune tissues and peripheral blood mononuclear cells, which will be an important resource for improved annotation of porcine immune genes and cell types. The Shiny-PIGGI (https://shinypiggi.ansci.iastate.edu) is implemented completely in R, runs on any modern web browser, and requires no programming. This tool thus increases accessibility through eliminating technical training requirements for using Seurat object and related R packages commonly used in scRNAseq analysis. Our main goal was to develop an interactive web application that allows users such as animal scientists and immunologists to visualize and analyze biological datasets. This tool will be demonstrated at the meeting. We intend to further build upon these existing tools to construct a scientist-friendly data resource and analytical ecosystem to facilitate single cell-level genomic analysis through data ingestion, storage, retrieval, re-use, visualization, and comparative annotation across agricultural species.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».