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Beginner Level
Padroneggiare la genomica: svelare informazioni attraverso l'analisi dei microarray
287Learners already enrolled
5-6 Avg Hours
CPD Accredited
Padroneggiare la genomica: svelare informazioni attraverso l'analisi dei microarray
Impara a eseguire varie analisi bioinformatiche sui dati di espressione genica generati utilizzando la tecnologia dei microarray.
In questo corso imparerai una gamma completa di diverse analisi bioinformatiche sui dati di espressione genica generati utilizzando la tecnologia dei microarray. In questo viaggio passo dopo passo, imparerai ... In questo corso imparerai una gamma completa di diverse analisi bioinformatiche sui dati di espressione genica generati utilizzando la tecnologia dei microarray. In questo viaggio passo dopo passo, imparerai innanzitutto a scaricare i dati di microarray. Quindi, imparerai a controllare la qualità complessiva dei dati del microarray, visualizzarli e trovare i geni espressi in modo differenziale (GEG). Inoltre, imparerai l'analisi del clustering e dell'ontologia genica (GO).
COURSE PUBLISHER Mohamad ZamaniDr. Mohammad Zamani bioinformatician
Total XP: What You Will Learn In This Free Course
- Illustrate installing the CRAN or B...
- Assess the gene expression database...
- Identify and extract differentially...
- Apply Gene Ontology (GO) analysis o...
- Illustrate installing the CRAN or Bioconductor packages directly or indirectly in the R environment
- Assess the gene expression database on various features (options) and download the expression data
- Identify and extract differentially expressed genes (DEGs) after importing the expression matrix into geWorkbench
- Apply Gene Ontology (GO) analysis on DEGs using the corresponding database (DAVID)
- Explain the details of different microarray methods, including DNA microarray
- Evaluate the general format of the gene expression matrix and read the various information presented in the matrix
- Compute cluster analysis on DEGs using the Hierarchical clustering method in the geWorkbench
- Compute cluster analysis using the K-Means algorithm, cluster, and factoextra packages in the R environment
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