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DATA ANALYSIS
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Data analysis
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Academic year 2023/2024
- Course ID
- BIO0157 Pds 308-BIDD
- Teachers
- Raffaele Adolfo Calogero
Paolo Provero
Luca Alessandrì - Degree course
- [0101M22] Molecular Biotechnology
- Year
- 1st year
- Teaching period
- To be defined
- Type
- Distinctive
- Credits/Recognition
- 6
- Course disciplinary sector (SSD)
- BIO/11 - molecular biology
- Delivery
- Formal authority
- Language
- English
- Attendance
- Obligatory
- Type of examination
- Practice test
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Sommario del corso
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Course objectives
The course aims to introduce the students to the main statistical tools used in the analysis of biological data. One part of the course will be dedicated to univariable and multivariable regression, with examples taken from biology, medicine, and other disciplines. The other part will tackle in depth a specific analytical task, namely the analysis of transcriptomic data generated by RNA sequencing. Both parts will take a hands-on approach in which all topics will be illustrated by examples worked out in complete detail using the R environment for statistical analysis and modern software development tools such as Docker and Git together with specific programs for transcriptome analysis.
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Results of learning outcomes
The students will be able to
- Formulate a scientific problem in terms of a regression problem on observational data
- Use the R environment to perform the regression analysis
- Interpret the results of a regression model
- Perform a complete analysis of transcriptomic data using procedure that ensure reproducibility of the results
- Design and perform the analysis of bulk and single cell RNAseq experiments
- Implementing reproducible analysis pipelines in docker containers
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Program
- Regression
- The concept of regression
- The R computing environment
- Linear regression
- Logistic regression
- Multiple regression
- Machine learning, overfitting, and cross-validation
- Introduction to causality
- Transcriptome analysis (Bulk and Single Cell RNAseq)
- Reproducibility of bioinformatics analyses
- Experimental design (bulk RNAseq single cell RNAseq)
- Fastq QC
- Converting fastq files in count table
- Dimensionality reduction: PCA, tSne and UMAP
- Differential expression analysis
- Clustering
- Functional Class enrichment
- Regression
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Course delivery
Lessons in computer room (Provero)
Lessons with your own laptop (Calogero)
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Learning assessment methods
One practical test for each part of the course
Suggested readings and bibliography
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Slides and other material provided in the Moodle page of the course
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Class schedule
Days Time Classroom Tuesday 9:00 - 11:00 Thursday 14:00 - 16:00 Lessons: from 05/03/2024 to 06/06/2024
Notes: The lessons will be held by Prof. Provero on Monday and by Prof. Calogero on Tuesday.
Prof. Provero lessons start on XXX the Xth in Aula XXXX MBC
Prof. Calogero/Alessandri lessons are scheduled every Tuesday from 09:00 to 11:00 starting from March the 5th (https://unito.webex.com/meet/raffaele.calogero)The moodle link is: https://biotec.i-learn.unito.it/course/view.php?id=1302
- Enroll
- Open
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