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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 

  1. Formulate a scientific problem in terms of a regression problem on observational data
  2. Use the R environment to perform the regression analysis
  3. Interpret the results of a regression model
  4. Perform a complete analysis of transcriptomic data using procedure that ensure reproducibility of the results
  5. Design and perform the analysis of bulk and single cell RNAseq experiments
  6. Implementing reproducible analysis pipelines in docker containers
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Program

  1. Regression
    1. The concept of regression
    2. The R computing environment
    3. Linear regression
    4. Logistic regression
    5. Multiple regression
    6. Machine learning, overfitting, and cross-validation
    7. Introduction to causality
  2. Transcriptome analysis (Bulk and Single Cell RNAseq)
    1. Reproducibility of bioinformatics analyses
    2. Experimental design (bulk RNAseq single cell RNAseq)
    3. Fastq QC
    4. Converting fastq files in count table
    5. Dimensionality reduction: PCA, tSne and UMAP
    6. Differential expression analysis
    7. Clustering
    8. Functional Class enrichment
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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 scheduleV

DaysTimeClassroom
Tuesday9:00 - 11:00
Thursday14:00 - 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

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