Analysis of transcriptomic data

The course “Transcriptomic Data Analysis” is dedicated to the analysis of gene expression data obtained using high‑throughput sequencing platforms.

During the course, both the analysis of bulk RNA‑Seq data and the increasingly popular scRNA‑Seq data (in recent years) will be covered. Special attention will be paid to machine learning methods (from GLM to VAE and dimensionality reduction techniques), which are now considered the “gold standard” at all stages of working with transcriptomic data.

The course consists of 15 lectures and 15 seminars. The lectures will focus on the theoretical foundations of the analysis methods used, as well as discussions on the applicability of various approaches. The seminars will cover specific examples of using different tools and address some in‑depth topics from the course. After each seminar, a homework assignment will be given to reinforce the material covered in the session.

A typical course participant is a student in a natural sciences field who wants to master modern methods for analyzing expression data and apply them effectively in their research work. To fully complete the course, proficiency in Python and R is required, along with a basic understanding of statistics, probability theory, and linear algebra.

Course Syllabus

The course includes 30 sessions: 15 lectures and 15 seminars

Requirements for students:

• Knowledge of Python and the numpy, pandas, and matplotlib libraries (this will be the main tool for our work);
• Basic knowledge of R and the ggplot2 library (to be able to run some tools that are available only in R);
• Basic understanding of mathematical statistics and regression analysis (completing at least one applied statistics course will suffice);
• Basic understanding of molecular biology.

Course Telegram

Занятия проводятся на базе Факультете Биоинженерии и биоинформатики

В программе курса 30 занятий: 15 лекций и 15 семинаров

Старт курса: с 9 сентября

Занятия будут проходить по пятницам:

• лекция 15:35 — 17:10
• семинар 17:20 — 18:55

Формат занятий: онлайн (дистанционно)

Требования к студентам:

• Знание Python и библиотек numpy, pandas и matplotlib (это будет основной инструмент нашей работы);
• Базовое знание R и библиотеки ggplot2 (чтобы уметь запустить некоторые инструменты, которые есть только на R);
• Базовое представление о математической статистике и регрессионному анализу (достаточно будет пройти хотя бы один курс по прикладной статистике);
• Базовое представление о молекулярной биологии.

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Набор на курс 2022 года закрыт