ABC-INT-Expression data
Integration Unit: Expression Data
(Integrator unit: select and normalize expression data)
Abstract:
This page integrates material from the learning units and defines a task for selecting and normalizing human expression data.
Deliverables:
- Integrator unit: Deliverables can be submitted for course marks. See below for details.
Prerequisites:
This unit builds on material covered in the following prerequisite units:
Contents
Evaluation
Your progress and outcomes of this "Integrator Unit" will be one of a small number of topics of the first oral exam for BCB420/JTB2020. That oral exam will be worth 20 % of your term grade.[1].
- Work through the tasks described below. Remember to document your work in your journal.
- Part of your task will involve writing an R script, place that code in a subpage of your User page on the Student Wiki and link to it from your Journal.
- Your work must be complete before 21:00 on the day before your exam.
- Schedule an oral exam by editing the signup page on the Student Wiki. You must have signed-up for an exam slot before 20:00 on the day before your exam.
Contents
Your task is to select an expression dataset that is suitable for use as features for human genes in machine learning. Currently, expression data are collected with microarrays and from RNAseq experiments. If we want to use different experiments in a computational experiment, we need to consider very carefully how to prepare comparable values.
To begin, please read the following paper:
We need expression datasets -
- with good coverage;
- not much older than ten years (quality!);
- with sufficient numbers of replicates;
- collected under interesting conditions;
- mapped to unique human gene identifiers.
As the result of this task, you should prepare a script that will produce one reference and one experimental data set for human genes (from the same experiment).
To avoid mistakes in praparing the dataset, discuss with your team members, or post questions on the mailing list. You are encouraged to discuss strategies with anyone however the script you submit must be entirely your own and you must not copy code (apart from the script template) from elsewhere.
Select an Expression Data Set
Task:
Clean it and impute missing data
Task:
Apply Quantile Normalization (QN)
Task:
- Post a script that will download the dataset and perform all required operations.
Interpret
- Be prepared to answer the following questions
Task:
- What is the coverage of your dataset?
Notes
- ↑ Note: oral exams will focus on the content of Integrator Units, but will also cover material that leads up to it. All exams in this course are cumulative.
Further reading, links and resources
If in doubt, ask! If anything about this learning unit is not clear to you, do not proceed blindly but ask for clarification. Post your question on the course mailing list: others are likely to have similar problems. Or send an email to your instructor.
About ...
Author:
- Boris Steipe <boris.steipe@utoronto.ca>
Created:
- 2018-01-25
Modified:
- 2017-11-01
Version:
- 1.0
Version history:
- 1.0 First live version
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