Biosystems Data Analysis
Table of Content
Week 1 ............................................................................................................................................................. 3
A ................................................................................................................................................................... 3
Lecture 1 Data pre-treatment – Initial analysis and preparation of ‘omics’ data ............................. 3
Statistical Errors - paper ......................................................................................................................... 11
R Tutorial and practical ........................................................................................................................... 13
B ................................................................................................................................................................. 17
Lecture 2 Principal Component Analysis......................................................................................... 17
Principal Component Analysis – Bro et al. .............................................................................................. 21
R practical ............................................................................................................................................... 22
C ................................................................................................................................................................. 26
Lecture 3 Clustering methods and Self Organising Maps (SOM)..................................................... 26
Self Organising Maps – Brereton ............................................................................................................ 31
R practical ............................................................................................................................................... 32
Week 2 ........................................................................................................................................................... 33
D ................................................................................................................................................................. 33
Lecture 4 BDA classification methods - supervised approach ....................................................... 33
R practical ............................................................................................................................................... 38
Week 3 ........................................................................................................................................................... 42
E ................................................................................................................................................................. 42
Lecture 5 ANOVA-Simultaneous Component Analysis – ASCA ....................................................... 42
ASCA – Smilde et.al................................................................................................................................. 45
R practical ............................................................................................................................................... 46
F.................................................................................................................................................................. 49
Lecture 6 Statistical Validation and Biomarker Selection ............................................................... 49
Smit ACA 2007 – paper ........................................................................................................................... 55
PLSDA cross validation – Johan et.al....................................................................................................... 55
R practical ............................................................................................................................................... 56
G ................................................................................................................................................................. 60
Lecture 7 Metabolic Network Inference ......................................................................................... 60
R practical ............................................................................................................................................... 64
Week 3 ........................................................................................................................................................... 66
H ................................................................................................................................................................. 66
Lecture 8 Microbiome data analysis ............................................................................................... 66
1
,Normalizing Microbiome Data – McKnight et.al..................................................................................... 72
R practical ............................................................................................................................................... 73
2
,Week 1
A
Pre-processing and pre-treatment of data is an important aspect of data analysis to remove instrumental
artefacts and add biological content to the data. One of the problems in Next Generation Sequencing
methods is the nonconstant variability in the data. Besides the variance stabilization approach we will also
discuss the meaning of the p-value and the false discovery rate. Read the Nuzzo paper for preparation and
make the questions in the Discussion_Nuzzo2014 pdf.
Web-lecture link: https://webcolleges.uva.nl/Mediasite/Play/0c3570ffed5b4f1ca3701fbdc2d591191d
Lecture 1 Data pre-treatment – Initial analysis and preparation of ‘omics’ data
Goals of the lecture:
- Learn the role of the chain of experimental techniques that determine data quality (e.g.: RNAseq)
- Learn techniques to explore the variation of omics data (bias and random effects)
- Learn techniques to normalize data (remove bias)
- Learn data transformations to remove heteroscedasticity (unequal random error)
- Know the consequences of random error for subsequent statistical analysis
- Learn the ideas behind Multiple Hypothesis Testing
The techniques mentioned above are part of the computer practicals: i.e. the topics treated in the practical
are subject of the exam.
Multiplex: quantification of a large number of (related) components in a single sample (such as omics).
VS
High throughput technologies: quantification of single component in a large number of samples (in a short
time, so not omics) .
Omics experiment is really low throughput, because lots of data takes lots of processing.
Multiplex technologies in biology:
Genomics reading multiple gene sequences in a single sample.
Transcriptomics: quantification of multiple transcript levels (mRNA) in a sample.
Proteomics: quantification & characterization of multiple proteins in a sample.
Metabolomics: quantification of many metabolites in a sample.
RNA-seq: do transcriptomics but in a way in which you sequence each transcript.
RNA-sequencing experimental procedure:
- Stopping all activity = quenching (because concentrations deviate very quick, otherwise noise)
- Isolation of mRNA (isolate out of the cells)
- Reverse transcription: RNA → DNA (because we cannot sequence RNA, thus use DNA)
- Optional amplification by PCR (polymerase chain reaction, create many DNA sequences)
- Library construction : attaching sequence tags/adaptors (to later trace the sequence)
- Sequencing
The experimental procedures affect the outcome:
Quenching because
- RNA’s have short half-lives in living cells.
- RNAses are abundant and have to be stopped
- Handling living cells cause stress which can change gene expression.
- Breaking cells (or bacteria) can be difficult
- Obtaining sample can be time-consuming.
3
, RNA isolation:
- Most RNA is ribosomal RNA (rRNA)
- Eukaryotic messenger RNA (mRNA) can be enriched by poly-A tail hybridization
Sample storage & quality control →
- Storage of mRNA should be done at – 80 ˚C.
- Quality control: 18S/28S rRNA ratio is
measured:
You see how quick mRNA is degraded in the image:
The long mRNA’s become shorter == degraded.
Sequencing and mapping sequences procedure >>
Results: a table of counts. Counts are number of
sequences mapped to a gene.
5 samples taken (A1 – B2) and x genes measured ^
You see large variation when adding all: total is not
equal for all A or all B conditions. This bias should be
removed.
The goal: detect differences in gene expression
between conditions.
Sources of variation
Technical sources (most can be removed) Biological sources
- Sample preparation (medium, temp) - Variation of interest
- Sample isolation (handling, speed of quenching…) - Variation between similar samples /
- Differences in mRNA quality individuals (can be noise but also interesting)
- cDNA synthesis
- Amount of cDNA added
- Sequence bias
- Random measurement error (only error that can’t be removed)
4
Table of Content
Week 1 ............................................................................................................................................................. 3
A ................................................................................................................................................................... 3
Lecture 1 Data pre-treatment – Initial analysis and preparation of ‘omics’ data ............................. 3
Statistical Errors - paper ......................................................................................................................... 11
R Tutorial and practical ........................................................................................................................... 13
B ................................................................................................................................................................. 17
Lecture 2 Principal Component Analysis......................................................................................... 17
Principal Component Analysis – Bro et al. .............................................................................................. 21
R practical ............................................................................................................................................... 22
C ................................................................................................................................................................. 26
Lecture 3 Clustering methods and Self Organising Maps (SOM)..................................................... 26
Self Organising Maps – Brereton ............................................................................................................ 31
R practical ............................................................................................................................................... 32
Week 2 ........................................................................................................................................................... 33
D ................................................................................................................................................................. 33
Lecture 4 BDA classification methods - supervised approach ....................................................... 33
R practical ............................................................................................................................................... 38
Week 3 ........................................................................................................................................................... 42
E ................................................................................................................................................................. 42
Lecture 5 ANOVA-Simultaneous Component Analysis – ASCA ....................................................... 42
ASCA – Smilde et.al................................................................................................................................. 45
R practical ............................................................................................................................................... 46
F.................................................................................................................................................................. 49
Lecture 6 Statistical Validation and Biomarker Selection ............................................................... 49
Smit ACA 2007 – paper ........................................................................................................................... 55
PLSDA cross validation – Johan et.al....................................................................................................... 55
R practical ............................................................................................................................................... 56
G ................................................................................................................................................................. 60
Lecture 7 Metabolic Network Inference ......................................................................................... 60
R practical ............................................................................................................................................... 64
Week 3 ........................................................................................................................................................... 66
H ................................................................................................................................................................. 66
Lecture 8 Microbiome data analysis ............................................................................................... 66
1
,Normalizing Microbiome Data – McKnight et.al..................................................................................... 72
R practical ............................................................................................................................................... 73
2
,Week 1
A
Pre-processing and pre-treatment of data is an important aspect of data analysis to remove instrumental
artefacts and add biological content to the data. One of the problems in Next Generation Sequencing
methods is the nonconstant variability in the data. Besides the variance stabilization approach we will also
discuss the meaning of the p-value and the false discovery rate. Read the Nuzzo paper for preparation and
make the questions in the Discussion_Nuzzo2014 pdf.
Web-lecture link: https://webcolleges.uva.nl/Mediasite/Play/0c3570ffed5b4f1ca3701fbdc2d591191d
Lecture 1 Data pre-treatment – Initial analysis and preparation of ‘omics’ data
Goals of the lecture:
- Learn the role of the chain of experimental techniques that determine data quality (e.g.: RNAseq)
- Learn techniques to explore the variation of omics data (bias and random effects)
- Learn techniques to normalize data (remove bias)
- Learn data transformations to remove heteroscedasticity (unequal random error)
- Know the consequences of random error for subsequent statistical analysis
- Learn the ideas behind Multiple Hypothesis Testing
The techniques mentioned above are part of the computer practicals: i.e. the topics treated in the practical
are subject of the exam.
Multiplex: quantification of a large number of (related) components in a single sample (such as omics).
VS
High throughput technologies: quantification of single component in a large number of samples (in a short
time, so not omics) .
Omics experiment is really low throughput, because lots of data takes lots of processing.
Multiplex technologies in biology:
Genomics reading multiple gene sequences in a single sample.
Transcriptomics: quantification of multiple transcript levels (mRNA) in a sample.
Proteomics: quantification & characterization of multiple proteins in a sample.
Metabolomics: quantification of many metabolites in a sample.
RNA-seq: do transcriptomics but in a way in which you sequence each transcript.
RNA-sequencing experimental procedure:
- Stopping all activity = quenching (because concentrations deviate very quick, otherwise noise)
- Isolation of mRNA (isolate out of the cells)
- Reverse transcription: RNA → DNA (because we cannot sequence RNA, thus use DNA)
- Optional amplification by PCR (polymerase chain reaction, create many DNA sequences)
- Library construction : attaching sequence tags/adaptors (to later trace the sequence)
- Sequencing
The experimental procedures affect the outcome:
Quenching because
- RNA’s have short half-lives in living cells.
- RNAses are abundant and have to be stopped
- Handling living cells cause stress which can change gene expression.
- Breaking cells (or bacteria) can be difficult
- Obtaining sample can be time-consuming.
3
, RNA isolation:
- Most RNA is ribosomal RNA (rRNA)
- Eukaryotic messenger RNA (mRNA) can be enriched by poly-A tail hybridization
Sample storage & quality control →
- Storage of mRNA should be done at – 80 ˚C.
- Quality control: 18S/28S rRNA ratio is
measured:
You see how quick mRNA is degraded in the image:
The long mRNA’s become shorter == degraded.
Sequencing and mapping sequences procedure >>
Results: a table of counts. Counts are number of
sequences mapped to a gene.
5 samples taken (A1 – B2) and x genes measured ^
You see large variation when adding all: total is not
equal for all A or all B conditions. This bias should be
removed.
The goal: detect differences in gene expression
between conditions.
Sources of variation
Technical sources (most can be removed) Biological sources
- Sample preparation (medium, temp) - Variation of interest
- Sample isolation (handling, speed of quenching…) - Variation between similar samples /
- Differences in mRNA quality individuals (can be noise but also interesting)
- cDNA synthesis
- Amount of cDNA added
- Sequence bias
- Random measurement error (only error that can’t be removed)
4
