Data analysis

Raw data

Alignment

Annotation

Quantification

Data processing

Genomics

Transcriptomics

Proteomics

Identification

Normalization

Imputation

Quality control

Dimensionality

reduction

Differential expression

Clustering

Co-expression

Data normalization

Functional enrichment

Protein-protein interaction

Pathway analysis

Metabolic mechanism

Function detection

Target selection

PRIDE

• PRIDE (PRoteomics IDEntifications) is a public data repository for proteomics data. It is one of the most widely used data repositories for proteomics data and is maintained by the European Bioinformatics Institute (EBI).
• PRIDE promotes transparency and reproducibility in proteomics research, enables the reuse of data for new studies, and facilitates collaboration between researchers.

PRIDE

https://www.ebi.ac.uk/pride/

PRIDE

UniProt

• UniProt is a comprehensive protein sequence database that provides a central resource for the retrieval of protein sequences, functional information, and annotation data. It is maintained by a collaboration between the European Bioinformatics Institute (EBI), the Swiss Institute of Bioinformatics (SIB), and the Protein Information Resource (PIR).
• UniProt also provides access to a range of tools and resources for proteomics data analysis.

Uniprot

https://www.uniprot.org/

Uniprot

Uniprot

Quality control

• Since the coverage of proteomics is not high, quality control or data filtering becomes an important step.
• Remove unreliable proteins
• Only identified by site – a protein was identified based on a single peptide in a specific location, and the identification may be less confident than identifications based on multiple peptides.
• Potential contaminant – a protein can be found in Decoy database.
• Reverse – a protein can only be found in a database with reverse protein sequence.

Quality control

• Filtering proteins with high proportional missing values
• Filtering by total proteins
• Filtering by groups
• Filtering samples with high proportional missing values

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Without filtering

With filtering

Differential expression analysis

• Differential expression analysis means taking the normalized read count data and performing statistical analysis to discover quantitative changes in expression levels between experimental groups.
• It is the most widely used analysis method for comparative transcriptomics.
• It is also often used for searching the biomarkers.

HER2 receptor

HER2 receptor

When are two values different

Replicates increase reliability

• More replicates, more reliable mean estimates

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• Define a fold change threshold ? Is it significant?

Fold change not informative enough

• The 3 proteins have the same fold change

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• Significance of protein B > signif. of protein A > signif. of protein C

T-test

• Consider both variance between groups and variance within groups
• Test-statistic:
• p-value: how likely is it to get a certain value of t by chance
• significant, if p-value <= significance level alpha, alpha = 0.01

P-value

Multiple hypothesis problem

• Multiplicity problem:
• 10,000 hypotheses are tested simultaneously
• Increased chance of false positives

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• Suppose
• none of the 10,000 proteins is differentially expressed
• We expect 10,000 * 0.01 = 100 with p-value < 0.01

Individual p–values are no significant findings

Benjamini-Hochberg correction

• Compute p-values of all tests (m=10)
• Order them in increasing order

Test if:

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21

Significance Level:

Adjusted Significance Level

Permutation-based FDR

MEASURED

PERMUTED

• False discovery rate estimated by counting hits on permuted data

Careful with technical replicates !!!

Volcano plot

FDR cutoff at

Sign. Level 0.05

LogFC

-Log(q)

Using Fold change

Difference

-log p

-log p

Difference

LogFC

-Log(q)

-Log(q)

LogFC

Volcano plot

The normal cutoff is

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Q-value ≤ 0.05 which means

-log(q-value) ≥ ~1.3

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Log₂(FC) ≥ 1 ~ 2 or Log₂(FC) ≤ -1 ~ -2

Example of PCA

Principle component analysis

PCA – Linear model

tSNE – Non-linear model

UMAP – non-linear model

Co-expression analysis

Correlation coefficient

• Pearson correlation coefficient
• Linear model
• Spearmann correlation coefficient
• Non-linear model
• Remove outliers
• Euclidean distance
• Distance based calculation
• Value differences can be considered

Co-expression analysis

Iron / Ferrichrome related sRNA

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• Correlated genes
• Iron transporters like ferrichrome transport permease
• Ferric Uptake Regulator (Fur) related genes like fhu and isd families^1,2
• Anti-correlated genes
• Ferritin, an iron storage protein
• Iron storage related proteins (glpK³, glmS⁴)

¹Troxell B. et al., 2013, Front. Cell. Infect. Microbiol.

²Zapotocznaa M. et al., 2012, J. Bacteriol.

³Rawls K.S. et al., 2011, J. Bacteriol.

⁴Friedman D.B. et al., 2006, PLoS Pathog.

Iron / Ferrichrome related sRNA

Fe

Transportation

Storage

Correlated

Anti-correlated

Correlation coefficient or�Euclidean distance

Correlation coefficient is based on the trend. In this case, the correlation coefficient will be nearly 100%.

Euclidean distance is based on the distance between two lines. In this case, Euclidean distance is still not small.

The key is “the amount is matter or not”.

Replicate correlation

Clustering heatmap

Gene Ontology (GO)

• Gene Ontology (GO) is a standardized and hierarchical system for the annotation and functional classification of genes and gene products. It provides a framework for describing the functions, processes, and cellular locations of genes and their encoded proteins, and is widely used in bioinformatics research to annotate and interpret biological data.
• The vocabulary includes three main categories or ontologies:
• Molecular function – the biochemical activities of gene products, such as catalytic or binding activities.
• Biological process – the broader biological processes that involve gene products, such as metabolic or developmental processes.
• Cellular component – the subcellular structures or locations where gene products are active, such as the nucleus or mitochondria.

Hierarchy tree of GO terms

GO – gene ontology

http://geneontology.org/

GO – gene ontology

https://www.biostars.org/p/450726/

Kyoto Encyclopedia of Genes and Genomes (KEGG)

• KEGG (Kyoto Encyclopedia of Genes and Genomes) is a database that provides a comprehensive resource for understanding biological pathways and functions in a variety of organisms.
• KEGG annotations are based on a combination of manual curation and automated annotation methods.
• The KEGG database also provides a range of tools and resources for pathway analysis, visualization, and annotation, including the KEGG pathway database, KEGG BRITE (a hierarchical classification of biological objects), and KEGG Mapper (a tool for mapping genes to pathways and visualizing omics data in the context of KEGG pathways).

KEGG categories

KEGG

https://www.genome.jp/kegg/

KEGG

KEGG

Enrichment analysis

• Fisher’s Exact Test

• Gene Ontology
• Biological process (GOBP)
• Molecular function (GOMF)
• Cellular component (GOCC)

Contingency table:

In pathway?

Significant ?

Is a particular pathway overrepresented in the set

of differentially expressed proteins?

Difference

-log p

Fatty acid metabolism

Significant

Non Significant

• Pathways
• KEGG

Enrichment analysis

• Fisher’s Exact Test

Contingency table:

In pathway?

Significant ?

Enrichment analysis on categorical data

LogFC

-Log(q)

Enrichment analysis