Data · dataset · 2026
Molecular characterisation of epithelial subtypes in colorectal cancer
Listed in ZivaHub and Deakin Research Online and DMU Figshare — shown once because both records carry DOI 10.17034/32805284.v1
Gene set enrichment methods are important tools for transcriptomic data analysis, used to identify enriched biological pathways in either different experimental conditions or disease contexts.
Description
There are two broad categories of gene set enrichment methods: (1) pairwise methods for group comparison and (2) single sample approaches for individual samples. Although these methods are widely used, problems can arise when approaches are used and interpreted interchangeably.
This thesis evaluated the outputs of pairwise and single sample methods, demonstrating that statistically significant differences do not always equate to biological relevance (Chapter 3). <br><br>Building on this methodological analysis, this thesis applied the transcriptional subtyping method, Pathway Derived Subtypes (PDS), which was developed by using a single sample approach for gene set enrichment to build a molecular classifier for Colorectal Cancer.
Read the rest (2 more)
In this thesis, the PDS classifier was not only applied to Colorectal Cancer but also other Gastrointestinal cancers (Chapter 4). The application of the PDS classifier to Gastrointestinal cancers offers a framework to unify the classification of these cancers, enabling cross cancer comparisons and the identification of targetable pathways.<br>At the single cell level, the application of the PDS classifier revealed distinct epithelial-specific subpopulations in the Colorectal Cancer epithelial cells which captured subtype specific biologies across individual cells while the heterogeneity within individual tumours was highlighted by the representation of different proportions of PDS classified cells (Chapter 5).<br><br>Overall, this thesis has provided insights into clearer interpretation of the results of gene set enrichment methods.
Additionally, it has provided a comprehensive application of the PDS classifier to Gastrointestinal cancers, providing opportunities to characterise various disease types. Finally, it was demonstrated that the PDS classifier can capture the PDS phenotypes at the single cell level, providing a wider use of the classifier originally developed in bulk Colorectal Cancer tumours.<br><br>Thesis is embargo until 31 July 2028.
Links
Where it is published
- DOI doi.org/10.17034/32805284.v1 ↗
DOI / persistent id · from zivahub uct ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from zivahub uct ac za
Topics
- From keywords
- Bioinformatics and computational biology · Bioinformatics and computational biology · Bioinformatics and computational biology · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Life Sciences · Life Sciences · Life Sciences · Medicine & Health · Medicine & Health · Medicine & Health
Provenance · 3 source records, 19 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ZivaHub | oai:figshare.com:article/32805284 | 11 d ago | JSON v1 |
| Deakin Research Online | oai:figshare.com:article/32805284 | 11 d ago | JSON v1 |
| DMU Figshare | oai:figshare.com:article/32805284 | 11 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[disease].local:disease:cancer | enrichment · zivahub uct ac za | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[disease].local:disease:disease | enrichment · zivahub uct ac za | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[field].anzsrc:group:3102 | mapping · dro deakin edu au | vocabulary-mapper@1.0.0 | keywords['Computational Biology'] |
| concepts[field].anzsrc:group:3102 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['Computational Biology'] |
| concepts[field].anzsrc:group:3102 | mapping · figshare dmu ac uk | vocabulary-mapper@1.0.0 | keywords['Computational Biology'] |
| concepts[field].local:field:earth-environmental | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| description | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/description |
| license_text | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| publication_date | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| title | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/title |