CS6101 Exploration of CS Research:
Analysis of Gene Expression based on Single-Cell Sequencing
Instructors: Drs Wong Limsoon & Niranjan Nagarajan /
2014/15 Semester 1
- Time will be 9am-11.30am on Thursdays
9th, 16th, 23rd, 30th Oct & 6th, 13th Nov 2014.
- Venue will be
The possibility of using gene expression profiling for diagnostic
and prognostic purposes has generated much excitement and research
in the last ten years. Microarray technology, which has been the
main gene expression profiling technique, produces the gene
expression profile that is an average over many cells in the sample.
Single-cell sequencing technology has recently become available and
presents an opportunity to obtain gene expression profile of
individual cells. This individual-cell level of granularity
should enable much more informative analysis.
In this journal club, we plan to cover recent literature on
1/ single-cell sequencing and 2/ gene expression analysis approaches.
We hope to develop in the participating students an appreciation
of the opportunity in combining these two technologies for
improving disease gene selection and for transitioning from
the selected genes to the understanding of the sequences of
causative molecular events.
We plan to read the selected papers below (and possibly other
related papers). Each student will be asked to pick and present
two of these (or other relevant papers of his choice). Each student
will be graded by all fellow attendees according to:
And by the lecturers and presenting student according to:
- The quality of ppt (readability, organization, attractiveness).
- The quality of presentation (organization, delivery, how well
he makes biologists understand the material).
- The level of understanding of what he is presenting (in particular, Q&A).
- The level of participation in discussion.
Tentative Reading List (To be refined)
Group I: Basics of single-cell gene expression profiling.
- Martin Bengtsson et al.
Gene expression profiling in single cells from the pancreatic islets
of Langerhans reveals lognormal distribution of mRNA levels,
Genome Research, 15:1388-1392, 2005.
- Adhemar Jaitin et al.
Massively parallel single-cell RNA-seq for marker-free decomposition
of tissues into cell types,
Science, 343:776-779, 2014.
- Anders Stahlberg et al.
RT-qPCR workflow for single-cell data analysis,
Methods, 59:80-88, 2013.
- Barbara Treutlein et al.
Reconstructing lineage hierarchies of the distal lung epithelium
using single-cell RNA-seq,
Nature, 509:371-375, 2014.
Group II: Basic single-cell gene expression analysis
- Florian Buettner et al.
A novel approach for resolving differences in single-cell gene
expression patterns from zygote to blastocyst,
Bioinformatics, 28:i626-i632, 2012.
- Andew McDavid et al.
Data exploration, quality control and testing in single-cell
qPCR-based gene expression experiments,
Bioinformatics, 29:461-467, 2013.
- Peter V. Kharchenko et al.
Bayesian approach to single-cell differential expression analysis,
Nature Methods, 11:740-742, 2014.
Group III: Gene-regulation network reconstruction
- Eran Segal et al.
Module networks: Identifying regulatory modules and their
condition-specific regulators from gene expression data,
Nature Genetics, 34:166-176, 2003.
- Joshua Stuart et al.
A gene coexpression network for global discovery of conserved
Science, 10:249-255, 2003.
- Sucheendra Palaniappan et al.
A hybrid factored frontier algorithm for dynamic Bayesian
network models of biopathways,
IEEE/ACM TCBB, 9:1352-1365, 2012.
- Cole Trapnell et al.
The dynamics and regulators of cell fate decisions are revealed
by pseudotemporal ordering of single cells,
Nature Biotechnology, 32:381-386, 2014.
Group IV: Model-based gene expression analysis
- Leonid Chindelevitch et al.
Causal reasoning on biological networks: Interpreting
Bioinformatics, 28:1114-1121, 2012.
- Ludwig Geistlinger et al.
From sets to graphs: Towards a realistic enrichment analysis
of transcriptomic systems,
Bioinformatics, 27:i366-i373, 2011.
- Mattia Zampieri et al.
A system-level approach for deciphering the transcriptional
response to prion infection,
Bioinformatics, 27:3407-3417, 2011.
Contact: Limsoon Wong / Last updated 29/8/2014.