文档介绍：Visualizing the Yeast’s gene expression time series data.
Group Members:
N. Alkharouf, I. Chouikha, S. Kime.
Outline:
Specific aims and objectives
Background on Yeast.
Background on clustering techniques used and issues related to the data.
Gene expression series.
Hierarchical clustering and data imaging.
Linking genes to SGD.
Relating genes to chromosomes.
Future work.
Conclusion.
Objectives and specific aims:
Goals of project: Clustering of the gene expression data in the yeast haromyces cerevisiae.
Ultimate goal: To get insight on the functions of unknown ORF’s (genes) and to reveal the underlying work that govern the mitotic cell cycle and other cellular activities.
Background
What are yeast?
Enlarged view of haromyces cerevisiae.
Background (continued)
Why Yeast?
Compact genome (6274 ORF’s).
Large number of chromosomes (16).
Fully sequenced as of 4/23/1996.
Easily maintained and manipulated in lab.
Model for gene expression and regulation.
Background (continued)
Clustering of gene expression time series data - what has been done?
Carr, Michaels and Symogyi clustering of the rat’s spinal cord gene expression data (1997).
Clustering of the 9 time point expression data of yeast genes going through the Diauxic shift (Carr).
Clustering of the 18 time point expression data of yeast genes going through the mitotic cell cycle (Spellman et al., 1998).
Background on Hierarchical clustering and data imaging
Hierarchical Clustering.
Data imaging.
Clustering of gene expression data.
Data Used:
Data from the experiments by Cho et al. (1997) on genome wide transcriptional analysis in the yeast haromyces cerevisiae.
Data downloaded from http://genomelcycle/data/
Issues concerning the data:
Data size plexity.
Missing values.
Scaling method(s).
Visualization.
Hierarchial clustering and data imaging on raw data with slopes: