February 9, 2016
Contents
Classwork
Clustering Activity
Clustering
Clustering: grouping genes and samples together and presenting them in a specific order.
Clustering is used as a data reduction analysis. It is representative of data points rather than an entire data set. When clustering, we seek to gain an understanding of patterns in a data set, so that they may be tested statistically. While analyzing patterns, it is important to consider the utility of log transformations, co-regulations, and direct/indirect relationships of genes. Both negative and positive correlations can be interesting and lead to important discoveries.
Hierarchical Clustering: joins the two most similar genes, then the next two most similar genes or cluster of genes until all genes have been joined.
In hierarchical clustering, after two genes or cluster of genes are joined, they cannot be pulled apart regardless of what future discoveries in data reveal. The biggest problem with hierarchical clustering is that it does not consider all data components together. Furthermore, no gene is left behind in hierarchical clustering; correlations begin with a value of 1 and end with a value of -1.
K-means Clustering: specifies how many clusters to form by randomly assigning each gene to one of k different clusters.
In K-means clustering, the average expression of all genes in each cluster is used to create k pseudo genes. Genes can be rearranged by assigning each one to the cluster represented by the pseudo gene to which it is most similar. K-means clustering can be repeated until there is convergence.
Supervised Clustering: finds genes in expression file whose patterns are highly similar to the desired gene or pattern.
Supervised clustering adds the closest gene first. Then, the gene closest to all of the genes already in a cluster is added. This process continues as long as the added gene is within the specified distance of genes already in cluster. The specified distance from one gene to a set of genes can be defined as the maximum, minimum, or average of all distances to individual members of the set (complete, single, and average linkage, respectively).
Cutting the Tree: the process of grouping genes by determining a threshold value in the dendrogram.
In cutting the tree, cut the dendrogram at different points and see what genes or clusters of genes are still clustered together. Genes that are still together are part of a cluster. Different clusters arise depending on where the tree was cut.
Intensity Plots
Intensity plots compare gene expression profiles. Proximity measures include: correlation, Euclidean distance, inner product XY, Hamming distance, L1 distance, and dissimilarities that may or may not be metrics.
We want our intensity plots to compare the genes and expression patterns between fed and non-fed snakes.
In order to measure the similarity or dissimilarity to the cluster, one much determine which linkage method to use.
Linkage Methods:
- Complete linkage: define the cluster by taking the average of the cluster's components and then treat the average like an individual to compare it to other genes.
- Incomplete linkage: average the gene of interest to all of the distances included in a cluster.(?)
- Mediode linkage: use a max/min approach, including a gene to the cluster if it is closest to one or all of the other genes in the cluster.
QT clust
One can also use QT clust instead of a heat map with the following steps:
- each gene builds a supervised cluster
- Gene with "best" list, and genes in its list, becomes next cluster
- Remove these genes from consideration, and repeat
- Stop when all genes are clustered, or largest cluster is smaller than user specified threshold
Questions to Consider:
- How do you compare one thing to a group of things?
- How can we track genes that match with a transcription factor?
Moving Forward:
- Remember, there is no one perfect, correct answer. Therefore, chase things that are of interest to you and cluster; however, practice restraint.
- It will be important to track genes that match with a transcription factor. Although a transcription factor might be small, big changes may still correlate with it.
- Gene ontology terms will help the clustering process.
