Feb 9 - Revision history

Duatchley at 19:54, 9 February 2016

2016-02-09T19:54:35Z

Duatchley at 19:35, 9 February 2016

2016-02-09T19:35:49Z

Duatchley at 19:35, 9 February 2016

2016-02-09T19:35:22Z

Duatchley at 19:25, 9 February 2016

2016-02-09T19:25:53Z

Duatchley at 19:19, 9 February 2016

2016-02-09T19:19:56Z

Duatchley at 19:11, 9 February 2016

2016-02-09T19:11:31Z

Duatchley at 19:11, 9 February 2016

2016-02-09T19:11:19Z

Duatchley: Created page with "Clustering: Grouping in a particular way based on some sort of algorithm with given parameters Why cluster? Exploration of huge data, extract patterns and make predictions on..."

2016-02-09T19:10:57Z

Created page with "Clustering: Grouping in a particular way based on some sort of algorithm with given parameters Why cluster? Exploration of huge data, extract patterns and make predictions on..."

New page

Clustering: Grouping in a particular way based on some sort of algorithm with given parameters

Why cluster? Exploration of huge data, extract patterns and make predictions on these patterns (hypothesis generation and testing)

Gene expression data:

Induction looks much more dramatic than the repression (be sure and remember this), equivalent on the fold change, but look very dissimilar

A log transformation "normalizing" the way this data looks for fold changes

Negative correlations are as informative as the positive correlations

Scatter/line plots are a different way to represent a heat map

Comparing Gene Expression Profiles or Guilt by expression:

Co-regulation or directly regulating each other

Proximity Measures:

Want to understand relationships genes and expression level over time or samples

Correlation, Euclidean distance (distance formula), Inner product x y, Hamming distance, L1 distance, Dissimilarities may or may not be metrics

← Older revision		Revision as of 19:54, 9 February 2016
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	Will likely stop at supervised clustering (around gene ontology perhaps) in this class, restraint is good, don't just go to a heat map		Will likely stop at supervised clustering (around gene ontology perhaps) in this class, restraint is good, don't just go to a heat map
		+
		+
		+	Completed clustering activities

← Older revision		Revision as of 19:35, 9 February 2016
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	4. Stop when all genes are clustered, or largest cluster is smaller than user specified threshold		4. Stop when all genes are clustered, or largest cluster is smaller than user specified threshold

−	Will likely stop at supervised clustering in this class, restraint is good, don't just go to a heat map	+	Will likely stop at supervised clustering (around gene ontology perhaps) in this class, restraint is good, don't just go to a heat map

@@ Line 47: / Line 47: @@
 Cutting the tree: group the things that are still joined at a certain point
-'''K-means clustering'''
+'''K-means Clustering'''
 Specify how many clusters to form
@@ Line 62: / Line 62: @@
 Really hard to pick clusters

@@ Line 35: / Line 35: @@
 Single linkage, Average Linkage, etc. Each will produce different clusters
-Hierarchical Clustering
+'''Hierarchical Clustering'''
 Join two most similar genes
@@ Line 41: / Line 41: @@
 Join next two most similar "objects", repeat until all genes have been joined (can never be pulled apart in your cluster once they are joined)
 Iterative and stringent

@@ Line 17: / Line 17: @@
 Co-regulation or directly regulating each other
-'''''Proximity Measures:'''''
+'''Proximity Measures:'''
 Want to understand relationships genes and expression level over time or samples
 Correlation, Euclidean distance (distance formula), Inner product x y, Hamming distance, L1 distance, Dissimilarities may or may not be metrics

@@ Line 3: / Line 3: @@
 Why cluster? Exploration of huge data, extract patterns and make predictions on these patterns (hypothesis generation and testing)
-Gene expression data:
+'''Gene expression data:'''
 Induction looks much more dramatic than the repression (be sure and remember this), equivalent on the fold change, but look very dissimilar
@@ Line 13: / Line 13: @@
 Scatter/line plots are a different way to represent a heat map
-Comparing Gene Expression Profiles or Guilt by expression:
+'''Comparing Gene Expression Profiles or Guilt by expression:'''
 Co-regulation or directly regulating each other
-Proximity Measures:
+''Proximity Measures:''
 Want to understand relationships genes and expression level over time or samples
 Correlation, Euclidean distance (distance formula), Inner product x y, Hamming distance, L1 distance, Dissimilarities may or may not be metrics