Download Clustering_PartII_2012

baySeq homework
HS analysis:
Out of 7388 genes with data, 1995 genes were DE at FDR <1%,
3158 genes were DE at FDR <5%
There were 3,582 genes with an average fold-change >2X (1.0 in log2 space)
2,669 (63%)
BUT
HS + EtOH analysis (added 2 replicates of a new conditions):
Only 1618 genes were DE (at any of the models) at FDR of 5%
??? Why so few when 3157 met this cutoff when HS was analyzed alone?
baySeq paper: harder to call DE with “more complex” models
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How well did baySeq do on the HS only analysis?
3158 genes FDR <0.05 (10K it on prior calc)
How well did baySeq do on the HS only analysis?
~50% of these: low counts
Many of remaining missed due
to day-to-day variation that
is not accounted for without
pairing the data
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902 genes FDR >5%
but fold-change >1.5X in both replicates
How well did baySeq do on the HS + EtOH analysis?
Models:
NDE = 1,1,1,1,1,1
DEH = 1,1,2,2,1,1
DEE = 1,1,1,1,2,2
DEHE = 1,1,2,2,2,2
DEHE2 = 1,1,2,2,3,3
1618 genes FDR <0.05
to at least one DE model
How well did baySeq do on the HS only analysis?
But, 1391 genes with FDR > 0.05
to all DE models but at least 1.5X
expression change in all 4 samples
Why weren’t these identified as DE?
218 of these genes were DE when HS
was analyzed ALONE.
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Assessing sensitivity (with VLOOKUP in Excel)
There were 64 known Hsf1 targets *with data* on the file.
My run identified 38 of those at an FDR of 0.01
38/64  59.4% sensitivity
45 were identified at FDR of 0.05%
45/64  70% sensitivity
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LAST TIME:
Gene X: X1
x coordinate
X2
X3
z coordinate
y coordinate
LAST TIME:
‘centroid’
(average vector)
4. Centroid linkage clustering
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Sometimes, want to use the weighted pearson correlation
S x,y =
1
N
N
S
i=1
(Xi)
1
N
N
S
(Yi)
2
Xi
i=1
1
N
N
S
2
Yi
i=1
Gene X: X1 X2 X3 X4 X5
Gene Y: Y1 Y2 Y3 Y4 Y5
For example: if these arrays are identical,
the data are over-represented 3X
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Sometimes, want to use the weighted pearson correlation
1
S x,y =
S wi
N
S
i=1
(Xi)
wi
1
N
N
S
(Yi)
2
Xi
i=1
1
N
N
S
2
Yi
Where wi = 1
Li
k = array corr. cutoff
d = Pearson distance (= 1 - P. corr)
n = exponent (usually 1)
Gene X: X1 X2 X3 X4 X5
Gene Y: Y1 Y2 Y3 Y4 Y5
For example: if these arrays are identical,
the data are over-represented 3X
-- can weight experiments i = 3,4,5 by w = 0.33 10
Unweighted Pearson correlation
Weighted Pearson correlation
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Unweighted Pearson correlation
Weighted Pearson correlation
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Can also cluster
array experiments
based on global
similarity in expression
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Alizadeh et al. 2000
Hierarchical trees of gene expression data are analogous to phylogenetic trees
A
D
B
E
Distance between genes is
proportionate to the total branchlength
between genes (not the distance on the y-axis)
F
C
Orientation of the nodes is irrelevant ….
although some clustering programs try to
organize nodes in some way.
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Hierarchical trees of gene expression data are analogous to phylogenetic trees
A
D
B
E
Distance between genes is
proportionate to the total branchlength
between genes (not the distance on the y-axis)
F
C
D
B
Orientation of the nodes is irrelevant ….
although some clustering programs try to
organize nodes in some way.
A
E
F
C
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Genes involved in same cellular process are often coregulated
These genes may not have the same annotation, but still function together and are thus co-expressed
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M choose i = # of possible groups of size i
composed of the objects M
=
M!
(M-i)! * i !
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Advantages and Disadvantages of Hierarchical clustering
Advantages:
1) Straightforward
2) Captures biological information relatively well
Disadvantages:
1) Doesn’t give discrete clusters … need to define clusters with cutoffs
2) Hierarchical arrangement does not always represent data appropriately
-- sometimes a hierarchy is not appropriate: genes can belong
only to one cluster.
3) Get different clustering for different experiment sets
THERE IS NO ONE PERFECT CLUSTERING METHOD
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k-means clustering
Partitioning (or top-down) clustering method
-- Randomly split the data into k groups of equal number of genes
-- Calculate the centroid of each group
-- Reassign genes to the centroid to which it is most similar
-- Calculate a new centroid for each group, reassign genes, etc … iterate until stable
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k-means clustering
Partitioning (or top-down) clustering method
-- Randomly split the data into k groups of equal number of genes
-- Calculate the centroid of each group
-- Reassign genes to the centroid to which it is most similar
-- Calculate a new centroid for each group, reassign genes, etc … iterate until stable
Centroids
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k-means clustering
Partitioning (or top-down) clustering method
-- Randomly split the data into k groups of equal number of genes
-- Calculate the centroid of each group
-- Reassign genes to the centroid to which it is most similar
-- Calculate a new centroid for each group, reassign genes, etc … iterate until stable
What are the disadvantages of k-means clustering?
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k-means clustering
Partitioning (or top-down) clustering method
-- Randomly split the data into k groups of equal number of genes
-- Calculate the centroid of each group
-- Reassign genes to the centroid to which it is most similar
-- Calculate a new centroid for each group, reassign genes, etc … iterate until stable
What are the disadvantages of k-means clustering?
- Need to know how many clusters to ask for
(can define this empirically)
- Genes are not organized within each cluster
(can hierarchically cluster genes afterwards or use SOM analysis)
- Random process makes this an indeterminate method
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