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Important update (January 20th, 2011): the data below have been corrected for the BCR batch which is not necessarily the processing batch. The dataset needs to be reanalyzed.
Correlation between BCR batch and the processing batch for 27k arrays (January 20, 2012):
Batch vs clinical traits
Clinical traits: 36, number of batches: 13
Batch vs center:
| Code Block | ||
|---|---|---|
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> table(batchID,two)
two
batchID A3 AK AS B0 B2 B4 B8 BP CJ CW CZ DV EU
0859 31 8 2 0 0 0 0 0 0 0 0 0 0
1186 4 6 0 0 5 0 6 5 9 0 0 0 0
1275 0 12 0 29 1 0 1 0 0 0 0 0 0
1284 0 0 0 0 0 0 0 50 0 0 0 0 0
1303 0 0 0 6 0 0 0 11 24 0 6 0 0
1323 18 7 0 0 4 0 3 5 9 0 0 0 0
1332 0 0 0 6 0 0 0 39 2 0 0 0 0
1418 6 0 0 27 0 0 6 8 0 0 0 0 0
1424 0 0 0 0 0 0 0 28 16 0 3 0 0
1500 0 1 0 15 0 2 1 1 0 0 24 0 0
1536 2 0 0 18 5 0 5 0 13 9 0 9 0
1551 0 0 0 0 0 0 3 0 0 0 0 0 0
1670 0 0 0 6 0 7 4 0 7 6 7 0 4{code}
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Significant
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batch/trait
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correlations
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(complete
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table
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can
...
be
...
found
...
...
):
Survival vs Batch
Summary can be found here, batch is significantly correlated with survival:
Likelihood ratio test= 61.35 on 10 df, p=2.007e-09
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Wald
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test
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=
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64.35
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on
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10
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df,
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p=5.39e-10
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Score
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(logrank)
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test
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=
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75.35
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on
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10
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df,
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p=4.066e-12
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DNA
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methylation
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data
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analysis
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27k
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dataset,
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downloaded
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on
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December
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28,
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2011.
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219
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samples. Note: TCGA is terrible about their standards. I am extracting values for methylated and unmethylated probes from the files for each patient. For this dataset it is 1st and 4th columns. However, for GBM it is 1st and 2nd columns! Unreliable. It seems that the data for GBM was processed differently because standard deviation and the number of beads are missing for GBM. However I noticed that they actually provide negative controls intensity for the green and red dyes.
Technical variables available: batch, amount, concentration, day of shipment, month of shipment, year of shipment, plate row, plate column. Combine day, month and year in a single variable. Info about technical variables:
| Code Block | ||
|---|---|---|
| ||
> head(methNew)
batchID amount concentration plate_column plate_row dateCombined
2 0859 26.7 uL 0.14 ug/uL 1 A 17-3-2010
32 0859 26.7 uL 0.17 ug/uL 1 C 17-3-2010
59 0859 26.7 uL 0.15 ug/uL 1 D 17-3-2010
84 0859 26.7 uL 0.15 ug/uL 1 E 17-3-2010
> table(methNew$batchID)
0859 1186 1284 1303 1332
40 35 50 47 47
> table(methNew$amount)
26.7 uL
219
> table(methNew$concentration)
0.13 ug/uL 0.14 ug/uL 0.15 ug/uL 0.16 ug/uL 0.17 ug/uL
7 50 122 30 10
> table(methNew$plate_column)
1 2 3 4 5 6 7
39 40 40 40 35 23 2
> table(methNew$plate_row)
A B C D E F G H
30 28 28 27 27 27 27 25
> table(methNew$plate_column,methNew$plate_row)
A B C D E F G H
1 5 4 5 5 5 5 5 5
2 5 5 5 5 5 5 5 5
3 5 5 5 5 5 5 5 5
4 5 5 5 5 5 5 5 5
5 5 5 5 4 4 4 4 4
6 4 3 3 3 3 3 3 1
7 1 1 0 0 0 0 0 0
> table(methNew$dateCombined)
11-10-2010 17-3-2010 25-8-2010 27-9-2010 6-10-2010
47 40 35 50 47
> table(methNew$dateCombined,methNew$batchID)
0859 1186 1284 1303 1332
11-10-2010 0 0 0 0 47
17-3-2010 40 0 0 0 0
25-8-2010 0 35 0 0 0
27-9-2010 0 0 50 0 0
6-10-2010 0 0 0 47 0{code}
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Exclude
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"amount"
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from
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calculations
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for
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the
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correlations
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of
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the
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first
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principal
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components
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of
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the
...
data
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with
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the
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technical
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variables.
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Created a matrix of M values, didn't
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split
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read
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and
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green.
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Relative
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variance,
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no
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normalization
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and
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the
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outliers:
Based on the plot will look at the first 8 principal components:
| Code Block | ||
|---|---|---|
| ||
batchID concentration plate_column plate_row dateCombined
V1 2.024556e-22 0.5182919 0.22249235 0.9371285 2.024556e-22
V2 1.777673e-18 0.2878497 0.40175378 0.6195123 1.777673e-18
V3 3.196508e-01 0.3802798 0.27628233 0.5517096 3.196508e-01
V4 1.693859e-30 0.2449447 0.50367703 0.9672545 1.693859e-30
V5 2.435091e-03 0.1812444 0.08644977 0.5581507 2.435091e-03
V6 4.437547e-03 0.9473683 0.15938639 0.8458098 4.437547e-03
V7 1.271181e-03 0.3644802 0.79816984 0.7038321 1.271181e-03
V8 1.051940e-05 0.5905213 0.28713862 0.2173504 1.051940e-05{code}
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Batch
...
and
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dateCombined
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are
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highly
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correlated
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with
...
the
...
first
...
principal
...
components
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(V1
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-
...
V8
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are
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the
...
principal
...
components
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after
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performing
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an
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SVD
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on
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unnormalized
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matrix)
...
Start
...
by
...
removing
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the
...
batch.
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Relative
...
variance
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and
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the
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outliers
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after
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removing
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the
...
batch.
Yikes.
Correlation with the first principal components:
| Code Block | ||
|---|---|---|
| ||
batchID concentration plate_column plate_row dateCombined
V1 0.9717423 0.8262431 0.18591881 0.8304766 0.9717423
V2 0.9976239 0.4612353 0.34203646 0.3816463 0.9976239
V3 0.9578584 0.9056604 0.12948457 0.1792408 0.9578584
V4 0.9043202 0.4152433 0.02150515 0.6264030 0.9043202
V5 0.9991262 0.8505841 0.19052765 0.6834312 0.9991262
V6 0.8956311 0.1123490 0.55257726 0.7618414 0.8956311
V7 0.9991696 0.7699433 0.84761783 0.2805982 0.9991696
V8 0.9939025 0.6395495 0.44489016 0.6334089 0.9939025{code}
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Consider
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the
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data
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to
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be
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normalized.
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eSet object is available.