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1. Carefully identify the probes, remove the from the raw data (5060 + ~800).
2. Create two datasets: batch only normalized (125 tumor patients) and gender,age normalized (no need to remove batch since they all came from a single batch; 100 patients).
3. Repeat ConsensusClusterPlus with the most variable probes (HC, complete linkage, euclidean distance). Use 10% of the original probe number as described in the Sweave document. They followed the vignette directly. 
4. Repeat ConsensusClusterPlus using K-means, K=2:6, Pearson correlation. Use 10% of the original probe number as described in the Sweave document.Batch only normalized, HC, euclidean distance.

ConsensusClusterPlus, XY probes removed, hierarchical/euclidean

Batch removed

May be there are like 5 or 6 clusters but not 4. It definitely doesn't look the the clusters identified in the paper.

Gender and age adjusted, HC, euclidean distance. 

This looks significantly worse. 

 Final attempt:

ConsensusClusterPlus, K

means

, pearson correlation

Batch removed :(125 patients)

Code Block
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> icl[["clusterConsensus"]] k cluster clusterConsensus [1,] 2 1 0.8790997 [2,] 2 2 0.8825873 [3,] 3 1 0.8900365 [4,] 3 2 0.8615545 [5,] 3 3 0.8763479 [6,] 4 1 0.7145161 [7,] 4 2 0.8060535 [8,] 4 3 0.9781181 [9,] 4 4 0.9889430 [10,] 5 1 0.8289404 [11,] 5 2 0.7577152 [12,] 5 3 0.8221909 [13,] 5 4 0.7363796 [14,] 5 5 0.9454712 [15,] 6 1 0.8789223 [16,] 6 2 0.7593188 [17,] 6 3 0.7090342 [18,] 6 4 0.7150963 [19,] 6 5 0.9857516 [20,] 6 6 0.9189523

 I

tried to correlate clusters (Summary table for association with clinical variable for K=2,3 and K=4) with age and gender. Looks that the clusters don't correlate with age at all but have some correlation with gender.

K = 4:

Code Block
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> kruskal.test(tumorMeta$Age,consClass4) Kruskal-Wallis rank sum test data: tumorMeta$Age and consClass4 Kruskal-Wallis chi-squared = 4.9015, df = 3, p-value = 0.1792 > chisq.test(tumorMeta$Gender,consClass4) Pearsons Chi-squared test data: tumorMeta$Gender and consClass4 X-squared = 14.7676, df = 3, p-value = 0.002026

Age distribution among clusters:

Image Removed

Test for association with mutation status:

Code Block
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> chisq.test(k,tumorMeta$BRAF_mutation) Pearson's Chi-squared test data: k and tumorMeta$BRAF_mutation X-squared = 95.1974, df = 3, p-value < 2.2e-16 Warning message: In chisq.test(k, tumorMeta$BRAF_mutation) : Chi-squared approximation may be incorrect > chisq.test(k,tumorMeta$KRAS_mutation) Pearson's Chi-squared test data: k and tumorMeta$KRAS_mutation X-squared = 26.6428, df = 3, p-value = 6.995e-06 Warning message: In chisq.test(k, tumorMeta$KRAS_mutation) : Chi-squared approximation may be incorrect > chisq.test(k,tumorMeta$TP53_mutation) Pearson's Chi-squared test data: k and tumorMeta$TP53_mutation X-squared = 12.1586, df = 3, p-value = 0.006859

 

K = 3:

Code Block
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> kruskal.test(tumorMeta$Age,consClass3) Kruskal-Wallis rank sum test data: tumorMeta$Age and consClass3 Kruskal-Wallis chi-squared = 2.4866, df = 2, p-value = 0.2884 > chisq.test(tumorMeta$Gender,consClass3) Pearsons Chi-squared test data: tumorMeta$Gender and consClass3 X-squared = 5.9141, df = 2, p-value = 0.05197

So it is not very correlated with age and it is somewhat correlated with gender. 

Test for association with mutation status:

Code Block
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> k<-resultsK[[3]][["consensusClass"]] > chisq.test(k,tumorMeta$BRAF_mutation) Pearson's Chi-squared test data: k and tumorMeta$BRAF_mutation X-squared = 50.5952, df = 2, p-value = 1.031e-11 Warning message: In chisq.test(k, tumorMeta$BRAF_mutation) : Chi-squared approximation may be incorrect > chisq.test(k,tumorMeta$KRAS_mutation) Pearson's Chi-squared test data: k and tumorMeta$KRAS_mutation X-squared = 6.7096, df = 2, p-value = 0.03492 > chisq.test(k,tumorMeta$TP53_mutation) Pearson's Chi-squared test data: k and tumorMeta$TP53_mutation X-squared = 25.1538, df = 2, p-value = 3.451e-06

 

Gender and age are removed, ConsensusClusterPlus, K means, Pearson correlation:,4,5,6 (only batch is removed)

K	2	3	4	5	6
Age	0.78	0.88	0.7	0.6345	0.444
Gender	0.12	0.05	2.02e-03	5.35e-03	8.097155e-03
Rectal/colon	0.0016	4.74e-04	4.44e-03	7.41e-03	2.2e-03
Tumor stage	0.36	0.43	0.23	0.3	0.58
BRAF	1.33e-05	1.03e-11	1.67e-20	1.01e-19	5.15e-19
KRAS	3.87e-03	0.04	6.7e-06	1.22e-04	5.97e-06
KRAS type	1.97e-02	0.24	7.14e-03	1.59e-02	2.32e-03
TP53	0.96	3.45e-06	6.86e-03	1.75e-03	1.26e-03
MLH1 mutation	1.26e-04	5.11e-10	1.45e-20	8.96e-20	4.12e-19

 

ConsensusClusterPlus, K means, Pearson correlation

Age/gender removed, 100 patients

Cluster consensus:

> icl[["clusterConsensus"]]
      k cluster clusterConsensus
 [1,] 2       1        0.9008927
 [2,] 2       2        0.9411635
 [3,] 3       1        0.8680856
 [4,] 3       2        0.8760303
 [5,] 3       3        0.7333630
 [6,] 4       1        0.7745014
 [7,] 4       2        0.7901681
 [8,] 4       3        0.8024310
 [9,] 4       4        0.8247401
[10,] 5       1        0.9153414
[11,] 5       2        0.8141924
[12,] 5       3        0.6573712
[13,] 5       4        0.6708655
[14,] 5       5        0.6319631
[15,] 6       1        0.8695192
[16,] 6       2        0.8568005
[17,] 6       3        0.7128842
[18,] 6       4        0.7407073
[19,] 6       5        0.5585342
[20,] 6       6        0.7426816

Test for association of clusters with mutation status. K = 2.

#w is the data frame with clinical information for 100 tumor patients
 
> chisq.test(k,w$BRAF_mutation)
        Pearson's Chi-squared test with Yates' continuity correction
data:  k and w$BRAF_mutation 
X-squared = 15.2964, df = 1, p-value = 9.189e-05
> chisq.test(k,w$KRAS_mutation)
        Pearson's Chi-squared test with Yates' continuity correction
data:  k and w$KRAS_mutation 
X-squared = 5.0882, df = 1, p-value = 0.02409
> chisq.test(k,w$TP53_mutation)
        Pearson's Chi-squared test with Yates' continuity correction
data:  k and w$TP53_mutation 
X-squared = 0.12, df = 1, p-value = 0.729