sciclone iteration does not converge
2
0
Entering edit mode
14 months ago
2891913164 • 0

Hello everyone: I have a problem when using sciclone. I extracted the relevant information needed by sciclone from the vcf file generated from the paired normal tumor data as input, and then the following problem has been encountered. I would like to ask whether a certain screening should be carried out when extracting data from the vcf file? If I need to filter the vcf file, what criteria should I use to filter it? Hope to get your help, thank you very much!

[1] "checking input data..."
12462 sites (of 12480 original sites) are copy number neutral and have adequate depth in all samples
0 sites (of 12480 original sites) were removed because of copy-number alterations
18 sites (of 12480 original sites) were removed because of inadequate depth
18 sites (of 12480 original sites) were removed because of copy-number alterations or inadequate depth
[1] "clustering..."
Disable overlapping std dev condition
kmeans initialization:
V1
0.369202574615969
0.0451805969314854
0.0012764317159691
0.068427701248344
0.130563008213922
0.508433685628903
0.419371045057848
0.567150829909654
0.0267695356487535
0.23147262643314
0.998687595233288
0.0133681853132312
0.00484191691530496
0.096954918736847
0.767968455466162
0.306889133938259
0.893953674722921
0.171738804853286
0.647833820706109
0.469620180355922
Using threshold:  0.7
Dropped cluster 5 with too few variants ( 0 ) center: 0.4961276
Dropped cluster 5 with too few variants ( 0 ) center: 0.4889433
Dropped cluster 5 with too few variants ( 0 ) center: 0.4961276
Dropped cluster 7 with too few variants ( 0 ) center: 0.4961276
Dropped cluster 11 with too few variants ( 0 ) center: 0.4961276
Dropped cluster 12 with too few variants ( 0 ) center: 0.4961276
Dropped cluster 12 with too few variants ( 0 ) center: 0.228628
Cluster 1 pi = 0.381 center = 0.382 SEM = (0.372, 0.393) sd = (0.000, 1.000)
Cluster 2 pi = 0.557 center = 0.004 SEM = (0.004, 0.004) sd = (0.000, 0.006)
Cluster 3 pi = 0.062 center = 0.479 SEM = (0.476, 0.482) sd = (0.417, 0.544)
Converged on the following parameters:
mu:
3088.34643978221 88967.680080543 35087.7594185802
alpha:
57064.3763044592 40966.6875698716 574.763038704414
nu:
1720.59558497192 15169.4708677206 33819.9881156314
beta:
19643.4195415117 25.601254166207 510.134678204943
pi:
0.380504889742695   0.557239249326397   0.0622558639309075
[1] "finished clustering full-dimensional data..."
[1] "found 3 clusters using bmm in full dimensional data"
There were 50 or more warnings (use warnings() to see the first 50)
> writeClusterTable(sc, "test.new/clusters1")
> sc.plot1d(sc,"test.new/clusters1.1d.pdf")
>
warnings()
1: did not converge in 10 iterations
2: did not converge in 10 iterations
3: did not converge in 10 iterations
4: did not converge in 10 iterations
5: did not converge in 10 iterations
6: Quick-TRANSfer stage steps exceeded maximum (= 623100)
7: did not converge in 10 iterations
8: did not converge in 10 iterations
9: did not converge in 10 iterations
10: did not converge in 10 iterations
11: did not converge in 10 iterations
12: did not converge in 10 iterations
13: did not converge in 10 iterations
14: did not converge in 10 iterations
15: Quick-TRANSfer stage steps exceeded maximum (= 623100)
16: did not converge in 10 iterations
17: did not converge in 10 iterations
18: Quick-TRANSfer stage steps exceeded maximum (= 623100)
19: Quick-TRANSfer stage steps exceeded maximum (= 623100)
20: Quick-TRANSfer stage steps exceeded maximum (= 623100)
21: did not converge in 10 iterations
22: Quick-TRANSfer stage steps exceeded maximum (= 623100)
23: did not converge in 10 iterations
24: Quick-TRANSfer stage steps exceeded maximum (= 623100)
25: did not converge in 10 iterations
26: did not converge in 10 iterations
27: did not converge in 10 iterations
28: Quick-TRANSfer stage steps exceeded maximum (= 623100)
29: did not converge in 10 iterations
30: did not converge in 10 iterations
31: did not converge in 10 iterations
32: did not converge in 10 iterations
33: did not converge in 10 iterations
34: did not converge in 10 iterations
35: did not converge in 10 iterations
36: did not converge in 10 iterations
37: did not converge in 10 iterations
38: did not converge in 10 iterations
39: did not converge in 10 iterations
40: did not converge in 10 iterations
41: did not converge in 10 iterations
42: did not converge in 10 iterations
43: did not converge in 10 iterations
44: did not converge in 10 iterations
45: Quick-TRANSfer stage steps exceeded maximum (= 623100)
46: did not converge in 10 iterations
47: did not converge in 10 iterations
48: did not converge in 10 iterations
49: did not converge in 10 iterations
50: did not converge in 10 iterations

sciclone • 701 views
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Entering edit mode

Tagging: Chris Miller

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Entering edit mode
14 months ago

Failure to converge is a warning, not an error, but generally speaking, it probably suggests that your data is not easy to cluster. Here are some of the questions you should be asking yourself as you iterate on analyzing it: What does the plot look like? Can you at least sort of pick out clusters by eye in the density plot? How confident are you that all 12k sites are actually somatic (vs artifacts/noise)? It looks like there were no copy number calls input - are you sure that all your sites are CN-neutral?

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Entering edit mode

Dear chris: Thank you very much for your reply! I still have a problem when there are multiple base mutations at the mutation site, like the following situation. So when extracting var_read, should one kind of mutant base be randomly selected as var_read or the read of all the mutant bases are taken into consideration?

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Entering edit mode

SciClone expects each mutation to have a unique position, but doesn't actually care what those positions are. You could split this into multiple lines (vt decompose is one option) or just do it manually if there are only one or two.

22  42524986    ref   var   vaf
22  42524986_1  ref   var   vaf