Abstract
Cancer subtyping is of great importance for the prediction, diagnosis, and precise treatment of cancer patients. Many clustering methods have been proposed for cancer subtyping. In 2014, a clustering algorithm named Clustering by Fast Search and Find of Density Peaks (CFDP) was proposed and published in Science, which has been applied to cancer subtyping and achieved attractive results. However, CFDP requires to set two key parameters (cluster centers and cutoff distance) manually, while their optimal values are difficult to be determined. To overcome this limitation, an automatic clustering method named PSO-CFDP is proposed in this paper, in which cluster centers and cutoff distance are automatically determined by running an improved particle swarm optimization (PSO) algorithm multiple times. Experiments using PSO-CFDP, as well as LR-CFDP, STClu, CH-CCFDAC, and CFDP, were performed on four benchmark data-sets and two real cancer gene expression datasets. The results show that PSO-CFDP can determine cluster centers and cutoff distance automatically within controllable time/cost and, therefore, improve the accuracy of cancer subtyping.
References
1.
Azuaje
F
. Clustering-based approaches to discovering and visualising microarray data patterns
. Brief Bioinform
. 2003
Mar
;4
(1
):31
–42
.
[PubMed]
1467-54632.
Du
M
, Ding
S
, Jia
H
. Study on density peaks clustering based on k-nearest neighbors and principal component analysis
. Knowl Base Syst
. 2016
;99
:135
–45
. 0950-70513.
MacQueen
J
, editor. Some methods for classification and analysis of multivariate observations.
Proceedings of the fifth Berkeley symposium on mathematical statistics and probability
; 1967
; Oakland, CA, USA
: Oakland, CA, USA.4.
Johnson
SC
. Hierarchical clustering schemes
. Psychometrika
. 1967
Sep
;32
(3
):241
–54
.
[PubMed]
0033-31235.
Turakulov
R
, Easteal
S
. Number of SNPS loci needed to detect population structure
. Hum Hered
. 2003
;55
(1
):37
–45
.
[PubMed]
0001-56526.
Kohonen
T
, Somervuo
P
. Self-organizing maps of symbol strings
. Neurocomputing
. 1998
;21
(1-3
):19
–30
. 0925-23127.
Monti
S
, Tamayo
P
, Mesirov
J
, Golub
T
. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data
. Mach Learn
. 2003
;52
(1-2
):91
–118
. 0885-61258.
Shen
R
, Olshen
AB
, Ladanyi
M
. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis
. Bioinformatics
. 2009
Nov
;25
(22
):2906
–12
.
[PubMed]
1367-48039.
Rodriguez
A
, Laio
A
. Machine learning. Clustering by fast search and find of density peaks
. Science
. 2014
Jun
;344
(6191
):1492
–6
.
[PubMed]
0036-807510.
Mehmood
R
, El-Ashram
S
, Bie
R
, Sun
Y
. Effective cancer subtyping by employing density peaks clustering by using gene expression microarray
. Pers Ubiquitous Comput
. 2018
;22
(3
):615
–9
. 1617-490911.
Li
X
, Wong
KC
. Evolutionary multiobjective clustering and its applications to patient stratification
. IEEE Trans Cybern
. 2018
;(99
):1
–14
.
[PubMed]
2168-226712.
Wang
G
, Song
Q
. Automatic clustering via outward statistical testing on density metrics
. IEEE Trans Knowl Data Eng
. 2016
;28
(8
):1971
–85
. 1041-434713.
Pengcheng
G
, Xing
W
, Yubing
W
, Yue
C
, Ying
Z
, editors. Research on automatic determining clustering centers algorithm based on linear regression analysis.
Image, Vision and Computing (ICIVC), 2017
2nd International Conference on; 2017: IEEE. 14.
Jinyin
C
, Xiang
L
, Haibing
Z
, Xintong
B
. A novel cluster center fast determination clustering algorithm
. Appl Soft Comput
. 2017
;57
:539
–55
. 1568-494615.
Zhang
W
, Shang
J
, Li
H
, Sun
Y
, Liu
JX
, editors. SIPSO: selectively informed particle swarm optimization based on mutual information to determine SNP-SNP interactions.
International Conference on Intelligent Computing
; 2016
: Springer.
16.
Sun
Y
, Shang
J
, Liu
JX
, Li
S
, Zheng
CH
. epiACO - a method for identifying epistasis based on ant Colony optimization algorithm
. BioData Min
. 2017
Jul
;10
(1
):23
.
[PubMed]
1756-038117.
Zhou
R
, Liu
Q
, Xu
Z
, Wang
L
, Han
X
. Improved Fruit Fly Optimization Algorithm-based density peak clustering and its applications
. Teh Vjesn
. 2017
;24
(2
):473
–80
.1330-365118.
Yang
XS
, Deb
S
. Cuckoo search: recent advances and applications
. Neural Comput Appl
. 2014
;24
(1
):169
–74
. 0941-064319.
Ling
HL
, Wu
JS
, Zhou
Y
, Zheng
WS
. How many clusters? A robust PSO-based local density model
. Neurocomputing
. 2016
;207
:264
–75
. 0925-231220.
Wang
Y
, Pang
W
, Zhou
Y
. Density propagation based adaptive multi-density clustering algorithm
. PLoS One
. 2018
Jul
;13
(7
):e0198948
.
[PubMed]
1932-620321.
Shang
J
, Sun
Y
, Liu
JX
, Xia
J
, Zhang
J
, Zheng
CH
. CINOEDV: a co-information based method for detecting and visualizing n-order epistatic interactions
. BMC Bioinformatics
. 2016
May
;17
(1
):214
.
[PubMed]
1471-210522.
Chen
CY
, Ye
F
, editors. Particle swarm optimization algorithm and its application to clustering analysis.
Electrical Power Distribution Networks (EPDC), 2012
Proceedings of 17th Conference on
; 2012
: IEEE.
23.
Shang
J
, Sun
Y
, Li
S
, Liu
J-X
, Zheng
C-H
, Zhang
J
. An improved opposition-based learning particle swarm optimization for the detection of SNP-SNP interactions.
BioMed research international. 2015
;2015. 24.
Guimin
C
, Jianyuan
J
, Qi
H
. Study on the strategy of decreasing inertia weight in particle swarm optimization algorithm
. Journal-Xian Jiaotong University.
2006
;40
(1
):53
.25.
Clerc
M
, Kennedy
J
. The particle swarm-explosion, stability, and convergence in a multidimensional complex space
. IEEE Trans Evol Comput
. 2002
;6
(1
):58
–73
. 1089-778X26.
Rendón
E
, Abundez
I
, Arizmendi
A
, Quiroz
EM
. Internal versus external cluster validation indexes.
International Journal of computers communications. 2011
;5(1):27-34.27.
Sun
H
, Wang
S
, Jiang
Q
. FCM-based model selection algorithms for determining the number of clusters
. Pattern Recognit
. 2004
;37
(10
):2027
–37
. 0031-320328.
Yeung
KY
, Ruzzo
WL
. Principal component analysis for clustering gene expression data
. Bioinformatics
. 2001
Sep
;17
(9
):763
–74
.
[PubMed]
1367-480329.
Golub
TR
, Slonim
DK
, Tamayo
P
, Huard
C
, Gaasenbeek
M
, Mesirov
JP
, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. science. 1999
;286(5439):531-7.30.
Liu
JX
, Wang
D
, Gao
YL
, Zheng
CH
, Xu
Y
, Yu
J
. Regularized non-negative matrix factorization for identifying differential genes and clustering samples: a survey
. IEEE/ACM Trans Comput Biol Bioinformatics
. 2017
.1545-596331.
Monti
S
, Savage
KJ
, Kutok
JL
, Feuerhake
F
, Kurtin
P
, Mihm
M
, et al. Molecular profiling of diffuse large B-cell lymphoma identifies robust subtypes including one characterized by host inflammatory response
. Blood
. 2005
Mar
;105
(5
):1851
–61
.
[PubMed]
0006-497132.
Hoshida
Y
, Brunet
JP
, Tamayo
P
, Golub
TR
, Mesirov
JP
. Subclass mapping: identifying common subtypes in independent disease data sets
. PLoS One
. 2007
Nov
;2
(11
):e1195
.
[PubMed]
1932-6203© 2019 S. Karger AG, Basel
2019
Copyright / Drug Dosage / Disclaimer
Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.
You do not currently have access to this content.
