Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Discovering monotonic stemness marker genes from time-series stem cell microarray data

Wang, HW and Sun, HJ and Chang, TY and Lo, HH and Cheng, WC and Tseng, GC and Lin, CT and Chang, SJ and Pal, NR and Chung, IF (2015) Discovering monotonic stemness marker genes from time-series stem cell microarray data. BMC Genomics, 16.

Published Version
Available under License : See the attached license file.

Download (4MB) | Preview
[img] Plain Text (licence)
Available under License : See the attached license file.

Download (1kB)


Background: Identification of genes with ascending or descending monotonic expression patterns over time or stages of stem cells is an important issue in time-series microarray data analysis. We propose a method named Monotonic Feature Selector (MFSelector) based on a concept of total discriminating error (DEtotal) to identify monotonic genes. MFSelector considers various time stages in stage order (i.e., Stage One vs. other stages, Stages One and Two vs. remaining stages and so on) and computes DEtotal of each gene. MFSelector can successfully identify genes with monotonic characteristics.Results: We have demonstrated the effectiveness of MFSelector on two synthetic data sets and two stem cell differentiation data sets: embryonic stem cell neurogenesis (ESCN) and embryonic stem cell vasculogenesis (ESCV) data sets. We have also performed extensive quantitative comparisons of the three monotonic gene selection approaches. Some of the monotonic marker genes such as OCT4, NANOG, BLBP, discovered from the ESCN dataset exhibit consistent behavior with that reported in other studies. The role of monotonic genes found by MFSelector in either stemness or differentiation is validated using information obtained from Gene Ontology analysis and other literature. We justify and demonstrate that descending genes are involved in the proliferation or self-renewal activity of stem cells, while ascending genes are involved in differentiation of stem cells into variant cell lineages.Conclusions: We have developed a novel system, easy to use even with no pre-existing knowledge, to identify gene sets with monotonic expression patterns in multi-stage as well as in time-series genomics matrices. The case studies on ESCN and ESCV have helped to get a better understanding of stemness and differentiation. The novel monotonic marker genes discovered from a data set are found to exhibit consistent behavior in another independent data set, demonstrating the utility of the proposed method. The MFSelector R function and data sets can be downloaded from:


Social Networking:
Share |


Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Wang, HW
Sun, HJ
Chang, TY
Lo, HH
Cheng, WC
Tseng, GCctseng@pitt.eduCTSENG
Lin, CT
Chang, SJ
Pal, NR
Chung, IF
Date: 21 January 2015
Date Type: Publication
Journal or Publication Title: BMC Genomics
Volume: 16
DOI or Unique Handle: 10.1186/1471-2164-16-s2-s2
Schools and Programs: School of Public Health > Biostatistics
Refereed: Yes
Date Deposited: 22 Dec 2016 15:25
Last Modified: 10 Jun 2023 11:55


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item