國立虎尾科技大學 |

Detecting community structure in RNA-Seq co-expression and EEG networks.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Detecting community structure in RNA-Seq co-expression and EEG networks./
作者:	Rahmani, Bahareh.
面頁冊數:	1 online resource (82 pages)
附註:	Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9781339826837

Detecting community structure in RNA-Seq co-expression and EEG networks.
Rahmani, Bahareh.

Detecting community structure in RNA-Seq co-expression and EEG networks. - 1 online resource (82 pages)

Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.

Thesis (Ph.D.)--The University of Tulsa, 2016.

Includes bibliographical references

Clusters of genes in co-expression networks are commonly used as functional units for gene set enrichment detection and increasingly as features (attribute construction) for statistical inference and sample classification. One of the practical challenges of clustering for these purposes is to identify an optimal partition of the network where the individual clusters are neither too large, prohibiting interpretation, nor too small, precluding general inference. Newman Modularity is a spectral clustering algorithm that automatically finds the number of clusters, but for many biological networks the cluster sizes are suboptimal. This thesis generalizes Newman Modularity to incorporate information from indirect paths in RNA-Seq co-expression networks. The approach includes a merge-and-split algorithm that allows the user to constrain the range of cluster sizes: large enough to capture genes in relevant pathways, yet small enough to resolve distinct functions. I investigate the properties of our recursive indirect-pathways modularity (RIP-M) and compare it with other clustering methods using simulated co-expression networks and RNA-Seq data from an influenza vaccine response study. RIP-M had higher cluster assignment accuracy than Newman Modularity for finding clusters in simulated co-expression networks for all scenarios, and RIP-M had comparable accuracy to Weighted Gene Correlation Network Analysis (WGCNA). RIP-M was more accurate than WGCNA for modest hard thresholds and comparable for high, while WGCNA was slightly more accurate for soft thresholds. In the RNA-Seq study of immune response to vaccine, RIP-M and WGCNA enriched for a comparable number of immunologically relevant pathways.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781339826837Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Detecting community structure in RNA-Seq co-expression and EEG networks.
LDR:03819ntm a2200349K 4500 001 915757
005 20180823122923.5
006 m o u
007 cr mn||||a|a||
008 190606s2016 xx obm 000 0 eng d
020 $a 9781339826837
035 $a (MiAaPQ)AAI10124581
035 $a (MiAaPQ)utulsa:10123
035 $a AAI10124581
040 $a MiAaPQ $b eng $c MiAaPQ
100 1 $a Rahmani, Bahareh. $3 1189244
245 1 0 $a Detecting community structure in RNA-Seq co-expression and EEG networks.
264 0 $c 2016
300 $a 1 online resource (82 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
500 $a Includes supplementary digital materials.
500 $a Adviser: Brett McKinney.
502 $a Thesis (Ph.D.)--The University of Tulsa, 2016.
504 $a Includes bibliographical references
520 $a Clusters of genes in co-expression networks are commonly used as functional units for gene set enrichment detection and increasingly as features (attribute construction) for statistical inference and sample classification. One of the practical challenges of clustering for these purposes is to identify an optimal partition of the network where the individual clusters are neither too large, prohibiting interpretation, nor too small, precluding general inference. Newman Modularity is a spectral clustering algorithm that automatically finds the number of clusters, but for many biological networks the cluster sizes are suboptimal. This thesis generalizes Newman Modularity to incorporate information from indirect paths in RNA-Seq co-expression networks. The approach includes a merge-and-split algorithm that allows the user to constrain the range of cluster sizes: large enough to capture genes in relevant pathways, yet small enough to resolve distinct functions. I investigate the properties of our recursive indirect-pathways modularity (RIP-M) and compare it with other clustering methods using simulated co-expression networks and RNA-Seq data from an influenza vaccine response study. RIP-M had higher cluster assignment accuracy than Newman Modularity for finding clusters in simulated co-expression networks for all scenarios, and RIP-M had comparable accuracy to Weighted Gene Correlation Network Analysis (WGCNA). RIP-M was more accurate than WGCNA for modest hard thresholds and comparable for high, while WGCNA was slightly more accurate for soft thresholds. In the RNA-Seq study of immune response to vaccine, RIP-M and WGCNA enriched for a comparable number of immunologically relevant pathways.
520 $a Another important area where networks occur is in electroencephalogram (EEG) data from neuroimaging studies of the human brain. Extracting useful information from these signals directly from the time domain is challenging because of the non-linear and non-stationary nature of the time series. Hence, to extract important EEG features to inform diagnosis of brain and mood disorders, advanced signal processing techniques are required. I apply the Hurst exponent to EEG time series to find the most affected parts of the brain in post-traumatic stress disorder (PTSD) cases. Hurst exponents, which are calculated using the R/S technique, measure the long memory of EEG signals. I explore the possibility of creating a Hurst exponent-based method for informing diagnosis of PTSD and show that the Hurst exponent has the potential to capture long-memory properties in EEG data.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Bioinformatics. $3 583857
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0715
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of Tulsa. $b Computer Science. $3 1189245
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10124581 $z click for full text (PQDT)