2007
DOI: 10.6026/97320630002005
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Mfuzz: A software package for soft clustering of microarray data

Abstract: Abstract:For the analysis of microarray data, clustering techniques are frequently used. Most of such methods are based on hard clustering of data wherein one gene (or sample) is assigned to exactly one cluster. Hard clustering, however, suffers from several drawbacks such as sensitivity to noise and information loss. In contrast, soft clustering methods can assign a gene to several clusters. They can overcome shortcomings of conventional hard clustering techniques and offer further advantages. Thus, we constr… Show more

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Cited by 1,242 publications
(1,006 citation statements)
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“…The remaining root-expressed genes' expression profiles were standardized such that mean = 0 and SD = 1, followed by fuzzy C-means clustering using R package Mfuzz (Kumar and E Futschik, 2007). After monitoring the minimum distances between cluster centroids, the number of groups was optimized as 9.…”
Section: Gene Expression Pattern Type Assignmentmentioning
confidence: 99%
See 1 more Smart Citation
“…The remaining root-expressed genes' expression profiles were standardized such that mean = 0 and SD = 1, followed by fuzzy C-means clustering using R package Mfuzz (Kumar and E Futschik, 2007). After monitoring the minimum distances between cluster centroids, the number of groups was optimized as 9.…”
Section: Gene Expression Pattern Type Assignmentmentioning
confidence: 99%
“…Expression profile types were then assigned by clustering using the Mfuzz program (Kumar and E Futschik, 2007) as above or by FC (described below). Comparisons within species for the same expression profile types were assigned a P value of 0, whereas comparisons within species for different expression profile types were assigned a P value of 1 because each species was only able to have one expression profile type within a given family in this analysis.…”
Section: Generation and Analyses Of Supergenesmentioning
confidence: 99%
“…83 Loci were required to have at least one read associated per stage, a between-stage variance of at least two and an associated target outside of any piRNA cluster, leaving 7,351 loci. Read count data were similarly normalized and standardized as described above.…”
Section: Enrichment Of Individual Pirnas In Temporal Profilesmentioning
confidence: 99%
“…We found 510 genes that were differentially expressed. They were sorted into six different groups according to their expression profile using the c-means algorithm (Kumar and Futschik, 2007) (Supplementary Figure S3), and putative biological functions were assigned based on clusters of orthologous groups (Supplementary Figure S4). Several microarray data were confirmed by quantitative RT-PCR (Supplementary Table S3).…”
Section: Cultivation Mediummentioning
confidence: 99%