A test for spatial randomness based on k-NN distances

Zeng, Guangzhou; Dubes, Richard C.

doi:10.1016/0167-8655(85)90013-3

Cited by 16 publications

(9 citation statements)

References 7 publications

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“…U j are the minimum distances of the sub-models from m random points in the sampling window. To define the sampling window, we either took 25 to 75 percentile of the feature values or from δ to max.value-δ along each dimension, where δ denotes the standard deviation of the feature value (Dubes & Zeng 1987; Zeng & Richard C Dubes 1985; Zeng & Richard C. Dubes 1985). To estimate p-value, we repeat the above procedure 1000 times and measured the H value.…”

Section: Methodsmentioning

confidence: 99%

Heterogeneity of Transcription Factor binding specificity models within and across cell lines

Sharmin

Bravo

Hannenhalli

2015

Preprint

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Complex gene expression patterns are mediated by binding of transcription factors (TF) to specific genomic loci. The in vivo occupancy of a TF is, in large part, determined by the TFs DNA binding interaction partners, motivating genomic context based models of TF occupancy. However, the approaches thus far have assumed a uniform binding model to explain genome wide bound sites for a TF in a cell-type and as such heterogeneity of TF occupancy models, and the extent to which binding rules underlying a TFs occupancy are shared across cell types, has not been investigated. Here, we develop an ensemble based approach (TRISECT) to identify heterogeneous binding rules of cell-type specific TF occupancy and analyze the inter-cell-type sharing of such rules. Comprehensive analysis of 23 TFs, each with ChIP-Seq data in 4-12 cell-types, shows that by explicitly capturing the heterogeneity of binding rules, TRISECT accurately identifies in vivo TF occupancy (93%) substantially improving upon previous methods. Importantly, many of the binding rules derived from individual cell-types are shared across cell-types and reveal distinct yet functionally coherent putative target genes in different cell-types. Closer inspection of the predicted cell-type-specific interaction partners provides insights into context-specific functional landscape of a TF. Together, our novel ensemble-based approach reveals, for the first time, a widespread heterogeneity of binding rules, comprising interaction partners within a cell-type, many of which nevertheless transcend cell-types. Notably, the putative targets of shared binding rules in different cell-types, while distinct, exhibit significant functional coherence.

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Section: Methodsmentioning

confidence: 99%

Heterogeneity of Transcription Factor binding specificity models within and across cell lines

Sharmin

Bravo

Hannenhalli

2015

Preprint

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“…Of the four general types of statistical test identified by Gordon (1996a; i.e. presence of any group structure in the data, validity of an individual cluster, validity of a partition and validity of a complete hierarchical classification), the third is most relevant to this problem; however, the only other tests proposed for kNNDs relate to the first type of test (Zeng & Dubes 1985;Deheuvels et al 1988).…”

Section: A New Criterion For the Number Of Groupsmentioning

confidence: 99%

Cluster analysis and the identification of aggregations

Strauss

2001

Animal Behaviour

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“…Values around 1/2 are expected under randomness. When the points are generated by a Poisson process with intensity h, H has an approximate beta distribution with parameters (M,N), independent of h and d. Zeng and Dubes (1985b) extend H by measuring distance to the k-th nearest neighbor. Cox and Lewis (1976) proposed a statistic in two dimensions based on the ratio Ri of two distances.…”

Section: H=mentioning

confidence: 99%