Statistical analysis of intramembranous particles using freeze fracture specimens

Schladitz, Katja; Särkkä, Aila; Pavenstädt, Iris; Haferkamp, O; Mattfeldt, Torsten

doi:10.1046/j.1365-2818.2003.01210.x

Cited by 25 publications

(49 citation statements)

References 35 publications

(55 reference statements)

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“…Presumably a combination of a hard‐core phenomenon with ordering at longer distances is not untypical for biological materials. For example, it was found by explorative methods also in a previous study on point patterns of intramembranous particles on freeze fracture replicas (Schladitz et al , 2003; Mattfeldt, 2005). Besides, this statement holds only for the normal case group and not for the carcinoma group.…”

Section: Discussionmentioning

confidence: 71%

“…For each r ‐value, this yielded altogether 1000 g ‐values, including the g ‐value from the true sample. The data analysis was then performed in a global and in a local approach (see Schladitz et al , 2003; Mattfeldt et al , 2006). For the global approach, the 999 integral deviations of the simulated g ‐functions from the mean g ‐function of the simulations, and the single integral deviation of the g ‐function estimated from the true image and the expected g ‐function of the simulations, were computed.…”

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confidence: 99%

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Statistical modelling of the geometry of planar sections of prostatic capillaries on the basis of stationary Strauss hard‐core processes

Mattfeldt¹,

Eckel

Fleischer

et al. 2007

Journal of Microscopy

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SummaryIn a recent study, the capillarization of normal prostatic tissue and prostatic carcinoma tissue was characterized by means of explorative methods of spatial statistics. In the present paper, an attempt was made to go beyond the explorative approach and to characterize the observed point patterns of the capillary profiles on sections by means of a parametric model. For this purpose, the flexible class of Gibbs processes was considered. Specifically, stationary Strauss hard-core processes were fitted to the observed point patterns. The goodness of fit achieved by the model was checked by simulations with the Markov chain Monte Carlo method using the Metropolis-Hastings algorithm. Model fitting and simulations were performed with the help of the SPATSTAT package under R. The observed point patterns were in some cases compatible with realizations of stationary Strauss hard-core processes for all ranges of spatial interaction. However, deviations from the model were found for one or more domains of ranges in other cases. In the tumour tissue, a highly significant decrease of the interaction parameter of the Strauss hard-core process could be found as compared to the normal prostatic tissue. This finding is discussed in terms of a loss of the normal lobular architecture of the glands in the tumour tissue.

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Section: Discussionmentioning

confidence: 71%

mentioning

confidence: 99%

Statistical modelling of the geometry of planar sections of prostatic capillaries on the basis of stationary Strauss hard‐core processes

Mattfeldt¹,

Eckel

Fleischer

et al. 2007

Journal of Microscopy

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“…Although rigorous statistical methodology for a single spatial point pattern is quite extensive, little work has been done in the development of analysis for replicated point patterns, and even less in the analysis of replicated bivariate patterns. Thus the best approach to pooling L-functions from replicated patterns is unclear (Schladitz et al, 2003). This may be due to the application of spatial statistics in the fields of forestry and plant biology, where there is generally a single forest or habitat under study.…”

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confidence: 99%

Two types of Drosophila R7 photoreceptor cells are arranged randomly: A model for stochastic cell‐fate determination

Bell

Earl

Britt

2007

J of Comparative Neurology

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The R7 photoreceptor cells of the Drosophila retina are ultraviolet sensitive and are thought to mediate color discrimination and polarized light detection. In addition, there is growing evidence that the color sensitivity of the R8 cell within an individual ommatidium is regulated by a genetic switch that depends on the type of R7 cell adjacent to it. Here we examine the organization of the two major types of R7 cells by three different rigorous statistical methods and present evidence that they are arranged randomly and independently. First, we performed L-function analyses to test whether the organization of R7 cells (and the relationship between them) is regular, clustered, or completely spatially random. Next, we used generalized linear mixed models to test whether the proportion of R7 cell neighbors differs from their prevalence within the eye as a whole. Finally, we conducted a series of simulations to test whether the proportion of R7 cell neighbors differs from that in a random simulation. In each case, we found evidence that the organization of the two types of R7 cells is random and independent, suggesting that R7 cells in neighboring ommatidia are unlikely to interact and influence each other's identity and may be determined stochastically in a cell-autonomous manner. Compared with traditional lineage or inductive mechanisms, this may represent a novel mechanism of cell fate determination based on noisy or stochastic gene expression in which the differentiation of an individual R7 cell is a random event but the proportions of R7 cell subtypes are regulated.

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“…First, rather than studying the interactions between different types of points on the same image (Eckel et al ., 2009; Diggle et al ., 2006), we study the interactions between the points from subject-pairs. Thus, our replicates are pairs of subjects instead of individual subjects, locations, or images (Myllymäki et al ., 2012; Waller et al ., 2011; Schladitz et al ., 2003; Diggle et al ., 2000; Baddeley et al ., 1993). This introduces a complicated dependence structure between our replicates that needs to be accounted for.…”

Section: Introductionmentioning

confidence: 99%

Characterizing cross‐subject spatial interaction patterns in functional magnetic resonance imaging studies: A two‐stage point‐process model

et al. 2017

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We develop a two-stage spatial point process model that introduces new characterizations of activation patterns in multi-subject functional Magnetic Resonance Imaging (fMRI) studies. Conventionally multi-subject fMRI methods rely on combining information across subjects one voxel at a time in order to identify locations of peak activation in the brain. The two-stage model that we develop here addresses shortcomings of standard methods by explicitly modeling the spatial stucture of functional signals and recognizing that corresponding cross-subject functional signals can be spatially misaligned. In our first stage analysis, we introduce a marked spatial point process model that captures the spatial features of the functional response and identifies a configuration of activation units for each subject. The locations of these activation units are used as input for the second stage model. The point process model of the second stage analysis is developed to characterize multi-subject activation patterns by estimating the strength of cross-subject interactions at different spatial ranges. The model uses spatial neighborhoods to account for the cross-subject spatial misalignment in corresponding functional units. We applied our methods to an fMRI study of 21 individuals who performed an attention test. We identified four brain regions that are involved in the test and found that our model results agree well with our understanding of how these regions engage with the tasks performed during the attention test. Our results highlighted that cross-subject interactions are stronger in brain areas that have a more specific function in performing the experimental tasks than in other areas.

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Statistical analysis of intramembranous particles using freeze fracture specimens

Cited by 25 publications

References 35 publications

Statistical modelling of the geometry of planar sections of prostatic capillaries on the basis of stationary Strauss hard‐core processes

Statistical modelling of the geometry of planar sections of prostatic capillaries on the basis of stationary Strauss hard‐core processes

Two types of Drosophila R7 photoreceptor cells are arranged randomly: A model for stochastic cell‐fate determination

Characterizing cross‐subject spatial interaction patterns in functional magnetic resonance imaging studies: A two‐stage point‐process model

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