| 1. |
- Chakraborty, Paramita, et al.
(författare)
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Vesicular Location and Transport of S100A8 and S100A9 Proteins in Monocytoid Cells.
- 2015
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 10:12
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Tidskriftsartikel (refereegranskat)abstract
- We show here, by using surface biotinylation, followed by Western blotting or surface plasmon resonance analysis, that very low levels of S100A8 and/or S100A9 can be detected on the surface of THP-1 cells or freshly isolated human monocytes. This was supported by immune-electron microscopy where we observed membrane-associated expression of the proteins restricted to small patches. By using confocal microscopy we could determine that S100A8 and S100A9 protein in THP-1 cells or freshly isolated human monocytes was mostly present in vesicular structures. This finding was confirmed using immune-electron microscopy. Subcellular fractionation and confocal microscopy showed that these vesicular structures are mainly early endosomes and endolysosomes. Our subsequent studies showed that accumulation of S100A8 and S100A9 in the endolysosomal compartment is associated with induction of their release from the cells. Furthermore, an inhibitor of lysosomal activity could modulate the release of S100A8 and S100A9 in the extracellular milieu. Our current results suggest that the S100A8 and S100A9 proteins are primarily associated with certain kinds of cytosolic vesicles and may be secreted via an endolysosomal pathway.
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| 2. |
- Källberg, David, et al.
(författare)
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A moment-distance hybrid method for estimating a mixture of two symmetric densities
- 2018
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Ingår i: Modern Stochastics: Theory and Applications. - 2351-6054. ; 5:1, s. 1-36
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Tidskriftsartikel (refereegranskat)abstract
- In clustering of high-dimensional data a variable selection is commonly applied to obtain an accurate grouping of the samples. For two-class problems this selection may be carried out by fitting a mixture distribution to each variable. We propose a hybrid method for estimating a parametric mixture of two symmetric densities. The estimator combines the method of moments with the minimum distance approach. An evaluation study including both extensive simulations and gene expression data from acute leukemia patients shows that the hybrid method outperforms a maximum-likelihood estimator in model-based clustering. The hybrid estimator is flexible and performs well also under imprecise model assumptions, suggesting that it is robust and suited for real problems.
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| 3. |
- Källberg, David, 1982-, et al.
(författare)
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Estimation of entropy-type integral functionals
- 2016
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Ingår i: Communications in Statistics - Theory and Methods. - : Informa UK Limited. - 0361-0926 .- 1532-415X. ; 45:4, s. 887-905
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Entropy-type integral functionals of densities are widely used in mathematical statistics, information theory, and computer science. Examples include measures of closeness between distributions (e.g., density power divergence) and uncertainty characteristics for a random variable (e.g., Renyi entropy). In this paper, we study U-statistic estimators for a class of such functionals. The estimators are based on ε-close vector observations in the corresponding independent and identically distributed samples. We prove asymptotic properties of the estimators (consistency and asymptotic normality) under mild integrability and smoothness conditions for the densities. The results can be applied in diverse problems in mathematical statistics and computer science (e.g., distribution identication problems, approximate matching for random databases, two-sample problems).
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| 4. |
- Rydén, Patrik, et al.
(författare)
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The HRD-Algorithm : a general method for parametric estimation of two-component mixture models
- 2017
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Ingår i: Lecture Notes in Computer Science. - Cham : Springer. - 0302-9743 .- 1611-3349. ; 10684, s. 497-508
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Tidskriftsartikel (refereegranskat)abstract
- We introduce a novel approach to estimate the parameters of a mixture of two distributions. The method combines a grid approach with the method of moments and can be applied to a wide range of two-component mixture models. The grid approach enables the use of parallel computing and the method can easily be combined with resampling techniques. We derive the method for the special cases when the data are described by the mixture of two Weibull distributions or the mixture of two normal distributions, and apply the method on gene expression data from 409 ER+" role="presentation" style="box-sizing: border-box; display: inline-table; line-height: normal; letter-spacing: normal; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">ER+ER+ breast cancer patients.
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| 5. |
- Vidman, Linda, et al.
(författare)
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Cluster analysis on high dimensional RNA-seq data with applications to cancer research : An evaluation study
- 2019
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Ingår i: PLOS ONE. - San Francisco : Public Library of Science. - 1932-6203. ; 14:12
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Tidskriftsartikel (refereegranskat)abstract
- Background: Clustering of gene expression data is widely used to identify novel subtypes of cancer. Plenty of clustering approaches have been proposed, but there is a lack of knowledge regarding their relative merits and how data characteristics influence the performance. We evaluate how cluster analysis choices affect the performance by studying four publicly available human cancer data sets: breast, brain, kidney and stomach cancer. In particular, we focus on how the sample size, distribution of subtypes and sample heterogeneity affect the performance.Results: In general, increasing the sample size had limited effect on the clustering performance, e.g. for the breast cancer data similar performance was obtained for n = 40 as for n = 330. The relative distribution of the subtypes had a noticeable effect on the ability to identify the disease subtypes and data with disproportionate cluster sizes turned out to be difficult to cluster. Both the choice of clustering method and selection method affected the ability to identify the subtypes, but the relative performance varied between data sets, making it difficult to rank the approaches. For some data sets, the performance was substantially higher when the clustering was based on data from only one sex compared to data from a mixed population. This suggests that homogeneous data are easier to cluster than heterogeneous data and that clustering males and females individually may be beneficial and increase the chance to detect novel subtypes. It was also observed that the performance often differed substantially between females and males.Conclusions: The number of samples seems to have a limited effect on the performance while the heterogeneity, at least with respect to sex, is important for the performance. Hence, by analyzing the genders separately, the possible loss caused by having fewer samples could be outweighed by the benefit of a more homogeneous data.
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