TY - GEN A1 - Repsilber, Dirk A1 - Kern, Sabine A1 - Telaar, Anna A1 - Walzl, Gerhard A1 - Black, Gillian F. A1 - Selbig, Joachim A1 - Parida, Shreemanta K. A1 - Kaufmann, Stefan H. E. A1 - Jacobsen, Marc T1 - Biomarker discovery in heterogeneous tissue samples BT - taking the in-silico deconfounding approach T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Background: For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues. Results: Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach. Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available. Conclusions: The deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in realistically noisy conditions and with moderate sample sizes. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 854 KW - differential gene expression KW - quantile normalization KW - heterogeneous tissue KW - gene expression matrix KW - homogeneous cell population KW - selection KW - microdissection Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-429343 SN - 1866-8372 IS - 854 ER - TY - JOUR A1 - Vasishth, Shravan A1 - Suckow, Katja A1 - Lewis, Richard L. A1 - Kern, Sabine T1 - Short-term forgetting in sentence comprehension : crosslinguistic evidence from verb-final structures N2 - Seven experiments using self-paced reading and eyetracking suggest that omitting the middle verb in a double centre embedding leads to easier processing in English but leads to greater difficulty in German. One commonly accepted explanation for the English pattern-based on data from offline acceptability ratings and due to Gibson and Thomas (1999)- is that working-memory overload leads the comprehender to forget the prediction of the upcoming verb phrase (VP), which reduces working-memory load. We show that this VP-forgetting hypothesis does an excellent job of explaining the English data, but cannot account for the German results. We argue that the English and German results can be explained by the parser's adaptation to the grammatical properties of the languages; in contrast to English, German subordinate clauses always have the verb in clause-final position, and this property of German may lead the German parser to maintain predictions of upcoming VPs more robustly compared to English. The evidence thus argues against language- independent forgetting effects in online sentence processing; working-memory constraints can be conditioned by countervailing influences deriving from grammatical properties of the language under study. Y1 - 2010 UR - http://www.informaworld.com/0169-0965 U6 - https://doi.org/10.1080/01690960903310587 SN - 0169-0965 ER -