Testing for Unit Roots in Nonlinear Dynamic Heterogeneous Panels

In this paper we present a unit root test against a nonlinear dynamic heterogenous panel with each cross section modelled as an LSTAR model. All parameters are viewed as cross section specific. We allow for serially correlated residuals over time and heterogenous variance among cross sections. The test is derived under three special cases: (i) the number of cross sections and observations over time are fixed, (ii) observations over time are fixed and the number of cross sections tend to infinity, and (iii) first letting the number of observations over time tend to infinity and thereafter the number of cross sections. Small sample properties of the test show modest size distortions and satisfactory power being superior to the Im, Pesaran, and Shin t-type of test. We also show clear improvements in power compared to a univariate unit root test allowing for nonlinearities under the alternative hypothesis.Dynamic nonlinear heterogenous panels; Structural breaks; Unit roots; t-statistics; Central limit theorem;

Testing Parameter Constancy in Unit Root Autoregressive Models Against Continuous Change

In this paper we derive tests for parameter constancy when the data generating process is non-stationary against the hypothesis that the parameters of the model change smoothly over time. To obtain the asymptotic distributions of the tests we generalize many theoretical results, as well as new are introduced, in the area of unit roots. The results are derived under the assumption that the error term is a strong mixing. Small sample properties of the tests are investigated, and in particular, the power performances are satisfactory.Parameter constancy; LSTAR; Unit root; Brownian; motion; Strong mixing;

Dickey-Fuller Type of Tests against Nonlinear Dynamic Models

In this paper we introduce several test statistics of testing the null hypotheses of a random walk (with or without drift) against models that accommodate a smooth nonlinear shift in the level, the dynamic structure, and the trend. We derive analytical limiting distributions for all tests. Finite sample properties are examined. The performance of the tests is compared to that of the classical unit root tests by Dickey-Fuller and Phillips and Perron, and is found to be superior in terms of power.Dickey-Fuller test; LSTAR(p); LSTART(p); Nonlinear trends; Parameter constancy; Unit root; Brownian motion;

Inference for Unit Roots in a Panel Smooth Transition Autoregressive Model where the Time Dimension is Fixed

In this paper we derive a unit root test against a Panel Logistic Smooth Transition Autoregressive (PLSTAR). The analysis is concentrated on the case where the time dimension is fixed and the cross section dimension tends to infinity. Under the null hypothesis of a unit root, we show that the LSDV estimator of the autoregressive parameter in the linear component of the model is inconsistent due to the inclusion of fixed effects. The test statistic, adjusted for the inconsistency, has an asymptotic normal distribution whose first two moments are calculated analytically. To complete the analysis, finite sample properties of the test are examined. We highlight scenarios under which the traditional panel unit root tests by Harris and Tzavalis have inferior or reasonable power compared to our test.Dynamic nonlinear panel; Smooth transitions; Structural breaks; Unit roots; LSDV estimation; Central limit theorem;

The molecular portrait of in vitro growth by meta-analysis of gene-expression profiles

BACKGROUND: Cell lines as model systems of tumors and tissues are essential in molecular biology, although they only approximate the properties of in vivo cells in tissues. Cell lines have been selected under in vitro conditions for a long period of time, affecting many specific cellular pathways and processes. RESULTS: To identify the transcriptional changes caused by long term in vitro selection, we performed a gene-expression meta-analysis and compared 60 tumor cell lines (of nine tissue origins) to 135 human tissue and 176 tumor tissue samples. Using significance analysis of microarrays we demonstrated that cell lines showed statistically significant differential expression of approximately 30% of the approximately 7,000 genes investigated compared to the tissues. Most of the differences were associated with the higher proliferation rate and the disrupted tissue organization in vitro. Thus, genes involved in cell-cycle progression, macromolecule processing and turnover, and energy metabolism were upregulated in cell lines, whereas cell adhesion molecules and membrane signaling proteins were downregulated. CONCLUSION: Detailed molecular understanding of how cells adapt to the in vitro environment is important, as it will both increase our understanding of tissue organization and result in a refined molecular portrait of proliferation. It will further indicate when to use immortalized cell lines, or when it is necessary to instead use three-dimensional cultures, primary cell cultures or tissue biopsies

Automatic robust estimation for exponential smoothing: Perspectives from statistics and machine learning

A major challenge in automating the production of a large number of forecasts, as often required in many business applications, is the need for robust and reliable predictions. Increased noise, outliers and structural changes in the series, all too common in practice, can severely affect the quality of forecasting. We investigate ways to increase the reliability of exponential smoothing forecasts, the most widely used family of forecasting models in business forecasting. We consider two alternative sets of approaches, one stemming from statistics and one from machine learning. To this end, we adapt M-estimators, boosting and inverse boosting to parameter estimation for exponential smoothing. We propose appropriate modifications that are necessary for time series forecasting while aiming to obtain scalable algorithms. We evaluate the various estimation methods using multiple real datasets and find that several approaches outperform the widely used maximum likelihood estimation. The novelty of this work lies in (1) demonstrating the usefulness of M-estimators, (2) and of inverse boosting, which outperforms standard boosting approaches, and (3) a comparative look at statistics versus machine learning inspired approaches

Progression of RNA-sequencing to single-cell applications

New methods enable new discoveries. My time as a PhD student has run in parallel with the maturation of the RNA-seq method, and I have used it to discover basic properties of gene expression and transcriptomes. My part has been bioinformatics – the computer analysis of biological data. RNA-seq quantifies gene expression for all genes in one experiment, allowing discoveries without prior knowledge, as opposed to single-gene hypothesis testing. When I started my PhD, this was done by microarray followed by qRT-PCR validation, which can be arduous. In contrast to microarrays, RNA-seq quantifies expression with little ambiguity of which gene each expression value corresponds to, and in absolute terms. But at the time, data analysis of RNA-seq was full of unknowns and there were little software available. Nowadays, partly the result of my work, the data analysis is much less complicated, and RNA-seq can be performed on diminutive samples, down to single cells, which was not viable using microarrays. My first study (Paper I) used one of the very first RNA-seq datasets to study general features of transcriptomes, such as mean mRNA length (~1,500 nt) and the number of genes expressed per tissue (~13,000). I also found special features of some tissues: the liver transcriptome is dominated by a few highly expressed gene, brain expresses especially long mRNAs and testis expresses many more genes than other tissues. Following this tissue RNA-seq study, I evaluated a new library preparation method for single-cell RNA-seq (Paper III), developed before the prevalence of single-cell RNA-seq. I used technical replicates to show that the method was accurate and reliable for the more highly expressed genes at single-cell RNA levels, and with input RNA amounts corresponding to >50 cells it produced as good quality data as bulk RNA-seq. Then the method was applied on melanoma cells isolated from human blood, and I listed surface antigen genes that distinguished these circulating tumour cells from other cells in the blood. This single-cell RNA-seq method was then applied on pre-implantation embryo cells (Paper IV). Using first-generation crosses between two mouse strains, I could separate the expression from the maternal and the paternal copies of the genes. I found that 12-24% of the genes express only one of their two copies in any given cell, in a random manner that affects almost all the expressed genes. I also found that the two copies are expressed independently from each other. Finally, I studied Sox transcription factors during neural development (Paper II), combining RNA-seq and microarray data for different cell types with ChIP-seq data for transcription factor binding and histone modifications. I found that Sox proteins bind to the enhancers active in the stem cells where the Sox proteins are active, but also to enhancers specific to subsequent cells in ii development. I also found that different Sox factors bind to much the same enhancers, and that they can induce histone modifications. In conclusion, my work has advanced the RNA-seq method and increased the understanding of transcriptional regulation and output

X-chromosome upregulation is driven by increased burst frequency

Ohno's hypothesis postulates that X-chromosome upregulation rectifies X-dose imbalance relative to autosomal genes, present in two active copies per cell. Here we dissected X-upregulation into kinetics of transcription, inferred from allele-specific single-cell RNA-sequencing (scRNAseq) data from somatic mouse cells. We confirmed increased X-chromosome expression in female and male somatic cells, and remarkably found that the X-chromosome achieved upregulation by elevated burst frequencies. By monitoring differentiating female embryonic stem cells, we found that elevated burst frequency established on the active X-chromosome as X-inactivation occurred on the other allele. This provides mechanistic insights into X-chromosome upregulation.Ragnar Söderberg Foundation (M16/17)Swedish Research Council (2017-01062)Swedish Research Council (2017-01723)European Research Council (648842)Accepte

A DNA-nanoassembly-based approach to map membrane protein nanoenvironments

Most proteins at the plasma membrane are not uniformly distributed but localize to dynamic domains of nanoscale dimensions. To investigate their functional relevance, there is a need for methods that enable comprehensive analysis of the compositions and spatial organizations of membrane protein nanodomains in cell populations. Here we describe the development of a non-microscopy based method for ensemble analysis of membrane protein nanodomains. The method, termed NANOscale DEciphEring of membrane Protein nanodomains (NanoDeep), is based on the use of DNA nanoassemblies to translate membrane protein organization information into a DNA sequencing readout. Using NanoDeep, we characterised the nanoenvironments of Her2, a membrane receptor of critical relevance in cancer. Importantly, we were able to modulate by design the inventory of proteins analysed by NanoDeep. NanoDeep has the potential to provide new insights into the roles of the composition and spatial organization of protein nanoenvironments in the regulation of membrane protein function.EC Seventh Framework Programme FP7 (617711/EC)European Research Council (FP7-IDEAS-ERC)Knut and Alice Wallenberg Foundation (KAW 2017.0114)Swedish Research Council (2015-03520)Accepte