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Warren Buffett's ground rules : words of wisdom from the partnership letters of the world's greatest investor
\"Using the letters Warren Buffett wrote to his partners between 1956 and 1970, a veteran financial advisor presents the renowned guru's \"ground rules\" for investing--guidelines that remain startlingly relevant today. In the fourteen years between his time in New York with value-investing guru Benjamin Graham and his start as chairman of Berkshire Hathaway, Warren Buffett managed Buffett Partnership Limited, his first professional investing partnership. Over the course of that time--a period in which he experienced an unprecedented record of success--Buffett wrote semiannual letters to his small but growing group of partners, sharing his thoughts, approaches, and reflections. Compiled for the first time and with Buffett's permission, the letters spotlight his contrarian diversification strategy, his almost religious celebration of compounding interest, his preference for conservative rather than conventional decision making, and his goal and tactics for bettering market results by at least 10% annually. Demonstrating Buffett's intellectual rigor, they provide a framework to the craft of investing that had not existed before: Buffett built upon the quantitative contributions made by his famous teacher, Benjamin Graham, demonstrating how they could be applied and improved. Jeremy Miller reveals how these letters offer us a rare look into Buffett's mind and offer accessible lessons in control and discipline--effective in bull and bear markets alike, and in all types of investing climates--that are the bedrock of his success. Warren Buffett's Ground Rules paints a portrait of the sage as a young investor during a time when he developed the long-term value-oriented strategy that helped him build the foundation of his wealth--rules for success every investor needs today\"-- Provided by publisher.
BOOK
Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways
Because mouse models play a crucial role in biomedical research related to the human nervous system, understanding the similarities and differences between mouse and human brain is of fundamental importance. Studies comparing transcription in human and mouse have come to varied conclusions, in part because of their relatively small sample sizes or underpowered methodologies. To better characterize gene expression differences between mouse and human, we took a systems-biology approach by using weighted gene coexpression network analysis on more than 1,000 microarrays from brain. We find that global network properties of the brain transcriptome are highly preserved between species. Furthermore, all modules of highly coexpressed genes identified in mouse were identified in human, with those related to conserved cellular functions showing the strongest between-species preservation. Modules corresponding to glial and neuronal cells were sufficiently preserved between mouse and human to permit identification of cross species cell-class marker genes. We also identify several robust human-specific modules, including one strongly correlated with measures of Alzheimer disease progression across multiple data sets, whose hubs are poorly-characterized genes likely involved in Alzheimer disease. We present multiple lines of evidence suggesting links between neurodegenerative disease and glial cell types in human, including human-specific correlation of presenilin-1 with oligodendrocyte markers, and significant enrichment for known neuro-degenerative disease genes in microglial modules. Together, this work identifies convergent and divergent pathways in mouse and human, and provides a systematic framework that will be useful for understanding the applicability of mouse models for human brain disorders.
Journal Article
Strategies for aggregating gene expression data: The collapseRows R function
Background
Genomic and other high dimensional analyses often require one to summarize multiple related variables by a single representative. This task is also variously referred to as collapsing, combining, reducing, or aggregating variables. Examples include summarizing several probe measurements corresponding to a single gene, representing the expression profiles of a co-expression module by a single expression profile, and aggregating cell-type marker information to de-convolute expression data. Several standard statistical summary techniques can be used, but network methods also provide useful alternative methods to find representatives. Currently few collapsing functions are developed and widely applied.
Results
We introduce the R function collapseRows that implements several collapsing methods and evaluate its performance in three applications. First, we study a crucial step of the meta-analysis of microarray data: the merging of independent gene expression data sets, which may have been measured on different platforms. Toward this end, we collapse multiple microarray probes for a single gene and then merge the data by gene identifier. We find that choosing the probe with the highest average expression leads to best between-study consistency. Second, we study methods for summarizing the gene expression profiles of a co-expression module. Several gene co-expression network analysis applications show that the optimal collapsing strategy depends on the analysis goal. Third, we study aggregating the information of cell type marker genes when the aim is to predict the abundance of cell types in a tissue sample based on gene expression data (\"expression deconvolution\"). We apply different collapsing methods to predict cell type abundances in peripheral human blood and in mixtures of blood cell lines. Interestingly, the most accurate prediction method involves choosing the most highly connected \"hub\" marker gene. Finally, to facilitate biological interpretation of collapsed gene lists, we introduce the function userListEnrichment, which assesses the enrichment of gene lists for known brain and blood cell type markers, and for other published biological pathways.
Conclusions
The R function collapseRows implements several standard and network-based collapsing methods. In various genomic applications we provide evidence that both types of methods are robust and biologically relevant tools.
Journal Article
Single-nucleus and single-cell transcriptomes compared in matched cortical cell types
Transcriptomic profiling of complex tissues by single-nucleus RNA-sequencing (snRNA-seq) affords some advantages over single-cell RNA-sequencing (scRNA-seq). snRNA-seq provides less biased cellular coverage, does not appear to suffer cell isolation-based transcriptional artifacts, and can be applied to archived frozen specimens. We used well-matched snRNA-seq and scRNA-seq datasets from mouse visual cortex to compare cell type detection. Although more transcripts are detected in individual whole cells (~11,000 genes) than nuclei (~7,000 genes), we demonstrate that closely related neuronal cell types can be similarly discriminated with both methods if intronic sequences are included in snRNA-seq analysis. We estimate that the nuclear proportion of total cellular mRNA varies from 20% to over 50% for large and small pyramidal neurons, respectively. Together, these results illustrate the high information content of nuclear RNA for characterization of cellular diversity in brain tissues.
Journal Article
A comparison of anatomic and cellular transcriptome structures across 40 human brain diseases
Genes associated with risk for brain disease exhibit characteristic expression patterns that reflect both anatomical and cell type relationships. Brain-wide transcriptomic patterns of disease risk genes provide a molecular-based signature, based on differential co-expression, that is often unique to that disease. Brain diseases can be compared and aggregated based on the similarity of their signatures which often associates diseases from diverse phenotypic classes. Analysis of 40 common human brain diseases identifies 5 major transcriptional patterns, representing tumor-related, neurodegenerative, psychiatric and substance abuse, and 2 mixed groups of diseases affecting basal ganglia and hypothalamus. Further, for diseases with enriched expression in cortex, single-nucleus data in the middle temporal gyrus (MTG) exhibits a cell type expression gradient separating neurodegenerative, psychiatric, and substance abuse diseases, with unique excitatory cell type expression differentiating psychiatric diseases. Through mapping of homologous cell types between mouse and human, most disease risk genes are found to act in common cell types, while having species-specific expression in those types and preserving similar phenotypic classification within species. These results describe structural and cellular transcriptomic relationships of disease risk genes in the adult brain and provide a molecular-based strategy for classifying and comparing diseases, potentially identifying novel disease relationships.
Journal Article
Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type
We describe convergent evidence from transcriptomics, morphology, and physiology for a specialized GABAergic neuron subtype in human cortex. Using unbiased single-nucleus RNA sequencing, we identify ten GABAergic interneuron subtypes with combinatorial gene signatures in human cortical layer 1 and characterize a group of human interneurons with anatomical features never described in rodents, having large ‘rosehip’-like axonal boutons and compact arborization. These rosehip cells show an immunohistochemical profile (GAD1+CCK+, CNR1–SST–CALB2–PVALB–) matching a single transcriptomically defined cell type whose specific molecular marker signature is not seen in mouse cortex. Rosehip cells in layer 1 make homotypic gap junctions, predominantly target apical dendritic shafts of layer 3 pyramidal neurons, and inhibit backpropagating pyramidal action potentials in microdomains of the dendritic tuft. These cells are therefore positioned for potent local control of distal dendritic computation in cortical pyramidal neurons.
Journal Article
RiboTag Analysis of Actively Translated mRNAs in Sertoli and Leydig Cells In Vivo
Male spermatogenesis is a complex biological process that is regulated by hormonal signals from the hypothalamus (GnRH), the pituitary gonadotropins (LH and FSH) and the testis (androgens, inhibin). The two key somatic cell types of the testis, Leydig and Sertoli cells, respond to gonadotropins and androgens and regulate the development and maturation of fertilization competent spermatozoa. Although progress has been made in the identification of specific transcripts that are translated in Sertoli and Leydig cells and their response to hormones, efforts to expand these studies have been restricted by technical hurdles. In order to address this problem we have applied an in vivo ribosome tagging strategy (RiboTag) that allows a detailed and physiologically relevant characterization of the \"translatome\" (polysome-associated mRNAs) of Leydig or Sertoli cells in vivo. Our analysis identified all previously characterized Leydig and Sertoli cell-specific markers and identified in a comprehensive manner novel markers of Leydig and Sertoli cells; the translational response of these two cell types to gonadotropins or testosterone was also investigated. Modulation of a small subset of Sertoli cell genes occurred after FSH and testosterone stimulation. However, Leydig cells responded robustly to gonadotropin deprivation and LH restoration with acute changes in polysome-associated mRNAs. These studies identified the transcription factors that are induced by LH stimulation, uncovered novel potential regulators of LH signaling and steroidogenesis, and demonstrate the effects of LH on the translational machinery in vivo in the Leydig cell.
Journal Article