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We develop a cross-platform open-source Java application (BACOM2) with graphic user interface (GUI), and users also can use a XML file to set the parameters of algorithm model, file paths and the dataset of paired samples. BACOM2 implements the new entire pipeline of copy number change analysis for heterogeneous cancer tissues, including extraction of raw copy number signals from CEL files of paired samples, attenuation correction, identification of balanced AB-genotype loci, copy number detection and segmentation, global baseline calculation and absolute normalization, differentiation of deletion types, estimation of the normal tissue fraction and correction of normal tissue contamination. BACOM2 focuses on the common tools for data preparation and absolute normalization for copy number analysis of heterogeneous cancer tissues. The software provides an additional choice for scientists who require a user-friendly, high-speed processing, cross-platform computing environment for large copy number data analysis., Comment: 9 pages, 6 figures

BACOM is a statistically principled and unsupervised method that detects copy number deletion types (homozygous versus heterozygous), estimates normal cell fraction, and recovers cancer specific copy number profiles, using allele specific copy number signals. In a subsequent analysis of TCGA ovarian cancer dataset, the average normal cell fraction estimated by BACOM was found higher than expected. In this letter, we first discuss the advantages of the BACOM in relation to alternative approaches. Then, we show that this elevated estimate of normal cell fraction is the combined result of inaccurate signal modeling and normalization. Lastly, we describe an allele specific signal modeling and normalization scheme that can enhance BACOM applications in many biological contexts. An open source MATLAB program was developed to implement our extended method and it is publically available., Comment: 5 pages

Tissue heterogeneity is a major confounding factor in studying individual populations that cannot be resolved directly by global profiling. Experimental solutions to mitigate tissue heterogeneity are expensive, time consuming, inapplicable to existing data, and may alter the original gene expression patterns. Here we ask whether it is possible to deconvolute two-source mixed expressions (estimating both proportions and cell-specific profiles) from two or more heterogeneous samples without requiring any prior knowledge. Supported by a well-grounded mathematical framework, we argue that both constituent proportions and cell-specific expressions can be estimated in a completely unsupervised mode when cell-specific marker genes exist, which do not have to be known a priori, for each of constituent cell types. We demonstrate the performance of unsupervised deconvolution on both simulation and real gene expression data, together with perspective discussions., Comment: 5 pages, 5 figures, 3 tables

A three-stage developmental model of the socialization of altruism as a reinforcer was tested. The model posits mood as a mediator of helping behavior. The first stage of the model is characterized by a relatively low level of altruistic responding, regardless of mood state, among preschool children; the second by the acquisition, among primary-school-age children, of norms for social behavior; the third by the internalization of reward among adolescents. Following the induction of either a sad or a neutral mood, 192 children in three school-age groups were given an opportunity to share under either a public or private sharing condition. Age groups consisted of kindergarten childen and 1st-grade students, 4th- and 5th-grade students, and 9th- and 10th-grade students. Unlike other studies, a second mood check immediately followed the sharing opportunity to test the effects of sharing on mood. Despite the effectiveness of the mood induction and strong age effects, support was not found for the hypothesis that the sharing condition affects sharing behavior differentially across age. In sum, results provide weak support for the notion that moods can affect helping, but no support for the notion that helping relieves negative mood states. The discussion presents a conceptual analysis of the three-stage model and focuses on suggestions for future mood-helping research. (Author/RH)