Comparing Mixed Reality Agent Representations: Studies in the Lab and in the Wild

Mixed-reality systems provide a number of different ways of representing users to each other in collaborative scenarios. There is an obvious tension between using media such as video for remote users compared to representations as avatars. This paper includes two experiments (total n = 80) on user trust when exposed to two of three different user representations in an immersive virtual reality environment that also acts as a simulation of typical augmented reality simulations: full body video, head and shoulder video and an animated 3D model. These representations acted as advisors in a trivia quiz. By evaluating trust through advisor selection and self-report, we found only minor differences between representations, but a strong effect of perceived advisor expertise. Unlike prior work, we did not find the 3D model scored poorly on trust, perhaps as a result of greater congruence within an immersive context

Lessons learnt running distributed and remote mixed reality experiments

One traditional model of research on mixed-reality systems, is the laboratory-based experiment where a number of small variants of a user experience are presented to participants under the guidance of an experimenter. This type of experiment can give reliable and generalisable results, but there are arguments for running experiments that are distributed and remote from the laboratory. These include, expanding the participant pool, reaching specific classes of user, access to a variety of equipment, and simply because laboratories might be inaccessible. However, running experiments out of the laboratory brings a different set of issues into consideration. Here, we present some lessons learnt in running eleven distributed and remote mixed-reality experiments. We describe opportunities and challenges of this type of experiment as well as some technical lessons learnt

LDEncoder: Reference deep learning-based feature detector for transfer learning in the field of epigenomics

We propose a reference feature extractor that can be used for methylation data and potentially other epigenomic data sources. In doing so, it can be used in a trans-omics manner to bridge between epigenomics and transcriptomics. By having an internal latent space, it can solve classification/regression problems in a trans-omics manner. DNA methylation data is part of epigenomics data that is altered by external factors including the change in environment. It has multiple roles including the regulation of gene expression. The goal of the reference feature extractor is to extract important features from the DNA methylation data while encoding the features in a low dimensional feature space. To achieve this, a pan-cancer dataset was used to train the model with a wide variety of data. Due to the low dimensional encoding, downstream tasks can be solved while utilising significantly fewer parameters. The current state-of-the-art can work with a trans-omics setting, but it was not able to generalise the model so that it could work in other settings [1--3]. For example, TDImpute [4] needed an extra decision-making model to complete the classification task, while not utilising the latent feature representation inferred inside the model. Furthermore, a multi-layer perceptron, called LDEncoder, used in this approach has a low encoding dimension (512), which is used to represent the high dimensional DNA methylation data in a significantly lower-dimensional feature space. So, if the new classification/regression problem needs to be solved, the input dimension of 512 can be used for the transfer learning of the model. This significantly reduces the amount of time and computational resources needed for solving problems. In effect, transforming the DNA methylation data to gene expression data (RNA-seq) while having a bottleneck enables the lower dimensional encoding of the data. Also, in a similar scenario, we evaluated the performance of various models and techniques inspired by successful ones in computer vision. These included incorporating the model parameter savers based on the best validation loss and CpG site sorting1. We found some promising results as shown in Table 1. Also, we further evaluate the generalisability of the model through cancer/non-cancer prediction and breast cancer molecular subtype prediction results

Sex differences in oncogenic mutational processes

Funder: Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (NSERC Canadian Network for Research and Innovation in Machining Technology); doi: https://doi.org/10.13039/501100002790Funder: Genome Canada (Génome Canada); doi: https://doi.org/10.13039/100008762Funder: Canada Foundation for Innovation (Fondation canadienne pour l'innovation); doi: https://doi.org/10.13039/501100000196Funder: Terry Fox Research Institute (Institut de Recherche Terry Fox); doi: https://doi.org/10.13039/501100004376Abstract: Sex differences have been observed in multiple facets of cancer epidemiology, treatment and biology, and in most cancers outside the sex organs. Efforts to link these clinical differences to specific molecular features have focused on somatic mutations within the coding regions of the genome. Here we report a pan-cancer analysis of sex differences in whole genomes of 1983 tumours of 28 subtypes as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We both confirm the results of exome studies, and also uncover previously undescribed sex differences. These include sex-biases in coding and non-coding cancer drivers, mutation prevalence and strikingly, in mutational signatures related to underlying mutational processes. These results underline the pervasiveness of molecular sex differences and strengthen the call for increased consideration of sex in molecular cancer research

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Sex differences in oncogenic mutational processes

Sex differences have been observed in multiple facets of cancer epidemiology, treatment and biology, and in most cancers outside the sex organs. Efforts to link these clinical differences to specific molecular features have focused on somatic mutations within the coding regions of the genome. Here we report a pan-cancer analysis of sex differences in whole genomes of 1983 tumours of 28 subtypes as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We both confirm the results of exome studies, and also uncover previously undescribed sex differences. These include sex-biases in coding and non-coding cancer drivers, mutation prevalence and strikingly, in mutational signatures related to underlying mutational processes. These results underline the pervasiveness of molecular sex differences and strengthen the call for increased consideration of sex in molecular cancer research.Sex differences have been observed in multiple facets of cancer epidemiology, treatment and biology, and in most cancers outside the sex organs. Efforts to link these clinical differences to specific molecular features have focused on somatic mutations within the coding regions of the genome. Here we report a pan-cancer analysis of sex differences in whole genomes of 1983 tumours of 28 subtypes as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We both confirm the results of exome studies, and also uncover previously undescribed sex differences. These include sex-biases in coding and non-coding cancer drivers, mutation prevalence and strikingly, in mutational signatures related to underlying mutational processes. These results underline the pervasiveness of molecular sex differences and strengthen the call for increased consideration of sex in molecular cancer research.Peer reviewe

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that -80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAFPeer reviewe

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts