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Tommye Hope
In English, the term happi is most often translated as "happi coat" or "happy coat". Happi are typically blue, with designs in red, black, and white, though variations with a number of different colours are also seen in modern day Japan. Modern happi coats may be made of cotton or polyester fabrics.
What are the deliverables of this project?
The Happy Pinecrest People Initiative (HaPPI) is a year-long pilot exercise with multiple phases. Focus groups will help statisticians develop a survey that will be administered to the community in the coming months to quantify levels of happiness and to identify opportunities (programs, etc) for improving the quality of life in Pinecrest. We later hope to measure success by administering future surveys.
What is the World Happiness Foundation?
It is a non-profit organization with the mission to build the capabilities of individuals, communities, organizations, and governments to help accelerate progress toward enhancing happiness and well-being for all people.
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At Drunk Elephant, we believe there are six culprits that are at the root of almost all skin problems. If you take a break from the Suspicious 6, your skin can reset to its happiest, healthiest state.
Plant-derived biodegradable scrub agent that is perfectly spherical and provides a gentle physical exfoliation to sweep away the buildup of dead cells and other substances from the scalp. Cellulose acetate is an ideal alternative to environmentally problematic polyethylene beads, too-soft jojoba beads, and too-abrasive crushed nut shells.
Recovering metagenome-assembled genomes (MAGs) from shotgun sequencing data is an increasingly common task in microbiome studies, as MAGs provide deeper insight into the functional potential of both culturable and non-culturable microorganisms. However, metagenome-assembled genomes vary in quality and may contain omissions and contamination. These errors present challenges for detecting genes and comparing gene enrichment across sample types. To address this, we propose happi, an approach to testing hypotheses about gene enrichment that accounts for genome quality. We illustrate the advantages of happi over existing approaches using published Saccharibacteria MAGs, Streptococcus thermophilus MAGs, and via simulation.
While cultivation-based studies have historically been used to study the gene content of bacteria, it has become increasingly common to employ shotgun metagenomics to study bacterial genomes and communities. Shotgun metagenomic sequencing involves untargeted sequencing of all DNA in an environment, enabling the study of genomes in their environmental context. Short reads from shotgun sequencing can be assembled into contigs and binned into metagenome-assembled genomes (MAGs), which represent a partial reconstruction of an individual bacterial genome. Despite major advances in methods for binning MAGs, MAGs can contain two types of errors. First, there can be genes that are truly present in the genome the MAG represents but are unobserved in a MAG. Common reasons for this error include inadequate sequencing depth, high diversity in the metagenomes under study, and the inherent limitations of short read sequencing for reconstructing repetitive regions [8,9,10,11,12]. A second type of error in MAGs is erroneously observed genes: genes that are included in a MAG that are not truly present in the originating genome. This phenomenon is often referred to as contamination. The use of automated binning tools in the absence of manual inspection and refinement can lead to elevated rates of contamination. For example, the identification of contaminating contigs from manual refinement of MAGs produced by a massive unsupervised genome reconstruction effort removed 30 putative functions from a single contaminated genome [13, 14].
where \(Y_i\) are conditionally independent Bernoulli distribution random variables; \(\varepsilon\) is the probability that a gene is observed in a genome in which it is absent (e.g., due to contamination or crosstalk); \(M_i \in \mathbb R^q\) is a vector of genome quality covariates; and \(f(\cdot ) :\mathbb R^q \rightarrow \mathbb R\) is a flexible function to connect quality variables to the probability of detecting a present gene. Relevant quality variables are context-dependent and could include coverage of the gene from metagenomic read recruitment, completion (percentage of single copy core genes observed in the genome), redundancy (percentage of single copy core genes observed more than once in the genome), and an indicator for the genome originating from an isolated bacterial population.
The latent variable structure of our model makes the expectation-maximization algorithm [15] an appealing choice for estimating unknown parameters \(\theta = \left( \beta , f\right)\). Because we do not observe \(\\lambda _i\_i=1^n\), \(\varepsilon\) and f are not, in general, jointly identifiable. Therefore, we treat \(\varepsilon\) as a hyperparameter that can be fixed by the user or leveraged for sensitivity analyses. To improve stability of parameter estimates, we impose a Firth-type penalty on \(\beta\). The complete data penalized log-likelihood is linear in \(\lambda _i\), which allows us to simplify the expected complete data penalized log-likelihood at step t of an EM iteration as
Different methods identified different differentially present genes. Out of 713 COG functions tested, happi identified 176 differentially present genes when controlling false discovery rate (FDR) at the 5% level; GLM-LRT identified 219 genes; and GLM-Rao identified 175 genes. Out of the 176 genes identified as differentially present by happi, all 176 genes were also identified by GLM-LRT as differentially present and 166 genes were identified by GLM-Rao as differentially present.
To investigate the biological plausibility of the results from each method, we assessed the number of core genes that were identified as differentially present. [18] identified 172 COG functions in the TM7 core genome, and because core genes are genes that are present in most genomes of a particular clade, we consider differentially present core genes to be false positives. Controlling FDR at 5%, happi identified 6 out of 172 core genes to be differentially present; GLM-LRT identified 10 genes; and GLM-Rao identified 7 genes. While this difference is not substantial, we consider this reduction in the number of false positives to be an advantage of happi.
Our proposed method calculated lower p-values for 16% and 29% of genera compared to GLM-LRT and GLM-Rao. We show results from 6 specific model estimates in Fig. 1: 3 core genes for which happi produced greater p-values than GLM-Rao (upper panels; we believe these signals to be truly null), and 3 accessory genes for which it produced smaller p-values than GLM-Rao (lower panels). In all instances where happi produced greater p-values than GLM-Rao, non-detections generally occurred in genomes with low mean coverage. GLM-Rao does not account for coverage information, and so unlike happi, it can conflate gene absence with non-detections due to quality. We believe that statements about significance should be moderated when detection patterns can be attributable to quality variables and therefore that it is reasonable that p-values are larger in these three cases. In contrast, happi produced smaller p-values than GLM-Rao in instances when non-detections occurred for greater coverage MAGs, or broadly across the range of MAG coverage (lower panels). In these instances, differences in detection are less likely to be attributable to quality factors, and it is reasonable that the significance of findings can be strengthened by including data on quality variables.
We test the null hypothesis that the probability that a gene is present are equal for tongue and plaque-associated Saccharibacteria genomes. The top 3 panels show core genes for which our proposed method resulted in greater p-values than existing methods, and the lower 3 panels show accessory genes for which our proposed method resulted in smaller p-values than existing methods. Our method reduced p-values when differences in detection cannot be attributed to genome quality factors (here, coverage), and increased p-values in situations when non-detection may be conflated with lower quality genomes. Points have been jittered vertically to separate observations
We also investigated the sensitivity of the results of happi to different choices of \(\varepsilon\), the probability of observing a gene given that it is truly absent (Additional file 1: S2). For specific genes of interest, we encourage users to investigate plausible levels of \(\varepsilon\) to confirm the robustness of their results to this hyperparameter. In general, we recommend choosing \(\varepsilon\) based on genome redundancy metrices or based on other tuning parameters for MAG construction. We discourage further exploration of genes whose significantly differential presence hinges on the assumption of low genome contamination levels and is not robust across small increases in \(\varepsilon\).
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