Purity Vst Crack Download
Mozelle Towers
Guided by appraisal-based models of the influence of emotion upon judgment, we propose that disgust moralizes--that is, amplifies the moral significance of--protecting the purity of the body and soul. Three studies documented that state and trait disgust, but not other negative emotions, moralize the purity moral domain but not the moral domains of justice or harm/care. In Study 1, integral feelings of disgust, but not integral anger, predicted stronger moral condemnation of behaviors violating purity. In Study 2, experimentally induced disgust, compared with induced sadness, increased condemnation of behaviors violating purity and increased approval of behaviors upholding purity. In Study 3, trait disgust, but not trait anger or trait fear, predicted stronger condemnation of purity violations and greater approval of behaviors upholding purity. We found that, confirming the domain specificity of the disgust-purity association, disgust was unrelated to moral judgments about justice (Studies 1 and 2) or harm/care (Study 3). Finally, across studies, individuals of lower socioeconomic status (SES) were more likely than individuals of higher SES to moralize purity but not justice or harm/care.
Fineness is another way of expressing the precious metal content of gold jewellery, and represents the purity in parts per thousand. When stamped on jewellery, usually this is stated without the decimal point.
Tumour purity is the proportion of cancer cells in the admixture. Until recently, it was estimated by a pathologist, primarily by visual or image analysis of tumour cells. With the advancement of genomic technologies, many new computational methods have arisen to infer tumour purity. These methods make estimates using different types of genomic information, such as gene expression6, somatic copy-number variation7,8,9 somatic mutations7,10 and DNA methylation7,11. Estimates made by these methods are generally consistent with one another, though, to date, no systematic sensitivity analysis in multiple cancer types has been performed.
The Cancer Genome Atlas (TCGA) is currently the largest available data set for genomic analysis of tumours. It contains over 10,000 pretreatment samples across 30 cancer types and includes measurements such as RNA sequencing (RNA-seq), DNA methylation, copy-number variation and more12. The consortium had originally set a quality threshold that tumour samples included in the cohort be composed of at least 80% tumour nuclei, as determined by visual analysis13. However, this threshold was later reduced to 60%. Given the status of TCGA as a flagship project of the National Cancer Institute, we assumed that sample purity was the best possible using current conventional sample acquisition methods, and we thus hypothesized that differences in purity were due more to properties of the cancers, and less to the acquisition method. While TCGA argues that 60% purity is sufficient to distinguish the tumour's signal from those of other cells, it remains to be evaluated if this level of purity across tumour samples affects the interpretation of genomic analyses.
In recent years, sporadic analyses have sought to determine tumour purity levels and take them into account during analysis14,15,16,17,18,19,20,21. These studies used different purity estimation methods and tested only specific parameters, which were mainly in the context of detecting somatic mutations22.
(a) Pairwise correlations between tumour purity methods used in 21 cancer types and all samples combined. Grey cells: data not available from both purity methods. Correlations between the genomic-based methods are high in most cancer types. Correlations with IHC are low, yet always positive. (b) Violin plots of CPE tumour purity in 21 cancer types. The cancers were ordered according to median purity.
Next, we determined the average purity level of all associated tumour samples for each cancer type, according to each method. In accordance with previous results, there was high concordance among the DNA, RNA and methylation-based methods, and lower to no agreement with IHC (Supplementary Fig. 4). Average tumour purity across all samples from all cancer types was 81.113.9%, 76.116.1% and 75.721.2% (means.d.) for ESTIMATE, LUMP and IHC, respectively. An exception was ABSOLUTE, with an average estimate of 62.319.9%. This difference is explained by methodological differences: while ABSOLUTE is a direct measure of the tumour cells in a sample, ESTIMATE and LUMP estimate purity indirectly by measuring immune and stromal counterparts in the sample. Thus, the difference in the average estimates is arguably due to the presence of non-immune and stromal cells in a sample, such as contaminating adjacent normal cells, which are not measured by ESTIMATE and LUMP.
Scatter plot of median number of mutations per tumour sample for each of the 21 cancer types (log 10 scale) versus median tumour purity as calculated by CPE. Pearson coefficient is presented. The least-squares line presented was calculated without the five outliers coloured in lighter blue.
Correlative analyses are widely applied to genomics in the study of cancer. One key approach is the gene co-expression network, which assigns a score to a pair of genes based on their co-expression frequencies in different samples. Co-expression networks have been used extensively in cancer studies, with an aim to unravel hallmark pathways and prioritize novel candidate genes26. We found that identifying co-expression networks from genomics data without accounting for tumour purity is problematic. Gene expression profiles from bladder carcinoma illustrate the problem. For example, expression levels of colony-stimulating factor 1 receptor (CSF1R) and Janus kinase 3 (JAK3), tyrosine protein kinases and known cancer-driver genes27, are highly correlated with each other (Spearman correlation R=0.67, P
(a) The problem of co-expression without accounting for sample purity. Top panel: correlation of expression between colony-stimulating factor 1 receptor (CSF1R) and Janus kinase 3 (JAK3) in bladder urothelial carcinoma (BLCA). Lower panels: high linear correlation between tumour purity and expression of those genes. The y axis is in log scale, and the fitted line is in log scale accordingly. There is no known interaction between these genes. (b) Co-expression matrix of top 5,000 genes according to gene expression s.d. Cell(i,j) is the Spearman coefficient between expression of gene i and gene j. Genes were clustered according to the Euclidean distance between them. Bottom vector: coloured by the correlation of the genes with purity. The four major clusters are boxed; average correlation of genes with purity in the cluster is shown. Group A is highly enriched with genes negatively correlated with purity. Group C has only genes positively correlated with purity. (c) Scatter plot of co-expression correlations (x axis) versus partial correlation of co-expression controlling for CPE purity levels (y axis) in all 21 cancer types. Analysis was restricted to the top 1,000 genes according to gene expression standard deviation in each cancer type, and the plot shows correlations with a Spearman coefficient >0.5. The colours correspond to the multiplication of the correlation of the co-expressed genes with purity. (d) Scatter plot of the difference in correlation between regular co-expression and purity controlled pair of genes (x axis) versus the pairwise multiplication of the co-expressed genes with purity. Red line: kernel smoothing regression of the data.
We extended this observation to all available gene pairs. Strikingly, we found that the strongest gene networks, that is, groups of genes with correlated expression profiles, were composed of genes highly associated with purity (Fig. 4b; Supplementary Fig. 9). Group A, which contains 25.7% of the genes, was enriched with 60.0% of all co-expressing gene pairs (Spearman coefficient R>0.5), but also with genes negatively correlated with purity (91.1% of genes with R0.3), compared with an expected ratio of only 0.6%. As expected, the group A gene ontology annotations were enriched with terms related to the immune system, but also with other terms such as extracellular matrix organization and other cellular functions (Supplementary Table 3). Group C, on the other hand, contained only genes positively correlated with purity. Those genes did not seem to share specific gene ontology annotations. While genes in both groups may be part of a shared co-expression network, the above analysis demonstrates that a correlation between them may be explained in large part by tumour purity. We attempted to address this bias by applying partial correlations with controlling for tumour purity in the co-expression analysis. The number of pairwise co-expressions in bladder carcinoma decreased by 39.7%, and the fraction of co-expressions between purity-associated genes decreased by 58.4%. Overall in all 21 cancer types, we observed a 21.0% decrease in the number of pairwise co-expressions when controlling for purity (Fig. 4c; Supplementary Fig. 10), and a decrease of 48.7% of co-expressions when both genes are correlated with purity. This decrease was tightly correlated with the pairwise correlation of the genes with purity (defined as the multiplication of the coefficients of the correlation of expression with purity between the co-expressing genes). For every 0.1 increase in the level of pairwise correlation with purity, we observed a 0.1 correlation decrease (Fig. 4d). We concluded that naive correlation between genomic profiling measures gives results that are highly confounded by tumour purity. We suggest that future co-expression analyses should employ partial correlation analysis by adjusting for tumour purity.
We used the DESeq2 package36 to apply DE analysis to RNA-seq counts of tumour and normal samples across a dozen cancer types with sufficient normal tissue for sampling. We compared our findings with a DE analysis designed to include purity estimates, which is equivalent to adjusting gene expression by purity. This comparison found numerous marked differences in relative expression levels. Many genes were differentially expressed before purity adjustment, but no differences between cancer samples and controls were seen after adjustment. Some genes even changed state from up- to downregulation or the other way around. Most importantly, we found differentially expressed genes after adjustment that had not been identified before. Figure 6b illustrates expression patterns of the immunotherapy target cytotoxic T-lymphocyte-associated protein 4 (CTLA4) and its ligand, CD86 (also known as B7.2) in traditional and adjusted DE analyses in three cancer types. Standard DE analysis labelled both genes as highly upregulated in KIRC samples. However, most of the difference from healthy samples could be ascribed to differences in purity. In LUAD, on the other hand, CTLA4 was detected as upregulated only after accounting for purity, while the downregulation of CD86 was again a byproduct of purity. In THCA, this trend was reversed: CTLA4 seemed downregulated, until DE adjustment, while CD86 was only detected as upregulated after adjustment.
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