Professional Ethics And Human Values By Raghavan Pdf 14
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To determine whether half-lives of orthologous genes in the mouse and human are similarly correlated, we counted the number of pairs of genes for which we had half-life measurements in both species, for each pairwise combination of genes. We then counted the number of pairs in which the mouse and human half-lives were significantly correlated (i.e., Pearson’s r > 0.7 or Spearman’s rho > 0.7) and determined the fraction of these pairs in which the correlation in the mouse was higher than that in the human. These comparisons were made within the same studies as well as between the studies. We found that the numbers were higher for the same-cell-type half-lives, as compared to a random set of half-lives (p < 10e-10), but that the frequencies were similar among different-cell-type half-lives (p > 0.01; Fig. 1h).
To determine whether the overlap between the human half-life datasets is large enough to be considered statistically significant, we calculated the Pearson correlation among them. A subset of the human half-life datasets considered above (Table 1) had sufficient overlap to be considered a single compendium (Fig. 1f). We found that the correlation between any two of these datasets was non-significant (p > 0.01) for all but two pairs, which had p > 0.05. These last two pairs were the only ones in which data from a same-cell-type measurement pair was in the same study. Furthermore, the correlation between PC1s derived from each of the 36 studies (as compared to the distribution of PC1s from a set of randomly generated half-lives) was significantly larger (p < 10e-5) than the equivalent random distribution. The same analysis was applied to a comparable compendium of mouse half-life datasets (Fig. 1g), and we found that the mouse half-lives were significantly more correlated among each other than a random set of half-lives (p < 10e-3). Given that these compendia were generated independently from one another, we conclude that their individual distributions are highly correlated and suggest that the half-lives of the human and mouse transcriptomes are enriched for shared features.
For mouse data, we normalize the data using the per sample normalization method [50], and then convert the data into log-space with the average of the total counts across all tissues as the reference sample. For both human and mouse data, we used the edgeR [48] package to normalize and to estimate the level of expression of each gene. Using the trimmed mean of M-values (TMM) method [51] to remove the detection threshold, we used the edgeR package to estimate the transcript-level TPM for each tissue (Additional file 4).
Mouse half-lives and raw data. We provided half-lives for all genes in mouse with the corresponding raw data, which is provided in Additional file 3. The raw data was log-transformed to account for the fact that values were provided as degradation rates.
For human samples, the raw half-life data was normalized using the quantile normalization method [49] and then transformed into pseudo-counts with the total number of counts per gene before the level of expression. 827ec27edc