AbstractAssessing similarity is highly important for bioinformatics algorithms to determine correlations between biological information. A common problem is that similarity can appear by chance, particularly for low expressed entities. This is especially relevant in single cell RNA-seq (scRNA-seq) data because read counts are much lower compared to bulk RNA-seq.Recently, a Bayesian correlation scheme, that assigns low similarity to genes that have low confidence expression estimates, has been proposed to assess similarity for bulk RNA-seq. Our goal is to extend the properties of the Bayesian correlation in scRNA-seq data by considering 3 ways to compute similarity. First, we compute the similarity of pairs of genes over all cells. Second, we identify specific cell populations and compute the correlation in those populations. Third, we compute the similarity of pairs of genes over all clusters, by considering the total mRNA expression.We demonstrate that Bayesian correlations are more reproducible than Pearson correlations. Compared to Pearson correlations, Bayesian correlations have a smaller dependence on the number of input cells. We show that the Bayesian correlation algorithm assigns high similarity values to genes with a biological relevance in a specific population.We conclude that Bayesian correlation is a robust similarity measure in scRNA-seq data.
AbstractAssessing similarity is highly important for bioinformatics algorithms to determine correlations between biological information. A common problem is that similarity can appear by chance, particularly for low expressed entities. This is especially relevant in single cell RNA-seq (scRNA-seq) data because read counts are much lower compared to bulk RNA-seq.Recently, a Bayesian correlation scheme, that assigns low similarity to genes that have low confidence expression estimates, has been proposed to assess similarity for bulk RNA-seq. Our goal is to extend the properties of the Bayesian correlation in scRNA-seq data by considering 3 ways to compute similarity. First, we compute the similarity of pairs of genes over all cells. Second, we identify specific cell populations and compute the correlation in those populations. Third, we compute the similarity of pairs of genes over all clusters, by considering the total mRNA expression.We demonstrate that Bayesian correlations are more reproducible than Pearson correlations. Compared to Pearson correlations, Bayesian correlations have a smaller dependence on the number of input cells. We show that the Bayesian correlation algorithm assigns high similarity values to genes with a biological relevance in a specific population.We conclude that Bayesian correlation is a robust similarity measure in scRNA-seq data.
“…Meng (2017) 12 found that PROX1 and LYVE1 correlate with liver regeneration and can be used as biomarkers to identify formation of new liver sinusoidal endothelial cells. However, Meng (2017) 12 did not indicate how the endothelial cells constitute sinusoidal final shape. The abovementioned studies found factors correlated with organogenesis (see orange highlight in Table 3).…”
Section: Factors Associated With Regeneration Process Pacementioning
Researchers in different disciplines studied liver’s genetic expression of organogenesis in embryogenesis; however, organogenesis has not been studied as an independent and a complementary process during adult liver regeneration. This paper reviewed studies and extracted information related to organogenesis in adult liver regeneration because of organogenesis’ important role in cancer and tissue regeneration.
“…While many studies investigated different factors involved in liver regeneration, only a few researched factors involved in organogenesis (Table 3) investigated hepatocyte nuclear factor (vHnf1) gene and its effect on regeneration process and concluded that a lack of vHNF1 leads to formation of a defective hepatic bud and an abnormal gut regionalization. Meng (2017) 9 found that PROX1 and LYVE1 correlate with liver regeneration and can be used as biomarkers to identify formation of new liver sinusoidal endothelial cells. However, Meng (2017) 9 did not indicate how the endothelial cells constitute sinusoidal final shape.…”
Section: Factors Associated With Regeneration Process Pacementioning
confidence: 99%
“…Meng (2017) 9 found that PROX1 and LYVE1 correlate with liver regeneration and can be used as biomarkers to identify formation of new liver sinusoidal endothelial cells. However, Meng (2017) 9 did not indicate how the endothelial cells constitute sinusoidal final shape. The abovementioned studies found factors correlated with organogenesis (see orange highlight in Table 3).…”
Section: Factors Associated With Regeneration Process Pacementioning
Researchers in different disciplines studied liver's genetic expression of organogenesis in embryogenesis; however, organogenesis has not been studied as an independent and a complementary process during adult liver regeneration. This paper reviewed studies and extracted information related to organogenesis in adult liver regeneration because of organogenesis' important role in cancer and tissue regeneration.
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