Background: The commercially available 10x Genomics protocol to generate droplet-based single-cell RNA-seq (scRNA-seq) data is enjoying growing popularity among researchers. Fundamental to the ...analysis of such scRNA-seq data is the ability to cluster similar or same cells into non-overlapping groups. Many competing methods have been proposed for this task, but there is currently little guidance with regards to which method to use.
Methods: Here we use one gold standard 10x Genomics dataset, generated from the mixture of three cell lines, as well as three silver standard 10x Genomics datasets generated from peripheral blood mononuclear cells to examine not only the accuracy but also robustness of a dozen methods.
Results: We found that some methods, including Seurat and Cell Ranger, outperform other methods, although performance seems to be dependent on the complexity of the studied system. Furthermore, we found that solutions produced by different methods have little in common with each other.
Conclusions: In light of this, we conclude that the choice of clustering tool crucially determines interpretation of scRNA-seq data generated by 10x Genomics. Hence practitioners and consumers should remain vigilant about the outcome of 10x Genomics scRNA-seq analysis.
Remove unwanted variation (RUV) is an estimation and normalization system in which the underlying correlation structure of a multivariate dataset is estimated from negative control measurements, ...typically gene expression values, which are assumed to stay constant across experimental conditions. In this paper we derive the weight matrix which is estimated and incorporated into the generalized least squares estimates of RUV‐inverse, and show that this weight matrix estimates the average covariance matrix across negative control measurements. RUV‐inverse can thus be viewed as an estimation method adjusting for an unknown experimental design. We show that for a balanced incomplete block design (BIBD), RUV‐inverse recovers intra‐ and interblock estimates of the relevant parameters and combines them as a weighted sum just like the best linear unbiased estimator (BLUE), except that the weights are globally estimated from the negative control measurements instead of being individually optimized to each measurement as in the classical, single measurement BIBD BLUE.
Introduction
Genetic variations of enzymes that affect the pharmacokinetics and hence effects of medications differ between ethnicities, resulting in variation in the risk of adverse drug reactions ...(ADR) between different populations. Previous work has demonstrated that risk-group considerations can be incorporated into approaches of statistical signal detection. It is unknown whether databases of individual case safety reports (ICSRs) are sensitive to pharmacogenomic differences between populations.
Objective
The aim of this study was to explore the sensitivity of a global database of ICSRs to known pharmacogenomic risk variants common in Japan.
Methods
The data source was VigiBase, the global database of ICSRs, including all reports entered in the version frozen on 5 January 2020. Subgroup disproportionality analysis was used to compare ICSRs of two subgroups, Japan and rest of world (RoW). Reports for UGT1A1-metabolized irinotecan and the CYP2C19-metabolized drugs voriconazole, escitalopram and clopidogrel were selected for comparison between the subgroups based upon known genetic polymorphisms with high prevalence in Japan. Contrast between the subgroups was quantified by IC delta
(
IC
Δ
), a robust shrinkage observed-to-expected (OE) ratio on a log scale. Harmonic mean
p
values (HMP) were calculated for each drug to evaluate whether a list of pre-specified ADRs were collectively significantly over- (or under-)reported as hypothesized. Daily drug dosages were calculated for ICSRs with sufficient information, and dose distributions were compared between Japan and RoW and related to differences in regionally approved doses.
Results
The predictions of over-reporting patterns for specific ADRs were observed and confirmed in bootstrap HMP analyses (
p
= 0.004 for irinotecan and
p
< 0.001 for each of voriconazole, escitalopram and clopidogrel) and compared with similar drugs with different metabolic pathways. The impact of proactive regulatory action, such as recommended dosing and therapeutic drug monitoring (TDM), was also observable within the global database. For irinotecan and escitalopram, there was evidence of use of lower dosages as recommended in the Japanese labels; for voriconazole, there was evidence of use of TDM with an over-reporting of terms related to drug level measurements and an under-reporting of liver toxicity.
Conclusions
Pharmaco-ethnic vulnerabilities caused by pharmacogenomic differences between populations may contribute to differences in ADR reporting between countries in a global database of ICSRs. Regional analyses within a global database can inform on the effectiveness of local risk minimization measures and should be leveraged to catalyse the conversion of real-world usage into safer use of drugs in ethnically tailored ways.
In the conventional model of transcriptional activation, transcription factors bind to response elements and recruit co-factors, including histone acetyltransferases. Contrary to this model, we show ...that the histone acetyltransferase KAT7 (HBO1/MYST2) is required genome wide for histone H3 lysine 14 acetylation (H3K14ac). Examining neural stem cells, we find that KAT7 and H3K14ac are present not only at transcribed genes but also at inactive genes, intergenic regions, and in heterochromatin. KAT7 and H3K14ac were not required for the continued transcription of genes that were actively transcribed at the time of loss of KAT7 but indispensable for the activation of repressed genes. The absence of KAT7 abrogates neural stem cell plasticity, diverse differentiation pathways, and cerebral cortex development. Re-expression of KAT7 restored stem cell developmental potential. Overexpression of KAT7 enhanced neuron and oligodendrocyte differentiation. Our data suggest that KAT7 prepares chromatin for transcriptional activation and is a prerequisite for gene activation.
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•KAT7 is essential for H3K14 acetylation, neuron, and oligodendrocyte differentiation•KAT7 and H3K14ac are distributed widely in the genome, including in H3K9me3+ domains•Neuronal gene expression program fails to be activated in the absence of KAT7•KAT7 creates a permissive chromatin state for transcriptional activation
The role of acetylation of specific histone lysine residues remains unclear. Kueh et al. find that the absence of the lysine acetyltransferase KAT7 in neural stem cells causes loss of H3K14 acetylation, a failure to activate the neuron differentiation gene expression program, and neuronal differentiation.
The systematic study of transcriptional responses to genetic and chemical perturbations in human cells is still in its early stages. The largest available dataset to date is the newly released L1000 ...compendium. With its 1.3 million gene expression profiles of treated human cells it offers many opportunities for biomedical data mining, but also data normalization challenges of new dimensions. We developed a novel and practical approach to obtain accurate estimates of fold change response profiles from L1000, based on the RUV (Remove Unwanted Variation) statistical framework. Extending RUV to a big data setting, we propose an estimation procedure, in which an underlying RUV model is tuned by feedback through dataset specific statistical measures, reflecting p-value distributions and internal gene knockdown controls. Applying these metrics - termed evaluation endpoints - to disjoint data splits and integrating the results to select an optimal normalization, the procedure reduces bias and noise in the L1000 data, which in turn broadens the potential of this resource for pharmacological and functional genomic analyses. Our pipeline and normalization results are distributed as an R package (nelanderlab.org/FC1000.html).
Background: The commercially available 10x Genomics protocol to generate droplet-based single cell RNA-seq (scRNA-seq) data is enjoying growing popularity among researchers. Fundamental to the ...analysis of such scRNA-seq data is the ability to cluster similar or same cells into non-overlapping groups. Many competing methods have been proposed for this task, but there is currently little guidance with regards to which method to use.
Methods: Here we use one gold standard 10x Genomics dataset, generated from the mixture of three cell lines, as well as multiple silver standard 10x Genomics datasets generated from peripheral blood mononuclear cells to examine not only the accuracy but also running time and robustness of a dozen methods.
Results: We found that Seurat outperformed other methods, although performance seems to be dependent on many factors, including the complexity of the studied system. Furthermore, we found that solutions produced by different methods have little in common with each other.
Conclusions: In light of this we conclude that the choice of clustering tool crucially determines interpretation of scRNA-seq data generated by 10x Genomics. Hence practitioners and consumers should remain vigilant about the outcome of 10x Genomics scRNA-seq analysis.
Intra-tumor heterogeneity concerns the existence of genetically different subclones within the same tumor. Single sample quantification of heterogeneity relies on precise determination of chromosomal ...copy numbers throughout the genome, and an assessment of whether identified mutation variant allele fractions match clonal or subclonal copy numbers. We discuss these issues using data from SNP arrays, whole exome sequencing and pathologist purity estimates on several breast cancers characterized by ERBB2 amplification. We show that chromosomal copy numbers can only be estimated from SNP array signals or sequencing depths for subclonal tumor samples with simple subclonal architectures under certain assumptions.
Two-Dimensional Difference In Gel Electrophoresis (2D-DIGE) is a powerful tool for measuring differences in protein expression between samples or conditions. However, to remove systematic variability ...within and between gels the data has to be normalized. In this study we examined the ability of four existing and four novel normalization methods to remove systematic bias in data produced with 2D-DIGE. We also propose a modification of an existing method where the statistical framework determines whether a set of proteins shows an association with the predefined phenotypes of interest. This method was applied to our data generated from a monkey model (Macaca fascicularis) of Parkinson's disease.
Using 2D-DIGE we analysed the protein content of the striatum from 6 control and 21 MPTP-treated monkeys, with or without de novo or long-term L-DOPA administration. There was an intensity and spatial bias in the data of all the gels examined in this study. Only two of the eight normalization methods evaluated ('2D loess+scale' and 'SC-2D+quantile') successfully removed both the intensity and spatial bias. In 'SC-2D+quantile' we extended the commonly used loess normalization method against dye bias in two-channel microarray systems to suit systems with three or more channels.Further, by using the proposed method, Differential Expression in Predefined Proteins Sets (DEPPS), several sets of proteins associated with the priming effects of L-DOPA in the striatum in parkinsonian animals were identified. Three of these sets are proteins involved in energy metabolism and one set involved proteins which are part of the microtubule cytoskeleton.
Comparison of the different methods leads to a series of methodological recommendations for the normalization and the analysis of data, depending on the experimental design. Due to the nature of 2D-DIGE data we recommend that the p-values obtained in significance tests should be used as rankings only. Individual proteins may be interesting as such, but by studying sets of proteins the interpretation of the results are probably more accurate and biologically informative.
Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disorder mainly characterised by motor symptoms. Extensive physical activity has been implicated in the aetiology of ALS. Differences ...in anthropometrics, physical fitness and isometric strength measured at 18-19 years were assessed to determine if they are associated with subsequent death in ALS.
Data on body weight and height, physical fitness, resting heart rate and isometric strength measured at conscription were linked with data on death certificates in men born in 1951-1965 in Sweden (n=809 789). Physical fitness was assessed as a maximal test on an electrically braked bicycle ergometer. Muscle strength was measured as the maximal isometric strength in handgrip, elbow flexion and knee extension in standardised positions, using a dynamometer. Analyses were based on 684 459 (84.5%) men because of missing data. A matched case control study within this sample was performed. The population was followed until 31 December 2006, and 85 men died from ALS during this period.
Weight adjusted physical fitness (W/kg), but not physical fitness per se, was a risk factor for ALS (OR 1.98, 95% CI 1.32 to 2.97), whereas resting pulse rate, muscle strength and other variables were not.
Physical fitness, but not muscle strength, is a risk factor for death at early age in ALS. This may indicate that a common factor underlies both fitness (W/kg) and risk of ALS.
REPLICATED MICROARRAY DATA Lönnstedt, Ingrid; Speed, Terry
Statistica Sinica,
01/2002, Letnik:
12, Številka:
1
Journal Article
Recenzirano
cDNA microarrays permit us to study the expression of thousands of genes simultaneously. They are now used in many different contexts to compare mRNA levels between two or more samples of cells. ...Microarray experiments typically give us expression measurements on a large number of genes, say 10,000-20,000, but with few, if any, replicates for each gene. Traditional methods using means and standard deviations to detect differential expression are not completely satisfactory in this context, and so a different approach seems desirable. In this paper we present an empirical Bayes method for analysing replicated microarray data. Data from all the genes in a replicate set of experiments are combined into estimates of parameters of a prior distribution. These parameter estimates are then combined at the gene level with means and standard deviations to form a statistic B which can be used to decide whether differential expression has occurred. The statistic B avoids the problems of using averages or t-statistics. The method is illustrated using data from an experiment comparing the expression of genes in the livers of SR-BI transgenic mice with that of the corresponding wild-type mice. In addition we present the results of a simulation study estimating the ROC curve of B and three other statistics for determining differential expression: the average and two simple modifications of the usual t-statistic. B was found to be the most powerful of the four, though the margin was not great. The data were simulated to resemble the SR-BI data.
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