Assessing Consistency Across Functional Screening Datasets in Cancer Cells.
Ontology highlight
ABSTRACT: Motivation
Many high-throughput screening studies have been carried out in cancer cell lines to identify therapeutic agents and targets. Existing consistency assessment studies only examined two datasets at a time, with conclusions based on a subset of carefully selected features rather than considering global consistency of all the data. However, poor concordance can still be observed for a large part of the data even when selected features are highly consistent.Results
In this study we assembled nine compound screening datasets and three functional genomics datasets. We derived direct measures of consistency as well as indirect measures of consistency based on association between functional data and copy number-adjusted gene expression data. These results have been integrated into a web application - the Functional Data Consistency Explorer (FDCE), to allow users to make queries and generate interactive visualizations so that functional data consistency can be assessed for individual features of interest.Availability
The FDCE web tool and we have developed and the functional data consistency measures we have generated are available at https://lccl.shinyapps.io/FDCE/.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Cai L
PROVIDER: S-EPMC8652113 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Microarray studies with human subjects often have limited sample sizes which hampers the ability to detect reliable biomarkers associated with disease and motivates the need to aggregate data across studies. However, human gene expression measurements may be influenced by many non-random factors such as genetics, sample preparations, and tissue heterogeneity. These factors can contribute to a lack of agreement among related studies, limiting the utility of their aggregation. We show that it is feasible to carry out an automatic correction of individual datasets to reduce the effect of such 'latent variables' (without prior knowledge of the variables) in such a way that datasets addressing the same condition show better agreement once each is corrected. We build our approach on the method of surrogate variable analysis but we demonstrate that the original algorithm is unsuitable for the analysis of human tissue samples that are mixtures of different cell types. We propose a modification to SVA that is crucial to obtaining the improvement in agreement that we observe. We develop our method on a compendium of multiple sclerosis data and verify it on an independent compendium of Parkinson's disease datasets. In both cases, we show that our method is able to improve agreement across varying study designs, platforms, and tissues. This approach has the potential for wide applicability to any field where lack of inter-study agreement has been a concern.
| S-EPMC3989170 | biostudies-literature
Project description:In biology, organization at multiple scales potentiates biological function. Current advances in staining and imaging of biological tissues provide a wealth of data, but there are few metrics to quantitatively describe these findings. In particular there is a need for a metric that would characterize the correlation and consistency of orientation of different biological constructs within a tissue. We aimed to create such a metric and to demonstrate its use with images of cardiac tissues. The co-orientational order parameter (COOP) was based on the mathematical framework of a classical parameter, the orientational order parameter (OOP). Theorems were proven to illustrate the properties and boundaries of the COOP, which was then applied to both synthetic and experimental data. We showed the COOP to be useful for quantifying the correlation of orientation of constructs such as actin filaments and sarcomeric Z-lines. As expected, cardiac tissues showed perfect correlation between actin filaments and Z-lines. We also demonstrated the use of COOP to quantify the consistency of construct orientation within cells of the same shape. The COOP provides a quantitative tool to characterize tissues beyond co-localization or single construct orientation distribution. In the future, this new parameter could be used to represent the quantitative changes during maturation of cardiac tissue, pathological malformation, and other processes.
| S-EPMC4388480 | biostudies-literature
Project description:Patch-seq, combining patch-clamp electrophysiology with single-cell RNA-sequencing (scRNAseq), enables unprecedented access to a neuron's transcriptomic, electrophysiological, and morphological features. Here, we present a re-analysis of five patch-seq datasets, representing cells from ex vivo mouse brain slices and in vitro human stem-cell derived neurons. Our objective was to develop simple criteria to assess the quality of patch-seq derived single-cell transcriptomes. We evaluated patch-seq transcriptomes for the expression of marker genes of multiple cell types, benchmarking these against analogous profiles from cellular-dissociation based scRNAseq. We found an increased likelihood of off-target cell-type mRNA contamination in patch-seq cells from acute brain slices, likely due to the passage of the patch-pipette through the processes of adjacent cells. We also observed that patch-seq samples varied considerably in the amount of mRNA that could be extracted from each cell, strongly biasing the numbers of detectable genes. We developed a marker gene-based approach for scoring single-cell transcriptome quality post-hoc. Incorporating our quality metrics into downstream analyses improved the correspondence between gene expression and electrophysiological features. Our analysis suggests that technical confounds likely limit the interpretability of patch-seq based single-cell transcriptomes. However, we provide concrete recommendations for quality control steps that can be performed prior to costly RNA-sequencing to optimize the yield of high-quality samples.
| S-EPMC6187980 | biostudies-literature
Project description:BackgroundTranscriptome sequencing and assembly represent a great resource for the study of non-model species, and many metrics have been used to evaluate and compare these assemblies. Unfortunately, it is still unclear which of these metrics accurately reflect assembly quality.ResultsWe simulated sequencing transcripts of Drosophila melanogaster. By assembling these simulated reads using both a "perfect" and a modern transcriptome assembler while varying read length and sequencing depth, we evaluated quality metrics to determine whether they 1) revealed perfect assemblies to be of higher quality, and 2) revealed perfect assemblies to be more complete as data quantity increased.Several commonly used metrics were not consistent with these expectations, including average contig coverage and length, though they became consistent when singletons were included in the analysis. We found several annotation-based metrics to be consistent and informative, including contig reciprocal best hit count and contig unique annotation count. Finally, we evaluated a number of novel metrics such as reverse annotation count, contig collapse factor, and the ortholog hit ratio, discovering that each assess assembly quality in unique ways.ConclusionsAlthough much attention has been given to transcriptome assembly, little research has focused on determining how best to evaluate assemblies, particularly in light of the variety of options available for read length and sequencing depth. Our results provide an important review of these metrics and give researchers tools to produce the highest quality transcriptome assemblies.
| S-EPMC3733778 | biostudies-literature
Project description:BackgroundAs one of the most prevalent genitourinary cancers, bladder cancer (BLCA) is associated with high morbidity and mortality. Currently, limited indicators are available for early detection and diagnosis of bladder cancer, and there is a lack of specific biomarkers for evaluating the prognosis of BLCA patients. This study aims to identify critical genes that affect bladder cancer immunity to improve the diagnosis and prognosis of bladder cancer and to identify new biomarkers and targets for immunotherapy.MethodsTwo GEO datasets were used to screen differentially expressed genes (DEGs). The STRING database was used to construct a protein-protein interaction network of DEGs, and plug-in APP CytoHubba in Cytoscape was used to identify critical genes in the network. GO and KEGG analyses explored the functions and pathways of differential gene enrichment. We used GEPIA to validate the expression of differential genes, their impact on patient survival, and their relationship to clinicopathological parameters. Additionally, hub genes were verified using qRT-PCR and Western blotting. Immune infiltration analysis and multiple immunohistochemistry reveal the impact of Hub genes on the tumor microenvironment.ResultWe screened out 259 differential genes, and identified 10 key hub genes by the degree algorithm. Four genes (ACTA2, FLNA, TAGLN, and TPM1) were associated with overall or disease-free survival in BLCA patients and were significantly associated with clinical parameters. We experimentally confirmed that the mRNA and protein levels of these four genes were significantly decreased in bladder cancer cells. Immunoassays revealed that these four genes affect immune cell infiltration in the tumor microenvironment; they increased the polarization of M2 macrophages.ConclusionThese four genes affect the tumor microenvironment of bladder cancer, provide a new direction for tumor immunotherapy, and have significant potential in the diagnosis and prognosis of bladder cancer.
| S-EPMC10232963 | biostudies-literature
Project description:Multi-modal imaging, by light-microscopy (LM) and Magnetic Resonance Imaging (MRI), holds promise for examining the brain across various resolutions and scales. While MRI acquires images in three dimensions, acquisition of intact whole-brain by LM requires a process of tissue clearing that renders the brain transparent. Removal of lipids (delipidation) is a critical step in the tissue clearing process, and was previsouly suggested to be the cause for absence of MRI contrast in cleared brains. Yet, the association between MRI contrast, delipidation and the different clearing techniques is debatable. Here, we provide datasets concerning lipid-content in cleared brain tissues obtained by various approaches. Fixed mouse and rat brains were cleared by CLARITY, Scale, uDISCO and ECi clearing techniques. Lipid-content was assessed at various intermediate steps of the different clearing methods, as well as at the end of the processes. Methods employed included whole brain MRI acquisition, Oil Red O (ORO)- and carbocyanine DiI-staining of cryosections, and DiI-washout assay from brain slices. MRI contrast-to-noise ratio, staining intensities and integrity of tissue were systematically analyzed. We demonstrate that lipid electrophoresis, an essential step of the CLARITY approach, engenders progressive reduction in MRI contrast in non-cleared (PFA-fixed) control brains, as well as strongly reduces contrast from uDISCO and ECi-cleared brains. ORO minimally stained CLARITY-cleared brains, however efficiently labelled uDISCO and ECi-cleared brains. Conversely, and in contrast to ORO-staining, DiI equally stained control, CLARITY, ECi and uDISCO-cleared brains. Both ORO- and DiI-staining demonstrated impairment in brain tissue integrity following CLARITY, but less so in uDISCO and ECi brains. DiI-washout assay demonstrated that each of the solvents employed along the process of uDISCO and ECi are highly delipidating, as well as the SDS-electrophoresis employed during CLARITY clearing. However, Scale treatment preserved most of the DiI dye. These data emphasize the variability in lipid assessment of cleared tissues by common techniques, and may help to resolve the contribution of lipids in brain MRI contrast.
| S-EPMC10749242 | biostudies-literature
Project description:BackgroundLung cancer (LC) is the leading cause of cancer-related deaths in the United States (U.S.), with non-White people who smoke often bearing the burden of the highest rate of LC mortality. This is often due to later stage diagnoses, leading to poor prognosis and outcomes. We assess here how the eligibility criteria for LC screening set by the U.S. Preventive Services Task Force (USPSTF) and the Centers for Medicare and Medicaid Services (CMS) could contribute to racial disparities in screening access.MethodsThis paper analyzes data from the National Health and Nutrition Examination Survey (NHANES), an annual survey conducted by the Centers for Disease Control and Prevention (CDC) that gathers health and nutrition data from a representative sample of the U.S. population. After excluding those who were ineligible for LC screening, the final cohort of participants was 5,001, which consisted of 2,669 people who formerly smoked and 2,332 people who currently smoke.ResultsOut of 608 participants who were eligible for LC screening, 77.5% were non-Hispanic White (NHW) and 8.7% were non-Hispanic Black (NHB) participants versus 69.4% and 10.8% among 4,393 ineligible participants. Age, pack-years, and age along with pack-years were the most frequent reasons for ineligibility. LC screening ineligible NHW participants were statistically significantly older and had higher mean pack-years than the other racial and ethnic groups. NHB participants among the ineligible group had higher urinary cotinine levels compared to NHW participants.ConclusionsThis paper underscores the need for more individualized risk estimates when determining eligibility for LC screening, which could include biomarkers of smoking exposure. The analysis shows that current screening criteria, which rely solely on factors such as age and pack years, contribute to LC racial disparities.
| S-EPMC10261853 | biostudies-literature
Project description:Sequencing-based gene expression methods like RNA-sequencing (RNA-seq) have become increasingly common, but it is often claimed that results obtained in different studies are not comparable owing to the influence of laboratory batch effects, differences in RNA extraction and sequencing library preparation methods and bioinformatics processing pipelines. It would be unfortunate if different experiments were in fact incomparable, as there is great promise in data fusion and meta-analysis applied to sequencing data sets. We therefore compared reported gene expression measurements for ostensibly similar samples (specifically, human brain, heart and kidney samples) in several different RNA-seq studies to assess their overall consistency and to examine the factors contributing most to systematic differences. The same comparisons were also performed after preprocessing all data in a consistent way, eliminating potential bias from bioinformatics pipelines. We conclude that published human tissue RNA-seq expression measurements appear relatively consistent in the sense that samples cluster by tissue rather than laboratory of origin given simple preprocessing transformations. The article is supplemented by a detailed walkthrough with embedded R code and figures.
| S-EPMC4652619 | biostudies-literature
Project description:When numerous treatments exist for a disease (Treatments 1, 2, 3, etc), network meta-regression (NMR) examines whether each relative treatment effect (eg, mean difference for 2 vs 1, 3 vs 1, and 3 vs 2) differs according to a covariate (eg, disease severity). Two consistency assumptions underlie NMR: consistency of the treatment effects at the covariate value 0 and consistency of the regression coefficients for the treatment by covariate interaction. The NMR results may be unreliable when the assumptions do not hold. Furthermore, interactions may exist but are not found because inconsistency of the coefficients is masking them, for example, when the treatment effect increases as the covariate increases using direct evidence but the effect decreases with the increasing covariate using indirect evidence. We outline existing NMR models that incorporate different types of treatment by covariate interaction. We then introduce models that can be used to assess the consistency assumptions underlying NMR for aggregate data. We extend existing node-splitting models, the unrelated mean effects inconsistency model, and the design by treatment inconsistency model to incorporate covariate interactions. We propose models for assessing both consistency assumptions simultaneously and models for assessing each of the assumptions in turn to gain a more thorough understanding of consistency. We apply the methods in a Bayesian framework to trial-level data comparing antimalarial treatments using the covariate average age and to four fabricated data sets to demonstrate key scenarios. We discuss the pros and cons of the methods and important considerations when applying models to aggregated data.
| S-EPMC6563470 | biostudies-literature
Project description:IntroductionThickened liquids are commonly recommended to reduce the risk of penetration-aspiration. However, questions persist regarding the impact of bolus consistency on swallowing safety. The common practice of summarizing Penetration-Aspiration Scale (PAS) scores based on worst scores is a bias in prior analyses. The aim of this study was to examine the impact of liquid consistency on PAS scores using a Bayesian multilevel ordinal regression model approach, considering all scores across repeated bolus trials. A second aim was to determine whether PAS scores differed across thickener type within consistency.MethodsWe analyzed two prior datasets (D1; D2). D1 involved 678 adults with suspected dysphagia (289 female; mean age 69 years, range 20-100). D2 involved 177 adults (94 female; mean age 54 years, range 21-85), of whom 106 were nominally healthy and 71 had suspected dysphagia. All participants underwent videofluoroscopy involving ≥3 boluses of 20% w/v thin liquid barium and of xanthan-gum thickened barium in mildly, moderately and extremely thick consistencies. D2 participants also swallowed trials of slightly thick liquid barium, and starch-thickened stimuli for each thickened consistency. Duplicate blinded rating yielded PAS scores per bolus, with discrepancies resolved by consensus. PAS ratings for a total of 8,185 and 3,407 boluses were available from D1 and D2, respectively. Bayesian models examined PAS patterns across consistencies. We defined meaningful differences as non-overlapping 95% credible intervals (CIs).ResultsAcross D1 and D2, penetration occurred on 10.87% of trials compared to sensate (0.68%) and silent aspiration (1.54%), with higher rates of penetration (13.47%) and aspiration (3.07%) on thin liquids. For D1, the probability of a PAS score > 2 was higher for thin liquids with weighted PAS scores of 1.57 (CI: 1.48, 1.66) versus mildly (1.26; CI: 1.2, 1.33), moderately (1.1; CI: 1.07, 1.13), and extremely thick liquids (1.04; CI: 1.02, 1.08). D2 results were similar. Weighted PAS scores did not meaningfully differ between thin and slightly thick liquids, or between starch and xanthan gum thickened liquids.DiscussionThese results confirm that the probability of penetration-aspiration is greatest on thin liquids compared to thick liquids, with significant reductions in PAS severity emerging with mildly thick liquids.
| S-EPMC10920265 | biostudies-literature