Similar Datasets
Project description:A contamination focused approach for optimizing the single-cell RNA-seq experiment
| PRJNA982142 | ENA
Project description:Created scRNA seq datasets for mouse stomach metaplastic organoids
2024-06-14 | GSE269839 | GEO
Project description:A key challenge in single cell RNA-sequencing (scRNA-seq) data analysis are dataset- and batch-specific differences that can obscure the biological signal of interest. While there are various tools and methods to perform data integration and correct for batch effects, their performance can vary between datasets and according to the nature of the bias. Therefore, it is important to understand how batch effects manifest in order to adjust for them in a reliable way. Here, we systematically explore batch effects in scRNA-seq data from a variety of datasets according to magnitude, cell type specificity and complexity. We developed a cell-specific mixing score (\texttt{cms}) that quantifies how well cells from multiple batches are mixed. By considering distance distributions (in a lower dimensional space), the score is able to detect local batch bias and differentiate between unbalanced batches (i.e., when one cell type is more abundant in a batch) and systematic differences between cells of the same cell type. We implemented the \texttt{cms}, as well as related metrics to detect batch effects or measure structure preservation, in the CellMixS R/Bioconductor package. We systematically compare different metrics that have been proposed to quantify batch effects or bias in scRNA-seq data using real datasets with known batch effects and synthetic data that mimic various real data scenarios. While these metrics target the same question and are used interchangeably, we find differences in inter- and intra-dataset scalability, sensitivity and in a metric's ability to handle batch effects with differentially abundant cell types. We find that cell-specific metrics outperform cell type-specific and global metrics and recommend them for both method benchmarks and batch exploration.
2021-01-31 | E-MTAB-9916 | biostudies-arrayexpress
Project description:Background: As the most stable and experimentally accessible epigenetic mark, DNA methylation is of great interest to the research community. The landscape of DNA methylation across tissues, through development and in disease pathogenesis is not yet well characterised. Thus there is a need for rapid and cost effective methods for assessing genome-wide levels of DNA methylation. The Illumina Infinium HumanMethylation450 (450K) BeadChip is a very useful addition to the available methods but its complex design, incorporating two different assay methods, requires careful consideration. Accordingly, several normalization schemes have been published. We have taken advantage of known DNA methylation patterns associated with genomic imprinting and X-chromosome inactivation (XCI), in addition to the performance of SNP genotyping assays present on the array, to derive three independent metrics which we use to test alternative schemes of correction and normalization. These metrics also have potential utility as quality scores for datasets. Results: The standard index of DNA methylation at any specific CpG site is ? = M/(M + U + 100) where M and U are methylated and unmethylated signal intensities. Betas calculated from raw signal intensities (the default GenomeStudio behaviour) perform well, but using 11 methylomic datasets we demonstrate that quantile normalization methods produce marked improvement, even in highly consistent data, by all three metrics. The commonly used procedure of normalizing betas is inferior to the separate normalization of M and U, and it is also advantageous to normalize Type I and Type II assays separately. More elaborate manipulation of quantiles proves to be counterproductive. Conclusions: Careful selection of preprocessing steps can minimise variance and thus improve statistical power, especially for the detection of the small absolute DNA methylation changes likely associated with complex disease phenotypes. For the convenience of the research community we have created an R software package called wateRmelon, compatible with the existing methylumi, minfi and IMA packages, that allows others to utilise the same normalization methods and data quality tests on 450K data. Bisulfite converted DNA from the 11 cohorts (N=695, including 36 technical replicates) were hybridised to the Illumina Infinium 450k Human Methylation Beadchip v1.2
2013-01-24 | E-GEOD-43414 | biostudies-arrayexpress
Project description:Background: As the most stable and experimentally accessible epigenetic mark, DNA methylation is of great interest to the research community. The landscape of DNA methylation across tissues, through development and in disease pathogenesis is not yet well characterised. Thus there is a need for rapid and cost effective methods for assessing genome-wide levels of DNA methylation. The Illumina Infinium HumanMethylation450 (450K) BeadChip is a very useful addition to the available methods but its complex design, incorporating two different assay methods, requires careful consideration. Accordingly, several normalization schemes have been published. We have taken advantage of known DNA methylation patterns associated with genomic imprinting and X-chromosome inactivation (XCI), in addition to the performance of SNP genotyping assays present on the array, to derive three independent metrics which we use to test alternative schemes of correction and normalization. These metrics also have potential utility as quality scores for datasets. Results: The standard index of DNA methylation at any specific CpG site is β = M/(M + U + 100) where M and U are methylated and unmethylated signal intensities. Betas calculated from raw signal intensities (the default GenomeStudio behaviour) perform well, but using 11 methylomic datasets we demonstrate that quantile normalization methods produce marked improvement, even in highly consistent data, by all three metrics. The commonly used procedure of normalizing betas is inferior to the separate normalization of M and U, and it is also advantageous to normalize Type I and Type II assays separately. More elaborate manipulation of quantiles proves to be counterproductive. Conclusions: Careful selection of preprocessing steps can minimise variance and thus improve statistical power, especially for the detection of the small absolute DNA methylation changes likely associated with complex disease phenotypes. For the convenience of the research community we have created an R software package called wateRmelon, compatible with the existing methylumi, minfi and IMA packages, that allows others to utilise the same normalization methods and data quality tests on 450K data.
2013-01-24 | GSE43414 | GEO
Project description:Background: Genetic and genomic selection programs require large numbers of phenotypes observed for animals in shared environments. Direct measurements of phenotypes like meat quality, methane emission, and disease susceptibility are difficult and expensive to measure at scale but are critically important to livestock production. Our work leans on our understanding of the “Central Dogma” of molecular genetics to leverage molecular intermediates as cheaply-measured proxies of organism-level phenotypes. The rapidly declining cost of next-generation sequencing presents opportunities for population-level molecular phenotyping. While the cost of whole transcriptome sequencing has declined recently, its required sequencing depth still makes it an expensive choice for wide-scale molecular phenotyping. We aim to optimize 3′ mRNA sequencing (3′ mRNA-Seq) approaches for collecting cost-effective proxy molecular phenotypes for cattle from easy-to-collect tissue samples (i.e., whole blood). We used matched 3′ mRNA-Seq samples for 15 Holstein male calves in a heat stress trail to identify the 1) best library preparation kit (Takara SMART-Seq v4 3′ DE and Lexogen QuantSeq) and 2) optimal sequencing depth (0.5 to 20 million reads/sample) to capture gene expression phenotypes most cost-effectively. Results: Takara SMART-Seq v4 3′ DE outperformed Lexogen QuantSeq libraries across all metrics: number of quality reads, expressed genes, informative genes, differentially expressed genes, and 3′ biased intragenic variants. Serial downsampling analyses identified that as few as 8.0 million reads per sample could effectively capture most of the between-sample variation in gene expression. However, progressively more reads did provide marginal increases in recall across metrics. These 3′ mRNA-Seq reads can also capture animal genotypes that could be used as the basis for downstream imputation. The 10 million read downsampled groups called an average of 104,386 SNPs and 20,131 INDELs, many of which segregate at moderate minor allele frequencies in the population. Conclusion: This work demonstrates that 3′ mRNA-Seq with Takara SMART-Seq v4 3′ DE can provide an incredibly cost-effective (<$25/sample) approach to quantifying molecular phenotypes (gene expression) while discovering sufficient variation for use in genotype imputation. Ongoing work is evaluating the accuracy of imputation and the ability of much larger datasets to predict individual animal phenotypes.
2024-07-22 | GSE272596 | GEO
Project description:Single-cell RNA sequencing (scRNA-seq) holds great potential to revolutionize our understanding of cancer biology by discovering biomarkers and biological functions from circulating tumor cells (CTCs). However, the contamination from blood cells is one of the main challenges in CTC studies. Through investigation of recently published studies, we found that several previously reported CTC “biomarkers” were apparently contaminated by blood-derived transcripts such as HBB, HBA1/2, ITGA2B, and LCP1. Through re-analysis of several published CTC and cell line datasets, we revealed that CTC “biomarkers” were not only highly expressed in blood, but showed a high correlation between CTC and blood. We sequenced transcriptome of seven blood components from a healthy donor, finding that the platelet exhibited the highest probability to confound CTC data in terms of expression profiling and correlation analysis. Using CTC spike-in and recovery assay and we showed that the platelet induced artifacts could be reproduced in vitro. Surprisingly, the reproduced artifacts were also highly expressed in the platelet data but not in the other six blood components. We performed SNP calling on recovered CTC simulators, finding cross-contamination of exogenous transcripts. Furthermore, we applied a multiple linear regression model to estimate the platelet confounding index, showing that previously published CTC datasets were confounded with 13%~26% of platelet fractions and 7.9%~17% platelet cloaking judged by fluorescence flow cytometry. We devised a red blood cell lysis based protocol that better removed platelet compared to several platelet inhibitory reagents and kept transcriptomic profile undisturbed in CTC scRNA-seq. This work not only demonstrated the potential blood cell confounding factors in CTC studies, validated them in the past published datasets, and reproduced the artifacts in CTC simulation assay, but also devised an easily applicable method to remove the platelet cloaking, guaranteeing the reliability of CTC scRNA-seq research.
2020-04-18 | GSE135992 | GEO
Project description:Ratiometric controls were prepared from commercial sources of high quality RNA from human tissues with distinctly different expression patterns. The samples were processed using six different target labeling protocols and replicate datasets were generated on high density gene expression microarrays. The area under the curve from receiver operating characteristic plots was calculated as a measure of diagnostic performance. The reliable region of the dynamic range was derived from log ratio deviation plots made for each dataset. These samples and metrics provide a mechanism for assessing process improvement that can be used by clinical laboratories conducting microarray assays. <br><br>Additional processed data files are available on the FTP site for this experiment in E-TABM-1091.additional.zip.
2011-04-08 | E-TABM-1091 | biostudies-arrayexpress
Project description:Single-cell RNA sequencing (scRNA-seq) has emerged as a vital tool in tumor research, enabling exploration of molecular complexities at the individual cell level. It offers new technical possibilities for advancing tumor research and is anticipated to yield significant breakthroughs. However, deciphering meaningful insights from scRNA-seq data poses challenges, particularly in cell annotation and tumor subpopulation identification. Efficient algorithms are needed to unravel the intricate biological processes of cancer. To address these challenges, benchmarking datasets are essential to validate bioinformatics tools focusing on the analysis of single-cell omics in oncology. Here, we present a 10XGenomics scRNA-seq experiment, providing a controlled heterogeneity environment using lung cancer cell lines characterised by expressing seven different driver genes (EGFR, ALK, MET, ERBB2, KRAS, BRAF, ROS1), which are characterised by the presence of partial overlaps in their functional pathways. Furthermore, PBMC from a healthy donor were also sequenced
2023-12-02 | GSE243665 | GEO
Project description:Single-cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we developed a computational strategy to "anchor" diverse datasets together, enabling us to integrate and compare single-cell measurements not only across scRNA-seq technologies, but different modalities as well. As one demonstration of the method, we anchor single-cell protein measurements with a human bone marrow atlas to annotate and characterize lymphocyte populations. The multimodal data, generated using CITE-seq, is provided here alongside a corresponding bulk validation experiment.
2019-06-06 | GSE128639 | GEO