Similar Datasets
Project description:RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, scnRNA-Seq for short), can help characterize the composition of tissues and reveal cells that influence key healthy and disease functions. However, the use of these technologies is challenging because of their relatively high costs and exacting sample collection requirements. Computational deconvolution methods that infer the composition of RNA-Seq-profiled samples using scnRNA-Seq-characterized cell types can expand the benefit of these technologies, but their effectiveness remains controversial. We produced the first systematic evaluation of deconvolution methods on datasets with either known compositions or based on concurrent RNA-Seq and scnRNA-Seq profiles. Our analyses revealed biases that are common to scnRNA-Seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-Seq and scnRNA-Seq profiles can help improve the accuracy of both scnRNA-Seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), combined RNA-Seq transformation and a dampened weighted least squares deconvolution approach to consistently outperform other methods in predicting the composition of cell mixtures and tissue samples. Moreover, our analysis suggested that only SQUID could identify outcomes-predictive cancer cell subtypes in pediatric acute myeloid leukemia and neuroblastoma datasets.
2023-04-28 | GSE220607 | GEO
Project description:RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, scnRNA-Seq for short), can help characterize the composition of tissues and reveal cells that influence key healthy and disease functions. However, the use of these technologies is challenging because of their relatively high costs and exacting sample collection requirements. Computational deconvolution methods that infer the composition of RNA-Seq-profiled samples using scnRNA-Seq-characterized cell types can expand the benefit of these technologies, but their effectiveness remains controversial. We produced the first systematic evaluation of deconvolution methods on datasets with either known compositions or based on concurrent RNA-Seq and scnRNA-Seq profiles. Our analyses revealed biases that are common to scnRNA-Seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-Seq and scnRNA-Seq profiles can help improve the accuracy of both scnRNA-Seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), combined RNA-Seq transformation and a dampened weighted least squares deconvolution approach to consistently outperform other methods in predicting the composition of cell mixtures and tissue samples. Moreover, our analysis suggested that only SQUID could identify outcomes-predictive cancer cell subtypes in pediatric acute myeloid leukemia and neuroblastoma datasets.
2023-04-28 | GSE220605 | GEO
Project description:RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, scnRNA-Seq for short), can help characterize the composition of tissues and reveal cells that influence key healthy and disease functions. However, the use of these technologies is challenging because of their relatively high costs and exacting sample collection requirements. Computational deconvolution methods that infer the composition of RNA-Seq-profiled samples using scnRNA-Seq-characterized cell types can expand the benefit of these technologies, but their effectiveness remains controversial. We produced the first systematic evaluation of deconvolution methods on datasets with either known compositions or based on concurrent RNA-Seq and scnRNA-Seq profiles. Our analyses revealed biases that are common to scnRNA-Seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-Seq and scnRNA-Seq profiles can help improve the accuracy of both scnRNA-Seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), combined RNA-Seq transformation and a dampened weighted least squares deconvolution approach to consistently outperform other methods in predicting the composition of cell mixtures and tissue samples. Moreover, our analysis suggested that only SQUID could identify outcomes-predictive cancer cell subtypes in pediatric acute myeloid leukemia and neuroblastoma datasets.
2023-04-28 | GSE220651 | GEO
Project description:RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, scnRNA-Seq for short), can help characterize the composition of tissues and reveal cells that influence key healthy and disease functions. However, the use of these technologies is challenging because of their relatively high costs and exacting sample collection requirements. Computational deconvolution methods that infer the composition of RNA-Seq-profiled samples using scnRNA-Seq-characterized cell types can expand the benefit of these technologies, but their effectiveness remains controversial. We produced the first systematic evaluation of deconvolution methods on datasets with either known compositions or based on concurrent RNA-Seq and scnRNA-Seq profiles. Our analyses revealed biases that are common to scnRNA-Seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-Seq and scnRNA-Seq profiles can help improve the accuracy of both scnRNA-Seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), combined RNA-Seq transformation and a dampened weighted least squares deconvolution approach to consistently outperform other methods in predicting the composition of cell mixtures and tissue samples. Moreover, our analysis suggested that only SQUID could identify outcomes-predictive cancer cell subtypes in pediatric acute myeloid leukemia and neuroblastoma datasets.
2023-04-28 | GSE220606 | GEO
Project description:We have compared the genome-wide effects on the transcriptome after treatment with ICG-001 (the specific CBP inhibitor) versus C646, a compound that competes with acetyl-coA for the Lys-coA binding pocket of both CBP and p300. We found that both drugs cause large-scale changes in the transcriptome of HCT116 colon cancer cells and PANC1 pancreatic cancer cells, and reverse some tumor-specific changes in gene expression. Interestingly, although the epigenetic inhibitors affect cell cycle pathways in both the colon and pancreatic cancer cell lines, the WNT signaling pathway was affected only in the colon cancer cells. Notably, WNT target genes were similarly down-regulated after treatment of HCT116 with C646 as with ICG-001. To identify genes affected by direct targeting of a component of the transcriptional complex implicated in WNT regulation, we used siRNAs to knockdown TCF7L2 in PANC1 cells. Cells were treated with control siRNAs or siRNAs specific for TCF7L2 and RNA was analyzed by RNA-seq.
| S-ECPF-GEOD-63776 | biostudies-other
Project description:Tissue samples of tumor cells mixed with stromal cells cause underdetection of gene expression signatures associated with cancer prognosis or response to treatment. In silico dissection of mixed cell samples is essential for analyzing expression data generated in cancer studies. Currently, a systematic approach is lacking to address three challenges in computational deconvolution: (i) violation of linear addition of expression levels from multiple tissues when log-transformed microarray data are used; (ii) estimation of both tumor proportion and tumor-specific expression, when neither is known a priori; and (iii) estimation of expression profiles for individual patients.We have developed a statistical method for deconvolving mixed cancer transcriptomes, DeMix, which addresses the aforementioned issues in array-based expression data. We demonstrate the performance of our model in synthetic and real, publicly available, datasets. DeMix can be applied to ongoing biomarker-based clinical studies and to the vast expression datasets previously generated from mixed tumor and stromal cell samples.All codes are written in C and integrated into an R function, which is available at http://odin.mdacc.tmc.edu/?wwang7/DeMix.html.wwang7@mdanderson.orgSupplementary data are available at Bioinformatics online.
| S-EPMC3841439 | biostudies-literature
Project description:Cell type heterogeneity presents a challenge to the interpretation of epigenome data, compounded by the difficulty in generating reliable single-cell DNA methylomes for large numbers of cells and samples. We present EPISCORE, a computational algorithm that performs virtual microdissection of bulk tissue DNA methylation data at single cell-type resolution for any solid tissue. EPISCORE applies a probabilistic epigenetic model of gene regulation to a single-cell RNA-seq tissue atlas to generate a tissue-specific DNA methylation reference matrix, allowing quantification of cell-type proportions and cell-type-specific differential methylation signals in bulk tissue data. We validate EPISCORE in multiple epigenome studies and tissue types.
| S-EPMC7650528 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2015-01-07 | E-GEOD-64039 | biostudies-arrayexpress
Project description:Bulk expression data from heterogeneous cell populations pose a challenge for investigators, as differences in cell numbers and transcriptional programs may complicate analysis. To improve the performance of bulk RNA sequencing on mixed populations, we created Immune Cell Linkage through Exploratory Matrices (ICLite). The ICLite package for R constructs modules of correlated genes and identifies their relationship to specific lineages in mixed cell populations. This protocol details formatting, optimization of run parameters, and interpretation of results following implementation of ICLite. For complete details on the use and execution of this protocol, please refer to Camiolo et al. (2021).
| S-EPMC8488363 | biostudies-literature
Project description:In this study single cell RNA-Seq data was used to train a deconvolution algorithm. The algorithm was validated on paired bulk RNA-Seq profiles.
| EGAS00001006723 | EGA