GenesorteR: Feature Ranking in Clustered Single Cell Data
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE136950 | GEO | 2020/09/30
REPOSITORIES: GEO
Similar Datasets
Project description:genesorteR: Feature Ranking in Clustered Single Cell Data
| PRJNA564101 | ENA
Project description:Abstract: Transcriptome analysis was applied to characterize the physiological activities of Pseudomonas aeruginosa grown for three days in drip-flow biofilm reactors. Conventional applications of transcriptional profiling often compare two paired data sets that differ in a single experimentally controlled variable. In contrast this study obtained the transcriptome of a single biofilm state, ranked transcript signals to make the priorities of the population manifest, and compared rankings for a priori identified physiological marker genes between the biofilm and published data sets.
2010-11-20 | GSE22164 | GEO
Project description:Abstract: Transcriptome analysis was applied to characterize the physiological activities of Pseudomonas aeruginosa grown for three days in drip-flow biofilm reactors. Conventional applications of transcriptional profiling often compare two paired data sets that differ in a single experimentally controlled variable. In contrast this study obtained the transcriptome of a single biofilm state, ranked transcript signals to make the priorities of the population manifest, and compared rankings for a priori identified physiological marker genes between the biofilm and published data sets. Two drip flow biofilm conditions with three replicates each: (1) baseline control at 72hrs, (2) no treatment for 12 hours past baseline. Data from these two conditions were pooled
2010-11-20 | E-GEOD-22164 | biostudies-arrayexpress
Project description:We report an automated microwell array platform for single cell RNA-seq with significantly improved performance over previous implementations. We demonstrate cell capture efficiencies of >50%, compatibility with commercially available barcoded mRNA capture beads, and parallel expression profiling from thousands of individual cells. We apply our system to comprehensively assess heterogeneity in gene expression of patient-derived glioma neurospheres and uncover subpopulations similar to those observed in human glioma tissue.
2016-08-13 | GSE85575 | GEO
Project description:The ability to interrogate circulating tumor cells (CTC) and disseminated tumor cells (DTC) is restricted by the small number detected and isolated (typically <10). We wanted to determine if a commercially available technology could provide a transcriptomic profile of a single prostate cancer (PCa) cell. We clonally selected and cultured a single passage of cell cycle synchronized C4-2B PCa cells. Ten sets of single, 5-, or 10-cells were isolated using a micromanipulator under direct visualization with an inverted microscope. Additionally, we analyzed 10 individual DTC isolated from each of 2 patients with metastatic PCa. We have shown that a transcriptomic profile can be obtained from a single cell using commercially available technology. As expected, fewer amplified genes are detected from a single-cell sample than from pooled cell samples, but this method can be used to obtain a transcriptomic profile from DTC isolated from the bone marrow of patients with PCa. Custom Agilent 44K whole human genome expression oligonucleotide microarrays were used to profile clonally selected and cultured single passage of cell cycle synchronized C4-2B PCa cells isolated using a micromanipulator under direct visualization with an inverted microscope into ten sets of single, 5-, or 10-cells. Single disseminated tumor cells were isolated from bone marrow (BM) samples of two advanced prostate cancer patients. Essentially, a two-step selection process was employed, in which anti-CD45 and anti-CD61 conjugated to immunomagnetic beads were used for negative selection, and anti-HEA was used for positive selection. Cells were then fluorescently stained for BerEP4, counter stained with RPE anti-CD45, and individually selected (10 single cells each per patient) under fluorescent light using a micropipette system for further analysis. RNA was amplified using the WT-Oviation one-direct system and hybridization against a common reference pool of prostate tumor cell lines. Data from C42B cell data and data from single cells isolated from the bone marrow of patients were normalized and analyzed separately.
2013-02-19 | E-GEOD-38416 | biostudies-arrayexpress
Project description:Polyploidization drives regulatory and phenotypic innovation. How the merger of different genomes contributes to polyploid development is a fundamental issue in evolutionary developmental biology and breeding research. Clarifying this issue is challenging because of genome complexity and the difficulty in tracking stochastic subgenome divergence during development. Recent single-cell sequencing techniques enabled probing subgenome divergent regulation in the context of cellular differentiation. However, analyzing single-cell data suffers from high error rates due to high-dimensionality, noise, and sparsity, and the errors stack up in polyploid analysis due to the increased dimensionality of comparisons between subgenomes of each cell, hindering deeper mechanistic understandings. Here, we developed a quantitative computational framework, pseudo-genome divergence quantification (pgDQ), for quantifying and tracking subgenome divergence directly at the cellular level. Further comparing with cellular differentiation trajectories derived from scRNA-seq data allowed for an examination of the relationship between subgenome divergence and the progression of development. pgDQ produces robust results and is insensitive to data dropout and noise, avoiding high error rates due to multiple comparisons of genes, cells, and subgenomes. A statistical diagonostic approach is proposed to identify genes that are central to subgenome divergence during development, which facilitates the integration of different data modalities, enabling the identification of factors and pathways that mediate subgenome-divergent activity during development. Case studies demonstrated that applying pgDQ to single cell and bulk tissue transcriptome data promotes a systematic and deeper understanding of how dynamic subgenome divergence contributes to developmental trajectories in polyploid evolution.
2024-09-20 | GSE224228 | GEO
Project description:Background:; Cowpea (Vigna unguiculata L. Walp) is an important food and fodder legume of the semiarid tropics and subtropics worldwide, especially in sub-Saharan Africa. High density genetic linkage maps are needed for marker assisted breeding but are not available for cowpea. A single feature polymorphism (SFP) is a microarray-based marker which can be used for high throughput genotyping and high density mapping. Results:; Here we report detection and validation of SFPs in cowpea using a readily available soybean (Glycine max) genome array. Robustified projection pursuit (RPP) was used for statistical analysis using RNA as a surrogate for DNA. Using a 15% outlying score cut-off, 1058 potential SFPs were enumerated between two parents of a recombinant inbred line (RIL) population segregating for several important traits including drought tolerance, Fusarium and brown blotch resistance, grain size and photoperiod sensitivity. Sequencing of 25 putative polymorphism-containing amplicons yielded a SFP probe set validation rate of 68%. Conclusions:; We conclude that the Affymetrix soybean genome array is a satisfactory platform for identification of some 1000âs of SFPs for cowpea. This study provides an example of extension of genomic resources from a well supported species to an orphan crop. Presumably, other legume systems are similarly tractable to SFP marker development using existing legume array resources. SUBMITTER_CITATION: Detection and validation of single feature polymorphisms in cowpea (Vigna unguiculata L. Walp) using a soybean genome array Sayan Das, Prasanna R. Bhat, Chinta Sudhakar, Jeffrey D. Ehlers, Steve Wanamaker, Philip A. Roberts, Xinping Cui, Timothy J. Close BMC Genomics 2008, 9:107 Experiment Overall Design: Expression data were generated by hybridizing cowpea cRNA to the soybean genome array. A statistical method called robustified projection pursuit (RPP) was used for Single Feature Polymorphism(SFP) analysis. Only the values from the PM probes were utilized. The use of RNA as a surrogate for genomic DNA eliminated interference from highly repetitive DNA as a technical impediment to SFP detection. An important aspect of the RPP method is that it first utilizes a probe set level analysis to identify SFP-containing probe sets and then chooses individual probes from within each SFP-containing probe set. The net result is the identification of probes that directly overlay polymorphic sequences. Experiment Overall Design: Separate comparisons were made between two genotypes (with two replicates each) for unstressed and drought stressed treatments, resulting in two SFP lists. In the context of SFPs, there is no necessity to have separate stress and control lists; in fact it would be simpler and less costly to have only one SFP list from highly complex RNA made by blending stressed and unstressed RNA. In our case, two separate lists were available as a consequence of another study not described here which compared gene expression patterns in stressed and control plants (data not shown). At 15% outlying score cut-off, we detected 488 SFP probes in stressed and 661 SFP probes in unstressed treatments. The union of these two lists contained 1058 SFP probes and the intersection contained 91. A total of 37 primer pairs targeting 37 putative SFP probe sets were initially tested, of which 25 yielded single amplicons of the expected sizes from both parents. These 25 amplicons targeted 14 probe sets selected from the intersection of the two SFP probe set lists and 11 from the remaining SFP probe sets. 9 of the 14 SFP probe sets (64%) from the intersection list were validated at the DNA sequence level and 8 of the other 11 (73%) were validated.
2008-04-07 | E-GEOD-10284 | biostudies-arrayexpress
Project description:Single-cell transcriptomics allows the identification of cellular types, subtypes and states through cell clustering. In this process, similar cells are grouped before determining co-expressed marker genes for phenotype inference. The performance of computational tools is directly associated to their marker identification accuracy, but the lack of an optimal solution challenges a systematic method comparison. Moreover, phenotypes from different studies are challenging to integrate, due to varying resolution, methodology and experimental design. In this work we introduce matchSCore (https://github.com/elimereu/matchSCore), a measure to fastly match cell populations across tools, experiments and technologies. We compared 14 computational methods and evaluated their accuracy in clustering and gene marker identification in simulated data sets. Further, we used matchSCore to project cell type identities across mouse or human cell atlas projects. Despite originated from different technologies, cell populations could be matched across datasets, allowing the assignment of clusters to reference maps and their annotation.
2021-04-30 | GSE114138 | GEO
Project description:Single-cell RNA-seq (scRNA-seq) can be used to characterize cellular heterogeneity in thousands of cells. The reconstruction of a gene network based on coexpression patterns is a fundamental task in scRNA-seq analyses, and the mutual exclusivity of gene expression can be critical to understand such heterogeneity. Here, we propose an approach for detecting communities from a gene network constructed on the basis of coexpression properties. The community-based comparison of multiple coexpression networks enables the identification of functionally related gene clusters that cannot be fully captured through differential gene expression-based analysis. We also developed a novel metric referred to as the exclusively expressed index (EEI) that identifies mutually exclusive gene pairs from sparse scRNA-seq data. EEI quantifies and ranks the exclusive expression levels of all gene pairs from binary expression patterns while maintaining robustness against a low sequencing depth. We applied our methods to glioblastoma scRNA-seq data and found that gene communities are partially conserved after serum stimulation despite a considerable number of differentially expressed genes. We also demonstrate that the identification of mutually exclusive gene sets with EEI can improve the sensitivity of capturing cellular heterogeneity. Our methods complement existing approaches and provide new biological insights from even a large sparse dataset in the single-cell analysis field.
2021-07-22 | GSE144623 | GEO
Project description:Analysis of gene expression differences in relationship to dominance rank in female rhesus macaques. RNA obtained from 4 cell populations (helper T cells, cytotoxic T cells, monocytes, and B cells) from isolated peripheral blood mononuclear cells sampled from 5 adult female rhesus macaques across 5 different social groups and 5 different dominance ranks [dominance ranks : 1 = high, 5 = low]
2012-02-01 | E-GEOD-34127 | biostudies-arrayexpress