Results 11 to 20 of about 86,859 (350)
scBoolSeq: Linking scRNA-seq statistics and Boolean dynamics.
PLoS Computational Biology, 2023 Boolean networks are largely employed to model the qualitative dynamics of cell fate processes by describing the change of binary activation states of genes and transcription factors with time.
Gustavo Magaña-López +3 more
doaj +6 more sources
deMULTIplex2: robust sample demultiplexing for scRNA-seq [PDF]
Genome Biology, 2023 AbstractSingle-cell sample multiplexing technologies function by associating sample-specific barcode tags with cell-specific barcode tags, thereby increasing sample throughput, reducing batch effects, and decreasing reagent costs. Computational methods must then correctly associate cell-tags with sample-tags, but their performance deteriorates rapidly ...
Qin Zhu +2 more
openaire +5 more sources
Second batch of raw seq. data. Raw sequencing (scRNA-Seq, CITE-Seq, scTCR-seq) data available on the second batch of patients (36-70).
Van Camp
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DRscDB: A single-cell RNA-seq resource for data mining and data comparison across species
Computational and Structural Biotechnology Journal, 2021 With the advent of single-cell RNA sequencing (scRNA-seq) technologies, there has been a spike in studies involving scRNA-seq of several tissues across diverse species including Drosophila.
Yanhui Hu +11 more
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Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review
Genomics, Proteomics & Bioinformatics, 2022 Single-cell RNA sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands of single cells simultaneously.
Matthew Brendel +5 more
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scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles. [PDF]
, 2020 Simultaneous measurements of transcriptomic and epigenomic profiles in the same individual cells provide an unprecedented opportunity to understand cell fates. However, effective approaches for the integrative analysis of such data are lacking.
Jin, Suoqin, Nie, Qing, Zhang, Lihua
core +1 more source
Cerebro: Interactive visualization of scRNA-seq data [PDF]
Bioinformatics, 2019 AbstractSummaryDespite the growing availability of sophisticated bioinformatic methods for the analysis of single-cell RNA-seq data, few tools exist that allow biologists without bioinformatic expertise to directly visualize and interact with their own data and results.
Hillje, Roman +2 more
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Gene representation in scRNA-seq is correlated with common motifs at the 3′ end of transcripts
Frontiers in Bioinformatics, 2023 One important characteristic of single-cell RNA sequencing (scRNA-seq) data is its high sparsity, where the gene-cell count data matrix contains high proportion of zeros.
Xinling Li, Greg Gibson, Peng Qiu
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Multiplexed single-cell RNA-sequencing of mouse thymic and splenic samples
STAR Protocols, 2022 Summary: Multiplexed single-cell RNA-sequencing (scRNA-seq) enables investigating several biological samples in one scRNA-seq experiment. Here, we use antibodies tagged with a hashtag oligonucleotide (Ab-HTO) to label each sample, and 10× Genomics ...
Saran Pankaew +6 more
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Boosting scRNA-seq data clustering by cluster-aware feature weighting
BMC Bioinformatics, 2021 Background The rapid development of single-cell RNA sequencing (scRNA-seq) enables the exploration of cell heterogeneity, which is usually done by scRNA-seq data clustering.
Rui-Yi Li, Jihong Guan, Shuigeng Zhou
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