Pages that link to "Q34500228"
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The following pages link to ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis (Q34500228):
Displaying 50 items.
- Design and computational analysis of single-cell RNA-sequencing experiments (Q26751087) (← links)
- Single-cell Transcriptome Study as Big Data (Q26766140) (← links)
- Single-cell transcriptome sequencing: recent advances and remaining challenges (Q26767451) (← links)
- Single-Cell Transcriptomics Bioinformatics and Computational Challenges (Q28073365) (← links)
- Single-cell gene expression profiling and cell state dynamics: collecting data, correlating data points and connecting the dots (Q28078834) (← links)
- Exploring viral infection using single-cell sequencing (Q28078858) (← links)
- Discrete distributional differential expression (D3E)--a tool for gene expression analysis of single-cell RNA-seq data (Q31050441) (← links)
- FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data (Q31123178) (← links)
- Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data. (Q31133242) (← links)
- A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. (Q31146507) (← links)
- Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning (Q31171256) (← links)
- SCALE: modeling allele-specific gene expression by single-cell RNA sequencing. (Q33608955) (← links)
- Single-cell RNA-sequencing of the brain (Q33780520) (← links)
- Single-Cell RNA Sequencing of Human T Cells (Q36175926) (← links)
- Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference (Q36199624) (← links)
- CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data (Q36325956) (← links)
- Probabilistic modeling of bifurcations in single-cell gene expression data using a Bayesian mixture of factor analyzers. (Q36371547) (← links)
- Normalizing single-cell RNA sequencing data: challenges and opportunities (Q36371785) (← links)
- Computational approaches for interpreting scRNA-seq data (Q36376343) (← links)
- Single-cell profiling of human megakaryocyte-erythroid progenitors identifies distinct megakaryocyte and erythroid differentiation pathways (Q36867216) (← links)
- Dpath software reveals hierarchical haemato-endothelial lineages of Etv2 progenitors based on single-cell transcriptome analysis (Q37642701) (← links)
- Exploiting single-cell expression to characterize co-expression replicability (Q38392728) (← links)
- Delineating biological and technical variance in single cell expression data (Q38675040) (← links)
- Dr.seq2: A quality control and analysis pipeline for parallel single cell transcriptome and epigenome data (Q38696633) (← links)
- Single-Cell Genomics: Approaches and Utility in Immunology (Q38769681) (← links)
- JingleBells: A Repository of Immune-Related Single-Cell RNA-Sequencing Datasets (Q38833533) (← links)
- Disentangling neural cell diversity using single-cell transcriptomics (Q38939764) (← links)
- Revealing the vectors of cellular identity with single-cell genomics (Q39002947) (← links)
- Understanding development and stem cells using single cell-based analyses of gene expression. (Q39066301) (← links)
- switchde: inference of switch-like differential expression along single-cell trajectories (Q39067532) (← links)
- Bioconductor workflow for single-cell RNA sequencing: Normalization, dimensionality reduction, clustering, and lineage inference. (Q40059533) (← links)
- Splatter: simulation of single-cell RNA sequencing data (Q41152743) (← links)
- Impact of sequencing depth and read length on single cell RNA sequencing data of T cells. (Q41922550) (← links)
- Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures (Q42287007) (← links)
- Single-cell transcriptome analysis of fish immune cells provides insight into the evolution of vertebrate immune cell types (Q42317732) (← links)
- Modelling, inference and big data in biophysics. (Q42352408) (← links)
- Controlling for Confounding Effects in Single Cell RNA Sequencing Studies Using both Control and Target Genes (Q42670551) (← links)
- SINCERITIES: Inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles (Q42696604) (← links)
- Measuring Signaling and RNA-Seq in the Same Cell Links Gene Expression to Dynamic Patterns of NF-κB Activation. (Q42776217) (← links)
- Using neural networks for reducing the dimensions of single-cell RNA-Seq data (Q42777960) (← links)
- Network embedding-based representation learning for single cell RNA-seq data (Q42777982) (← links)
- Global and targeted approaches to single-cell transcriptome characterization (Q42778129) (← links)
- Accounting for technical noise in differential expression analysis of single-cell RNA sequencing data (Q42778181) (← links)
- f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq. (Q46726359) (← links)
- Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists (Q47104977) (← links)
- A general and flexible method for signal extraction from single-cell RNA-seq data. (Q47559173) (← links)
- A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data. (Q47593568) (← links)
- Missing data and technical variability in single-cell RNA-sequencing experiments (Q47625325) (← links)
- Application of single-cell sequencing in human cancer (Q47857737) (← links)
- The promise of single-cell RNA sequencing for kidney disease investigation. (Q48001767) (← links)