Application of the LoRAS oversampling approach on single-cell/single-nuclei data to annotate/identify specific cell populations in new data based on previously, manually curated data.
Investigation: 1 hidden item
Study: 1 hidden item
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Models: Data and Jupyter notebooks for our analysis on ..., Data and Jupyter notebooks for our analysis on ..., R script for scSynO usage of the Allen Brain At..., R script to generate the preprocessing input fo..., Unix script for the kallisto bustools processin...
SOPs: No SOPs
Validation of the sc-SynO model for the second use case of proliferative cardiomyocytes annotation. a) UMAP representation of the manually clustered single-nuclei dataset of Linscheid et al. (2019) Precicted cells of sc-SynO are highlighted in blue (based on top 20 selected features in the training model), red (based on top 100 selected features in the training model) cells not chosen are grey. b) UMAP representation of the manually clustered dataset of Vidal et al. (2020). PPrecicted cells of
Validation of the sc-SynO model for the first use case of cardiac glial cell annotation. UMAP representation of the manually clustered Bl6 dataset of Wolfien et al. (2020) Precicted cells of sc-SynO are highlighted in blue, cells not chosen are grey. UMAP representation of the manually clustered dataset of Vidal (2019). Precicted cells of sc-SynO are highlighted in blue, cells not chosen are grey. Average expression of the respective top five cardiac glial cell marker genes for both validation
Visualization of the workflow demonstrating a step-by-step explanation for a sc-SynO analysis. a) Several or one snRNA-Seq or scRNA-Seq fastq datasets can be used as an input. Here, we identify our cell population of interest and provide raw or normalized read counts of this specific population to sc-SynO for training. b) Further information for cluster annotation and processed count data are serving as input for the core algorithm. c) Based on the data input, we utilize the LoRAS synthetic
Here, we describe the index file generation of the mm10 genome, the genome alignment with kallisto, and quantification with bustools to obtain the used spliced / unspliced transcript input.
Creator: Markus Wolfien
Submitter: Markus Wolfien
Model type: Not specified
Model format: Not specified
Environment: Not specified
Organism: Mus musculus
Investigations: 1 hidden item
Studies: 1 hidden item