Models

Model derived from U2020.1 by fitting the scaling factors for matching TiMet data set for wild-type and clock mutants, in absolute units.

Model derived from U2019.1, in which the way the PRR genes are regulated is modified. Repression mechanism introduced Instead of activation between the PRRs for producing the wave of expression. This is inspired in the result of three models P2012, F2014 and F2016. P2012 introduced TOC1 repression in earlier genes relative to its expression. F2014 introduced also the backward repression of PRR9 |-- PRR7 |--- PRR5, TOC1. However little attention was given to why there is a sharper expression ...

Model written in Antimony human-readable language and then translate into SBML using Tellurium

Model written in Antimony human-readable language, Model used in Pokhilko et al 2012

autogenerated equation listing from the SBML of U2020.3, as a .PDF file

autogenerated equation listing from the SBML of U2019.3, as a .PDF file

NLRP3 inflammasome activation

Thrombotic complications and coagulopathy in COVID-19

Kynurenine synthesis pathway

TGF beta signalling

The role of the interaction between the SARS-CoV-2 Spike protein and the renin-angiotensin pathway, in particular human ACE2 in pulmonary blood pressure regulation

The Interferon-lambda (IFNL) map describes the action of the drug candidate IFNL on intra- and intercellular signal transduction under SARS-CoV-2.

The relation of the interferon 2 pathway and SARS-CoV-2.

The pathway of heme metabolism under COVID-19, involving Orf3a and Orf9c

The impact of SARS-CoV-2 on the apoptosis pathway

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.

Here is the detailed R script to generate the input needed by scSynO for synthetic cell generation and classification model training.

The code that can be embedded into any other Seurat data processing workflow is:

cell_expression_target_cluster <- as.matrix(GetAssayData(seuratobject, slot = "data")[, WhichCells(seuratobject, ident = "target_cluster_number")]) cell_expression_all_other_clusters <- as.matrix(GetAssayData(seuratobject, slot = "data")[, WhichCells(seuratobject, ident = ...

Single nuclei transcriptomics data as .csv files from the Allen Brain atlas data set of mus musculus (https://celltypes.brain-map.org/) have been utilized as an input for scSynO. The underlying analysis is part of the manuscript entitled "Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling". Data anaylsis and visalizations were mainly generated with the Seurat R package (https://satijalab.org/seurat/archive/v3.2/spatial_vignette.html)

U2019.3 that simulates light condition with ISSF

U2020.2 that simulates light condition with ISSF

U2019.1 that simulates light condition with ISSF

U2019.3 that simulates light condition with ISSF

U2019.2 that simulates light condition with ISSF

U2019.1 that simulates light condition with ISSF