Bioconductor Cotan
Statistical and computational method to analyze the co-expression of gene pairs at...
Install
npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-cotan -g -a claude-code -y- Repository
- bioMate-AI/biomate-bioconductor-kb793
- Category
- Engineering
- Updated
- 5mo ago
- License
- See repository
- Context cost
- Lightabout 1,490 tokens
- Runs scripts
- No, instructions only
What it does
Statistical and computational method to analyze the co-expression of gene pairs at single cell level. It provides the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts' distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can effectively assess the correlated or anti-correlated expression of gene pairs. It provides a numerical index related to the correlation and an approximate
COTAN
Workflows
Standard Workflow
Clean raw single-cell UMI count datasets by identifying and removing doublets, dead cells, and low-efficiency cells.
library(COTAN)
library(zeallot)
# Resolve operator precedence conflicts
conflicted::conflict_prefer("%<-%", "zeallot")
# 1. Create COTAN object and initialize metadata
rawDataset <- data.frame(Gene1 = c(1, 0, 5), Gene2 = c(0, 2, 0)) # Mock raw counts
obj <- COTAN(raw = rawDataset)
obj <- initializeMetaDataset(
obj,
GEO = "GSM2861514",
sequencingMethod = "Drop_seq",
sampleCondition = "mouse_cortex_E17.5"
)
# 2. Add condition metadata
data(vignette.cells.origin)
obj <- addCondition(obj, condName = "origin", conditions = vignette.cells.origin)
# 3. Drop problematic cells and run cleaning
obj <- dropGenesCells(obj, cells = c())
obj <- clean(obj)
# 4. Generate diagnostic plots
c(pcaCellsPlot, pcaCellsData, genesPlot, UDEPlot, nuPlot, zoomedNuPlot) %<-% cleanPlots(obj)
Note on inputs/outputs: Input is a raw UMI count matrix; output is a cleaned and filtered COTAN object with estimated cell efficiency (nu) parameters.
Cell Clustering And Dea
Cluster cells based on expression uniformity and perform differential expression analysis (DEA) on the resulting clusters.
library(COTAN)
# 1. Load pre-calculated clusterizations
data("vignette.split.clusters", package = "COTAN")
vignette.split.clusters <- asClusterization(
vignette.split.clusters,
allCells = getCells(obj)
)
# 2. Execute Differential Expression Analysis on clusters
vignette.split.coexDF <- DEAOnClusters(obj, clusters = vignette.split.clusters)
