Perform differential expression analysis on AnnData single-cell data to discover marker genes.
npx skills add https://github.com/TianGzlab/OmicsClaw --skill spatial-de
Please help me install this Agent Skill. Skill: spatial-de Source: https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/sc-de Command: npx skills add https://github.com/TianGzlab/OmicsClaw --skill spatial-de
This Skill automates the identification of marker genes in single-cell RNA sequencing data, which is crucial for understanding cell populations and biological states, preventing users from manually configuring complex statistical tests with inconsistent parameters.
Use the sc-de skill to find marker genes for all clusters in the file 'annotated.h5ad'.
High-intent search queries and answers about installing and using this skill.
To find marker genes in single-cell RNA-seq data, you can use this Skill to automate differential expression analysis on AnnData objects. It applies statistical methods like Wilcoxon, t-test, MAST, and DESeq2 pseudo-bulk to identify cluster-defining or condition-specific genes.
The best way to perform differential expression analysis between two cell groups is using methods like MAST or DESeq2 pseudo-bulk. This Skill automates these statistical tests on AnnData objects to accurately identify distinguishing marker genes.
Yes, you can use DESeq2 pseudo-bulk for single-cell marker gene discovery. This Skill supports DESeq2 pseudo-bulk alongside Wilcoxon, t-test, and MAST methods to perform robust differential expression analysis on AnnData objects.
To visualize marker genes from an AnnData object after running differential expression, this Skill automatically generates essential plots. It creates Volcano plots, MA plots, and dotplots to clearly interpret the identified markers.
Yes, this Skill requires AnnData objects as the input format for single-cell differential expression. You must provide your annotated single-cell RNA-seq data in an h5ad file to identify marker genes successfully.
You should use pseudo-bulk DESeq2 instead of Wilcoxon when comparing two experimental conditions to mitigate drop-out effects in single-cell data. This Skill supports both approaches for cluster-vs-rest or two-group comparisons on AnnData objects.
StarlitnightlyPreprocess TCGA bulk data into annotated AnnData objects with expression and clinical metadata.
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ya-wayIdentify cluster-specific marker genes and annotate cell types in single-cell RNA-seq data.
HolobiomicsLabPerform end-to-end differential expression analysis on bulk RNA-seq count data with PyDESeq2 workflows.
