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Testing & security AI Skills
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markersfinder
Flexible marker finding process that wraps Seurat's FindMarkers function for custom group comparisons beyond simple cluster-vs-all analysis. Unlike ClusterMarkers (all-vs-all cluster comparisons), MarkersFinder...
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GitHub
sampleinfo
The SampleInfo process is the pipeline entry point that reads sample metadata files, performs statistical analyses, and generates visualization reports.
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GitHub
metabolicpathwayactivity
Calculates pathway activity scores for metabolic pathways across different cell groups and subsets. This process quantifies the metabolic activity of each pathway per group, generating visualizations (heatmaps...
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GitHub
seuratclusterstats
Generates comprehensive cluster statistics and visualizations for Seurat objects, including dimension reduction plots, gene expression visualizations, cluster quality metrics, and clustree diagrams. This proces...
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GitHub
topexpressinggenesofallcells
Identifies and visualizes the top expressing genes per cluster across ALL cells (before T/B cell selection), followed by pathway enrichment analysis. Provides initial overview of all cell populations by highlig...
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GitHub
cdr3aaphyschem
Analyzes physicochemical properties of CDR3 amino acid sequences to understand biochemical characteristics of T-cell receptor repertoires. Performs regression analysis between two cell groups at different CDR3...
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GitHub
metabolicinput
Pass-through process that prepares Seurat object for metabolic landscape analysis. Routes the processed Seurat object to downstream metabolic analysis processes (MetabolicExprImputation, MetabolicPathwayActivit...
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GitHub
loadingrnafromseurat
Load pre-existing Seurat objects into the immunopipe pipeline instead of starting from raw count matrices via SampleInfo. This enables analysis on already processed single-cell RNA-seq data stored in Seurat R o...
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GitHub
screpcombiningexpression
Combine scTCR/BCR repertoire data with scRNA-seq expression data using `scRepertoire::combineExpression()`. This process integrates immune receptor information (CDR3 sequences, V(D)J genes, clonotypes) into a S...
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GitHub
seuratsubclustering
Performs fine-grained re-clustering on specific subsets of cells (e.g., individual clusters, cell types, or custom subsets). Unlike `Seurat::FindSubCluster` which only finds subclusters within a single cluster,...
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GitHub
topexpressinggenes
Identifies and visualizes the top expressing genes per cluster in T/B cells, followed by pathway enrichment analysis. Provides quick cluster characterization by highlighting the most highly expressed genes and...
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GitHub
tessa
TESSA (TCR and Expression Joint Clustering) is a Bayesian model that integrates T-cell receptor (TCR) sequence profiling with transcriptomes of T cells. It maps the functional landscape of the TCR repertoire by...
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GitHub
seuratpreparing
Load, prepare, and apply quality control (QC) to single-cell RNA-seq data using Seurat. Performs data loading, QC filtering, normalization, and multi-sample integration. This is a core preprocessing process tha...
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GitHub
screploading
Load single-cell TCR-seq or scBCR-seq data from various formats into a scRepertoire-compatible object. This process reads VDJ (variable, diversity, joining) receptor contig data from multiple single-cell sequen...
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GitHub
torbcellselection
Separates T and non-T cells or B and non-B cells from a mixed cell population. Uses either clonotype percentage from VDJ data, indicator gene expression (CD3 markers for T cells, CD19/CD20 for B cells), custom...
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GitHub
seuratclustering
Performs unsupervised clustering on single-cell RNA-seq data using Seurat. This process finds nearest neighbors, computes UMAP for visualization, and applies Louvain/Leiden algorithms to identify cell clusters....
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GitHub
metabolicexpimputation
Imputes missing/dropout values in scRNA-seq expression data to improve metabolic pathway analysis. This process handles sparsity common in single-cell RNA sequencing data by filling in zero values using advance...
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GitHub
celltypeannotation
Annotates cell clusters with biological cell type labels using multiple methods: direct assignment, ScType, scCATCH, hitype, or CellTypist. This process is essential for interpreting clustering results by assig...
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GitHub
scrnametaboliclandscape
Comprehensive metabolic landscape analysis pipeline for scRNA-seq data. This is an all-in-one process group performing complete metabolic pathway analysis including expression imputation, feature selection, pat...
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GitHub
seuratclusteringofallcells
Performs coarse clustering on ALL cells (including T cells, B cells, and non-T/B cells) before cell type selection. This process identifies broad cell populations to enable subsequent T/B cell selection via `TO...
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GitHub
clustermarkersofallcells
Finds marker genes for clusters of ALL cells before T/B cell selection. This process identifies differentially expressed genes across unsupervised clusters to help identify broad cell types (T cells, B cells, M...
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GitHub
clustermarkers
Finds differentially expressed genes (markers) for clusters of T/B cells using Seurat's FindMarkers function. Performs statistical testing between clusters, identifies cluster-defining genes, and automatically...
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GitHub
pseudobulkdeg
Performs pseudo-bulk differential gene expression analysis using DESeq2 or edgeR. Aggregates single-cell counts to sample-level pseudo-bulk data, then identifies differentially expressed genes between condition...
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