scIsoAgent

Runs project-specific ONT-only FLAIR transcriptome generation, optional multi-sample combine, quantification, and post-quantification single-cell matrix recovery with recover_sc_matrix.py.

Summarizes completed long-read single-cell RNA-seq runs using deep results-style integrative writing, with emphasis on post-annotation isoform analysis, sequence-model outputs, transcript-level events, gene-function interpretation, and literature-grounded condition-specific consequences.

Uses Orthrus transcript-sequence embeddings plus pretrained regression and classification heads to predict isoform-level functional properties for one gene at a time in a persistent tmux shell session.

Draws gene-level sashimi plots from per-sample BAM files after downstream annotation by extracting group-specific cell barcodes from a Seurat object, subsetting BAMs by barcode tag, building ggsashimi input tables, and iteratively plotting one gene at a time in tmux.

Adds the merged isoform count assay and isoform UMAP from a merged isoform Seurat object into a merged gene Seurat object, then saves a new combined Seurat RDS for joint downstream gene-plus-isoform analysis.

Performs downstream cell type annotation for the project in one persistent tmux R session, including cluster UMAP review, cluster-level marker discovery for gene and isoform assays, optional automatic annotation with ScType, optional manual cluster renaming, celltype-level differential analysis, heatmaps, enrichment, and cell composition summaries.

Performs differential transcript usage analysis in one persistent tmux R session using a Seurat object with an isoform assay, including DTU FASTA preparation, pseudobulk aggregation by sample and cell type, GTF subsetting, IsoformSwitchAnalyzeR object initialization, filtering, ORF annotation, DEXSeq-based DTU testing, result export, and switch plotting for user-selected genes.

Draws gene or isoform feature expression on a chosen reduction from a Seurat RDS using SCP::FeatureDimPlot, and for repeated plotting must keep one R session open so the object is loaded once and the user can iteratively request more plots.

Explores isoforms of interest in one persistent tmux R session, including pseudobulk-based isoform counting per gene, ranking genes by isoform complexity, feature-plot exploration of selected genes, celltype-level violin and heatmap summaries for selected isoforms, and IsoVis input export.

Builds per-sample isoform-level Seurat objects from transcript barcode-feature-matrix triplets, plots isoform QC metrics, subsets each isoform object to the cells retained in the matched gene-level QC Seurat object, and saves the per-sample isoform Seurat RDS outputs.

Runs SQANTI3 isoform classification from the terminal and then summarizes SQANTI structural categories, subcategories, coding status, and celltype-specific isoforms in one persistent tmux R session using the merged Seurat object and pseudobulk isoform summaries.

Merges per-sample isoform-level Seurat objects produced by generate-iso-obj, previews sample structure in UMAP, and optionally applies Harmony-based batch correction before saving the final merged isoform Seurat object.

Merges per-sample Seurat objects after QC and doublet removal, generates PCA and Harmony elbow plots for user-confirmed dimension choices, previews batch structure in UMAP, and optionally performs Harmony-based batch correction before saving the final merged object.

Profiles isoform diversity in one persistent tmux R session using a Seurat object with an isoform assay, including celltype-specific subsetting, entropy-based diversity analysis with find_diversity.R, entropy heatmaps, high- and low-entropy gene summaries, gene-specific entropy plots, and per-cell isoform proportion pie charts.

Orchestrates project-specific downstream quality control for long-read single-cell RNA-seq after empty-droplet removal, including metric plotting, user-confirmed filtering thresholds, dimensional reduction, clustering, doublet detection, and QC summary outputs.

Runs project-specific empty-droplet removal for long-read single-cell RNA-seq count matrices using the remove_droplets.r script, producing QC plots, summary tables, and Seurat objects before and after droplet filtering.

Prepares core downstream resource files for long-read single-cell RNA-seq analysis in this project, especially isoform-to-gene symbol dictionaries and gene-count matrices converted from ENSG IDs to gene symbols.

Splits the merged geneSymbol gene-count matrix produced after resource setup into per-sample matrices by parsing sample names from barcode prefixes before empty-droplet removal.

Predicts transcript translation initiation and termination sites with TranslationAI from transcript FASTA, then summarizes gene-specific TIS/TTS coordinates in one persistent tmux R session.

Predicts transcript subcellular localization with DeepLocRNA from transcript FASTA, then summarizes localization classes and gene-specific transcript localization scores in one persistent tmux R session.

Orchestrates project-specific downstream long-read single-cell RNA-seq analysis after upstream quantification, including resource setup, droplet cleanup, QC, sample merging, isoform matrix integration, cell type identification, isoform interpretation, isoform classification, isoform diversity profiling, and differential transcript usage.

Runs project-specific IsoQuant single-cell transcript discovery and quantification for ONT aligned-BAM input or PacBio CCS BAM input, with explicit run organization, barcode route, and command confirmation.

Runs project-specific bambu single-cell transcript discovery and quantification for ONT aligned-BAM input or PacBio raw FASTQ input, with explicit run organization, chemistry handling, and command confirmation.

Runs project-specific BLAZE demultiplexing for ONT long-read single-cell RNA-seq, including barcode and UMI detection, whitelist generation, chemistry confirmation, and demultiplexed FASTQ production.

Orchestrates project-specific upstream long-read single-cell RNA-seq analysis with explicit ONT vs PacBio branching, step ordering, dependency checks, resume rules, and command confirmation before execution.

Runs project-specific minimap2 splice-aware alignment for long-read RNA-seq, primarily for ONT single-cell reads after BLAZE, with explicit preset selection, tag-preservation handling, and command confirmation.

Runs project-specific samtools sorting, indexing, quick structural validation, and optional flagstat summary to prepare long-read RNA alignments for downstream quantification.

Orchestrates the full long-read single-cell RNA-seq workflow for this project, covering both upstream processing and downstream analysis. Use when the user wants a single entry skill for ONT or PacBio long-read single-cell RNA-seq from raw data through downstream interpretation.