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STAR Suite

STAR Suite updates the original STAR aligner by integrating four modules — STAR-perturb, STAR-Flex, STAR-SLAM, and TranscriptVB — to provide complete internal C/C++ pipelines for bulk RNA-seq, scRNA-seq, Perturb-seq, 10x Flex, and SLAM-seq. The integration results in substantial speedups (2.1–2.4x for bulk RNA-seq, 3.2–6.1x for Perturb-seq) and a simplified toolchain that can be installed through pre-compiled binaries for researchers and agents. No new external dependencies are required; the suite is built entirely with the existing STAR toolchain and vendored components. This is a drop-in replacement for the STAR aligner.

STAR Suite supports partial compilation: build only the module/tool targets you need instead of building the full suite every time.

Agent quickstart: see AGENTS.md for repo-specific guardrails, tests, and recent changes.

Core Additions over STAR 2.7.11b

  • Speedup: Bulk RNA-seq 2.1–2.4x faster than external stepwise pipelines; Perturb-seq 3.2–6.1x faster than Cell Ranger 9 with near-identical parity.
  • Batch Mode (--batchMode 1): Processes multiple FASTQs in one STAR invocation while reusing the loaded genome. Removes the need for --genomeLoad keep-in-memory workflows. Single-pass only (no --twopassMode); not supported with Solo (--soloType). Use --outFileNamePrefixAuto 1 for per-sample subdirectories.
  • TranscriptVB Quantification (--quantMode TranscriptVB): Variational Bayes and EM quantification for transcript-level abundance, with parity-oriented behavior against Salmon alignment-mode. Gene-level summarization via --quantVBgenesMode Tximport.
  • Transcriptome Output (--quantTranscriptomeSAMoutput): Replaces the former --quantTranscriptomeBan with more explicit control (e.g., BanSingleEnd_ExtendSoftclip).
  • Reference Automation (--autoIndex Yes): Automated reference download/build with --cellrangerStyleIndex Yes formatting and --genomeGenerateTranscriptome Yes for transcript-level quant workflows.
  • Native Gzip FASTQ Handling: Automatic detection of .gz FASTQ inputs with internal zlib streaming — no --readFilesCommand zcat needed. Legacy external helper available via --readFilesLegacyZcat Yes.
  • Cutadapt-Compatible Trimming (--trimCutadapt Yes): Native cutadapt-style trimming for bulk/PE workflows. Compatibility mode: --trimCutadaptCompat Cutadapt3.
  • Poly-G Trimming (--clip3pPolyG yes|no|auto): Trims poly-G artifacts common on NovaSeq/NextSeq platforms. Default auto activates in CellRanger4 mode. Without this, poly-G reads can inflate specific genes (e.g., LINC00486) and degrade gene-level correlations.
  • Samtools-style BAM Sorting (--outBAMsortMethod samtools): Spill-to-disk sort to reduce peak RAM pressure. Works with all modes including Flex.
  • Y/NoY Separation (--emitNoYBAM yes, --emitYNoYFastq yes): Split BAM and FASTQ outputs by chrY alignment. Works with bulk, single-cell, and Flex.
  • EmptyDrops_CR Integration: CR-compatible EmptyDrops path (including libscrna-backed behavior in scRNA/perturb flows).
  • Solo Features: sF BAM tag for feature type, --soloCBtype String for arbitrary barcode strings, --soloCellReadStats Standard for improved cell filtering.
  • CR-compat GEX (--soloCrGexFeature auto|gene|genefull): Controls which GEX source is merged in CR-compat mode.
  • CB/UB Tag Pairing (--soloCbUbRequireTogether yes|no): Enforce CB/UB tag pairing for tag injection (default yes).

Folder Structure

core/
  legacy/                        # Upstream STAR layout (single source of truth)
  features/                      # Shared overlays and feature tooling
    process_features/            # Perturb feature extraction/calling implementation
    feature_barcodes/            # Standalone barcode tools (assignBarcodes, demux)
    libscrna/                    # EmptyDrops/OrdMag/Occupancy shared library
flex/                    # Flex-specific code + tools
slam/                    # SLAM-seq code + tools
build/                   # Modular make fragments
scripts/                 # Suite-level helper scripts (see scripts/README.md)
docs/                    # Suite-level docs
tests/                   # Suite-level tests (see tests/ARTIFACTS.md for artifact locations)
tools/                   # Suite-level scripts/utilities
mcp_server/              # MCP server for scripted discovery/preflight/run workflows

Modules

  • STAR-core (core/): Legacy STAR (indexing, bulk, Solo) plus shared utilities. Build: make core (binary at core/legacy/source/STAR).
  • STAR-perturb (core/legacy/ + core/features/process_features/): CR-compatible perturb-seq path with integrated feature extraction/calling (process_features + call_features) and crispr_analysis/ outputs in CR-compat mode. Primary run path: STAR --pfMultiConfig ... --defaultCrCompat yes (see STAR-perturb section below).
  • STAR-Flex (flex/): FlexFilter pipeline and Flex-specific integrations. Build tools: make flex or make flex-tools.
  • STAR-SLAM (slam/): SLAM-seq quantification, SNP masking, trimming/QC. Build tools: make slam or make slam-tools.
  • Feature Barcodes (core/features/feature_barcodes/): Standalone barcode tools (assignBarcodes, demux_bam, demux_fastq) for perturb-seq testing. Build tools: make feature-barcodes-tools.
  • Process Features (core/features/process_features/): Full feature extraction/calling pipeline (assignBarcodes, call_features, demux_bam, demux_fastq) and standalone tool (star_feature_call). Build tools: make process-features-tools, make star-feature-call.
  • Shared Feature Toolchains (core/features/): Reusable tool layers used across modules, including vbem (TranscriptVB helpers), yremove_* (Y/noY splitting), bamsort, and libscrna. Build tools: make vbem-tools, make yremove-tools, plus in-core integrations.
  • MCP Server (tooling) (mcp_server/): Agent automation service for dataset/test discovery and controlled execution (list_datasets, list_test_suites, preflight, run_script, collect_outputs). This is repo tooling, not an analysis module.
  • Helper Scripts (scripts/): Standalone Python and Bash tools for FASTQ preflight, QC, parity benchmarking, downstream h5ad processing, and fixture management. These are not compiled into STAR; they run independently. Highlights include preflight_library_pairing.py (chemistry detection and library pairing for mislabeled Perturb-seq), report_additional_parity_metrics.py (STAR vs CR parity), and build_gene_full_velocyto_h5ad.py (Velocyto h5ad packaging). See scripts/README.md for the full catalogue.

Benchmarks

All benchmarks run on pikachu (AMD, 32 threads, 128 GB RAM, NVMe SSD). Detailed artifacts: comparisons/paper_benchmarks_20260318/.

Dataset: MorPHiC JAX KOLF PE RNA-seq (sample 21033-09-01-13-01, 6.5M read pairs, NovaSeq X Plus).

"External stepwise" = Trim Galore + STAR align + (optional remove_y_reads) + Salmon quant (sequential).

Bulk RNA-seq Wall Time

Dataset Y-removal Wall time (integrated) Wall time (stepwise) Speedup
JAX PE (full, 32 threads) no 37 s 87 s 2.4x
JAX PE (full, 32 threads) yes 61 s 125 s 2.1x

Bulk RNA-seq Parity (STAR TranscriptVB vs Salmon)

Dataset Y-removal Transcript Pearson Gene Pearson
JAX PE (full, 32 threads) no 0.995 0.997
JAX PE (full, 32 threads) yes 0.995 0.997
  • JAX PE noY: TranscriptVB vs Salmon alignment-mode VB on expressed transcripts; integrated 37 s vs external stepwise 87 s (32 threads). Speedup reflects elimination of Trim Galore and Salmon as separate steps; single-pass STAR handles trimming, alignment, and quantification.
  • JAX PE Y-removal: TranscriptVB vs Salmon alignment-mode VB on expressed transcripts; integrated 61 s vs external stepwise 125 s (32 threads). Y-removal adds chrY BAM splitting and noY FASTQ generation in both pipelines; integrated path handles this natively via --emitNoYBAM yes.

Perturb-seq Wall Time

Dataset Libraries Chemistry Reads STAR cells Wall time Speedup
A375 1k CRISPR 5' GemX GEX + CRISPR (2) TRU 47M 1,187 4.0 min 3.8x
UCSF EBs2_2 Perturb-seq GEX + CRISPRa (2) NXT→TRU 445M 13,721 19.0 min 3.2x
MSK 30polyKO GEX + gRNA + LARRY (3) Mixed TRU/NXT 669M 30,567 27.6 min 6.1x

Perturb-seq Parity (STAR vs Cell Ranger 9)

Dataset Cells (STAR / CR) Jaccard Gene Pearson Cell Pearson CRISPR match
A375 1k CRISPR 5' (GeneFull) 1,187 / 1,162 0.976 0.975 1.000 100% (1,083/1,083)
UCSF EBs2_2 (full, NXT) 13,721 / 13,760 0.976 0.995 1.000 98.9% (11,902/12,038)
MSK 30polyKO (3-lib, NXT+TRU) 30,567 / 32,256 0.942 0.994 1.000 99.4% (23,210/23,341)
  • A375: Gene Pearson on 15,673 filtered genes (min 20 counts, 1% cells); Cell Pearson 0.9995 on 1,160 common barcodes; CRISPR exact set-match on all 1,083 common cells (min-UMI 10), UMI Pearson 1.000; speedup = 4 min vs 15 min (32 threads, no BAM). CR9 reference: refdata-gex-GRCh38-2024-A, 1k_CRISPR_5p_gemx_count_refmatch_2024a_fullraw.
  • UCSF EBs2_2: Gene Pearson on 18,061 filtered genes; Cell Pearson 1.000 on 13,571 common barcodes; CRISPR set-match 98.9% on 12,038 evaluated cells, target-level match 99.5%; UMI Pearson 0.999; speedup = 19 min vs 61 min CR9 (32 threads, no BAM). CR9 reference: refdata-gex-GRCh38-2024-A, run on same corrected FASTQs.
  • MSK: Gene Pearson on 17,460 filtered genes; Cell Pearson 1.000 on 30,481 common barcodes; CRISPR set-match 99.4% on 23,341 evaluated cells (30 guides, min-UMI 2), UMI Pearson 1.000; speedup = 28 min vs 168 min (32 threads, no BAM). CR requires two separate runs (GEX+gRNA 58 min + GEX+LARRY 110 min); STAR handles all three libraries in a single pass with per-library whitelist support.

All parity metrics computed with scripts/report_additional_parity_metrics.py --gene-corr-min-counts 20 --gene-corr-min-cells-pct 0.01 per docs/PAPER_BENCHMARK_METHODOLOGY.md. CR9 references use refdata-gex-GRCh38-2024-A (gencode v44, mkref 8.0.0).

Perturb-seq Phase Breakdown (32 threads, no BAM)

Phase A375 (47M) UCSF EBs2_2 (445M) MSK 30polyKO (669M)
Genome load 44s 48s 48s
Feature assignment 32s 4m 17s 19m 50s
Mapping 81s 8m 31s 14m 42s
Solo counting 69s 7m 46s 9m 29s
PfMulti merge + calling 10s 1m 20s 1m 59s
writeCombinedMex (raw/filt) 2.0s / 1.5s 19.5s / 15.0s 35.1s / 24.4s

Feature assignment and mapping run concurrently via dynamicThreadInterface.

Flex (STAR vs Cell Ranger 7.2)

Dataset Cells (STAR / CR) Jaccard Gene Pearson Cell Pearson Speedup
JAX SC2300771 full (4 samples) 20,291 / 20,444 0.98 0.998 1.000 pending
  • Gene Pearson on 18,021 common genes; Cell Pearson on 20,173 shared barcodes.
  • Speedup not yet measured (optimization pending).

SLAM-seq (STAR-SLAM vs GrandSLAM/GEDI)

NTR parity (compat mode, GEDI is reference):

Dataset Sample NTR Pearson NTR Spearman
NW-5-21 ARID1A 1M (compat, no trim) 0h 0.978 0.990
NW-5-21 ARID1A 1M (compat, no trim) 6h 0.972 0.986
NW-5-21 ARID1A 1M (compat, no trim) 24h 0.967 0.985
100K fixture (SNP BED, ≥20 reads) -- 0.999 0.981
  • Comparison uses SNP-masked BAMs; GEDI is reference.
  • slam_requant replay: Pearson/Spearman 1.0 (exact parity with STAR output).
  • Compat mode (--slamCompatMode gedi) adds negligible overhead (<0.1% wall time, <1% memory).
  • Direct speedup comparison is not reported because GRAND-SLAM depends on alignment being completed first (it operates on pre-aligned BAMs), whereas STAR-SLAM performs alignment and quantification in a single pass. On the ARID1A time-course (167M reads, 4 samples), GEDI quantification alone adds ~14% to the alignment time (~5.5 min on top of ~40 min alignment).

Building & Installing

From source

# Core STAR binary
make core

# Module-focused builds
make flex           # core + Flex tools
make slam           # core + SLAM tools

# Individual tool targets
make feature-barcodes-tools    # assignBarcodes/demux (standalone)
make process-features-tools    # full process_features pipeline
make star-feature-call         # standalone feature caller
make vbem-tools                # TranscriptVB helpers
make yremove-tools             # Y/noY splitting tools

# Default build (core + common tools)
make                           # or: make default

# Build everything
make all

Selective filtering:

make default INCLUDE="core slam-tools"
make default EXCLUDE="flex-tools"

Run make help to see the full target list and descriptions.

From release artifacts

# Ubuntu package from a local artifact
sudo apt install ./star-suite_<version>_<arch>.deb

# Installer tarball (auto-detects host glibc level)
tar -xzf STAR-suite-<version>-linux-<arch>-installer.tar.gz
cd STAR-suite-<version>-linux-<arch>-installer
./install.sh

# Manual compatibility tarball
tar -xzf STAR-suite-<version>-linux-<arch>-glibc234.tar.gz
cd STAR-suite-<version>-linux-<arch>-glibc234
./install.sh

Release tarballs are validated in clean Ubuntu 22.04 and 24.04 Docker containers before publication. The installer bundle auto-detects the host glibc level and chooses the right bundled binary.

Packaging/release details and artifact policy:

  • docs/Star-binary-distribution.md
  • docs/Github-actions.md

Compilation details (module-by-module, clean rebuilds, and clean Ubuntu 24.04 validation):

  • docs/compile_instructions.md

Docker

A multi-stage Docker setup (Ubuntu 24.04) provides a clean build environment and separate runtime/test images.

Builder stage: Compiles STAR Suite from source with no host leakage. Validates make core, flex, slam, feature-barcodes-tools, default, and all.

Suite base runtime (suite-base): Minimal executable image with suite binaries (e.g. STAR) and no Python/test-only helpers.

Test images (built from suite-base):

  • test-tier-a: self-contained smoke helpers.
  • test-tier-b: fixture-backed helper stack (e.g. python3, bc, samtools).

Quickstart

# Build suite base image (default tag: biodepot/star-suite:latest)
./scripts/docker/build_image.sh

# Override tag or parallel jobs
IMAGE_TAG=myorg/star-suite:v1 MAKE_JOBS=8 ./scripts/docker/build_image.sh

# Reproducibility check: force a clean rebuild (no cache)
docker build --no-cache --target suite-base -f docker/Dockerfile -t biodepot/star-suite:latest --build-arg MAKE_JOBS=8 .

# Run STAR from suite base image
docker run --rm biodepot/star-suite:latest

# Run Tier A smoke tests (builds/uses test-tier-a image)
./scripts/docker/run_smokes_tier_a.sh

# Run Tier B smoke tests (builds/uses test-tier-b image; requires fixtures)
./scripts/docker/run_smokes_tier_b.sh

Fixture mount for Tier B

Tier B tests require data under /storage. Mount your fixture root:

docker run --rm -v /path/to/your/data:/storage biodepot/star-suite:test-tier-b bash -c "tests/run_cbub_regression_test.sh"

By default, ./scripts/docker/run_smokes_tier_b.sh uses STORAGE=/storage. Set STORAGE=/path to override (script uses it for the -v mount).

Expected layout: /storage/A375, /storage/flex_filtered_reference, etc. See plans/docker_plan.md for full fixture roots.

STAR_BIN override

Smoke tests honor STAR_BIN to decouple from source-relative paths. Docker smoke wrappers set STAR_BIN=/usr/local/bin/STAR automatically.

Validation

See docs/docker_validation.md for the latest portability check results.

Module Reference

This section documents the key features and flags for each module. For standard STAR flags not listed here, see core/legacy/README.md. Core additions are listed above in Core Additions over STAR 2.7.11b.

Flex

See flex/README_flex.md for the full pipeline reference.

STAR-Flex uses a pseudo-chromosome alignment approach: probe sequences are embedded as pseudo-chromosomes in a hybrid reference genome, and STAR's native alignment machinery handles gene assignment. Core features (trimming, spill-to-disk sorting, Y-chromosome splitting, TranscriptVB) all work with Flex.

Key flags:

  • --flex yes: Enable Flex pipeline.
  • --soloFlexExpectedCellsPerTag: Expected cells per sample tag.
  • --soloSampleWhitelist: TSV mapping sample tags to labels.
  • --soloProbeList: Probe gene list (auto-detected from index if omitted).
  • --soloSampleProbes: 10x probe barcode sequences file.

Features:

  • Sample tag detection, 1MM pseudocount correction for CBs, clique-based UMI deduplication, and occupancy filtering.
  • Y-chromosome splitting tested and validated (tests/TEST_REPORT_Y_SPLIT_FLEX.md).

SLAM

See slam/docs/SLAM_COMPATIBILITY_MODE.md and slam/docs/SLAM_seq.md.

Integrated SLAM-seq quantification with GRAND-SLAM parity:

Key flags:

  • --slamQuantMode 1: Enable SLAM quantification.
  • --slamGrandSlamOut 1: Generate GRAND-SLAM compatible output.
  • --slamCompatMode gedi: Enable GEDI compatibility (intronic classification, lenient overlap, overlap weighting).
  • --slamCompatIntronic, --slamCompatLenientOverlap: Fine-grained compat control.
  • --autoTrim variance: Variance-based detection of artifact-prone read ends.
  • --slamTrim5p, --slamTrim3p: Manual trim guards.
  • --slamErrorRateFromBlank 1: Seed error rate from a blank (e.g. no4sU) sample.
  • --outFileNamePrefixAuto 1: Derive sample name from first FASTQ and route outputs into subdirs.
  • --slamDumpBinary 1 --slamDumpWeights 1: Emit binary dumps for offline re-quantification with slam_requant.

Features:

  • Full gene-level NTR estimation (Binomial/EM models).
  • Auto-trimming: variance-based detection of artifact-prone read ends.
  • QC: comprehensive interactive HTML reports for T->C rates and error modeling.
  • Batch layout organizes outputs into alignments/, counts/, qc/, y_separated/.
  • Binary dump format documented in slam/docs/SLAM_DUMP_FORMAT.md.

STAR-perturb / CR-Compat

See docs/feature_barcodes.md and docs/CRISPR_FEATURE_CALLING_IMPLEMENTATION_SUMMARY.md.

CR-compatible Solo behavior with integrated CRISPR feature calling:

Key flags:

  • --pfMultiConfig: Cell Ranger-style multi processing with feature libraries.
  • --defaultCrCompat yes: Apply the CR-compat perturb defaults bundle.
  • --dynamicThreadInterface 1: Enable STAR/PF permit coordination.
  • --dynamicThreadConstMapPermits 32: Start with full map-side permit budget.
  • --crAssignConsumerThreads 32: Provision PF worker pool to full host budget.
  • --crAssignSearchThreads 1: Per-consumer search-thread mode.
  • --crMinUmi: Minimum UMI threshold for CRISPR feature calling (default 10; lower to 2-3 for lineage barcodes).
  • --soloCrGexFeature: Control merged GEX source (auto, gene, genefull).
  • --soloCrMode CR: Enable CR-compatible single-cell behavior.
  • --crChemistry: Barcode chemistry (auto, NXT, TRU). Default auto enables per-library auto-detection. Mixed NXT/TRU experiments are handled automatically; per-library overrides via the star_chemistry column in --pfMultiConfig.

Recommended execution profile (32-thread host):

--runThreadN 32 --dynamicThreadInterface 1 --dynamicThreadConstMapPermits 32 \
--dynamicThreadTelemetry 1 --crAssignConsumerThreads 32 --crAssignSearchThreads 1

Standalone tool (star_feature_call):

  • --compat-perturb: CR9-compatible output layout (crispr_analysis/).
  • --feature-ref, --whitelist, --fastq-dir, --output-dir: FASTQ -> MEX -> calls.
  • --call-only --mex-dir: call_features-only pass on existing MEX.
  • --emptydrops-use-fdr, --min-umi, --ratio-test: calling controls.

QC Outputs

  • SLAM QC (--slamQcReport <prefix>): Interactive HTML report (.html) and JSON metrics (.json) for T->C conversion rates, variance analysis, and trimming overlays.
  • FlexFilter QC (flexfilter_summary.tsv): Cell calling statistics (EmptyDrops/OrdMag), cell counts, UMI thresholds, and filtering rates per sample.

Sample Commands

Core alignment:

core/legacy/source/STAR \
  --runMode alignReads \
  --genomeDir /path/to/genome_index \
  --readFilesIn reads.fq.gz \
  --outFileNamePrefix out/ \
  --outSAMtype BAM SortedByCoordinate \
  --outSAMattributes NH HI AS nM MD

Batch mode (bulk, single-pass, SE):

core/legacy/source/STAR \
  --runMode alignReads \
  --genomeDir /path/to/genome_index \
  --readFilesIn A_R1.fq.gz,B_R1.fq.gz \
  --outFileNamePrefix /path/to/out_root/ \
  --outFileNamePrefixAuto 1 \
  --batchMode 1 \
  --outSAMtype BAM SortedByCoordinate

Batch mode (bulk, single-pass, PE):

core/legacy/source/STAR \
  --runMode alignReads \
  --genomeDir /path/to/genome_index \
  --readFilesIn A_R1.fq.gz,B_R1.fq.gz A_R2.fq.gz,B_R2.fq.gz \
  --outFileNamePrefix /path/to/out_root/ \
  --outFileNamePrefixAuto 1 \
  --batchMode 1 \
  --outSAMtype BAM SortedByCoordinate

Flex Mode (10x Fixed RNA Profiling):

core/legacy/source/STAR \
  --runMode alignReads \
  --genomeDir /path/to/flex_index \
  --readFilesIn reads_R2.fq.gz reads_R1.fq.gz \
  --flex yes \
  --soloType CB_UMI_Simple \
  --soloCBwhitelist /path/to/737K-fixed-rna-profiling.txt \
  --soloSampleWhitelist sample_whitelist.tsv \
  --outFileNamePrefix output/

SLAM Mode (Standard):

core/legacy/source/STAR \
  --runMode alignReads \
  --genomeDir /path/to/genome_index \
  --readFilesIn reads.fq.gz \
  --outFileNamePrefix out/ \
  --outSAMtype BAM SortedByCoordinate \
  --outSAMattributes NH HI AS nM MD \
  --slamQuantMode 1 \
  --slamSnpBed /path/to/snps.bed

SLAM Mode (GEDI Compatibility):

core/legacy/source/STAR \
  --runMode alignReads \
  --genomeDir /path/to/genome_index \
  --readFilesIn reads.fq.gz \
  --slamQuantMode 1 \
  --slamCompatMode gedi \
  --autoTrim variance \
  --outFileNamePrefix output/

SLAM Batch Mode (blank-first, SE/PE):

core/legacy/source/STAR \
  --runMode alignReads \
  --genomeDir /path/to/genome_index \
  --readFilesIn blank_R1.fq.gz,0h_R1.fq.gz,6h_R1.fq.gz,24h_R1.fq.gz \
  --outFileNamePrefix /path/to/out_root/ \
  --outFileNamePrefixAuto 1 \
  --slamQuantMode 1 \
  --slamBatchMode 1 \
  --slamErrorRateFromBlank 1 \
  --slamSnpBed /path/to/snps.bed

For paired-end, pass two comma-separated mate lists: --readFilesIn blank_R1.fq.gz,0h_R1.fq.gz,... blank_R2.fq.gz,0h_R2.fq.gz,...

STAR-perturb (integrated CR-compat mode):

core/legacy/source/STAR \
  --runMode alignReads \
  --runThreadN 32 \
  --genomeDir /path/to/index \
  --pfMultiConfig /path/to/multi_config.csv \
  --dynamicThreadInterface 1 \
  --dynamicThreadConstMapPermits 32 \
  --crAssignSearchThreads 1 \
  --defaultCrCompat yes \
  --outFileNamePrefix /path/to/outs/

STAR-perturb (standalone feature pipeline):

core/legacy/source/star_feature_call \
  --compat-perturb \
  --feature-ref /path/to/feature_reference.csv \
  --whitelist /path/to/whitelist.txt \
  --fastq-dir /path/to/feature_fastqs \
  --filtered-barcodes /path/to/filtered_barcodes.tsv \
  --output-dir /path/to/feature_out \
  --emptydrops-use-fdr \
  --min-umi 10

Codespaces Walkthroughs

STAR Suite includes GitHub Codespaces walkthroughs for the main module entry points.

Start here:

Ready now:

Work in progress:

Helpful follow-up guides:

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