Results for “qc”
11 skills
github qdrant-version-upgrade
Upgrade Qdrant version without interrupting application availability and ensuring data integrity.
36.2k
gabrielmoreira seq-wrangler
Runs NGS read QC, alignment, and BAM processing, wrapping FastQC, BWA/Bowtie2/Minimap2, SAMtools, and MultiQC for automated read-to-BAM workflows.
17 · bundle
k-dense-ai scanpy
Run standard single-cell RNA-seq analysis pipelines: QC, normalization, dimensionality reduction, clustering, differential expression, and visualization using Scanpy.
30.2k · bundle
gabrielmoreira rnaseq-de
Performs differential expression analysis on bulk RNA-seq or pseudo-bulk count matrices with QC, PCA, and contrast testing.
17 · bundle
More results
gabrielmoreira gwas-pipeline
Automates genome-wide association studies from genotype files to publication-ready results, running PLINK2 QC and REGENIE regression with Manhattan and QQ plots.
17 · bundle
k-dense-ai bulk-rnaseq
Orchestrates a complete bulk RNA-seq differential-expression study from raw FASTQ reads through QC, alignment, quantification, differential expression, pathway enrichment, and publication figures.
30.2k · bundle
github qdrant-scaling-qps
Guides scaling Qdrant query throughput (QPS) through performance tuning, horizontal scaling with read replicas, and disk I/O optimization.
36.2k
pranavnagrecha cpq-data-model
Maps the Salesforce CPQ managed-package object graph (SBQQ__ namespace) and explains when to use the CPQ Quote API for programmatic writes instead of direct DML.
15 · bundle
lingxling scanpy
Runs standard single-cell RNA-seq analysis with Scanpy, covering QC, normalization, dimensionality reduction, clustering, marker identification, visualization, and conversion of R single-cell formats to h5ad.
253 · bundle
mocchalera review-roughcut
Reviews a video rough cut by generating deterministic metrics, critiquing the edit against brief and blueprint, and producing review_report.yaml and review_patch.json artifacts.
3 · bundle
qcmuu ray-data
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
0 · bundle