GSE154778 Processing Pipeline
RNA-Seq
code_examples
1 step
Publication
A super-enhancer-regulated RNA-binding protein cascade drives pancreatic cancer.Nature communications (2023) — PMID 37673892
Dataset
GSE154778Single-cell transcriptomics analysis of pancreatic primary tumor and metastatic biopsy tissues
Warning: Pipeline descriptions and code snippets may be inferred or AI-generated. Use them only as a starting point to guide analysis, and validate before use.
Processing Steps
Generate Jupyter Notebook-
1
Cell Ranger
Cell Ranger vNot specified$ Bash example
# Install Cell Ranger (example, adjust for specific environment and version) # Download Cell Ranger from the 10x Genomics website: # https://www.10xgenomics.com/support/software/cell-ranger/downloads # For example, to install version 8.0.0: # wget https://cf.10xgenomics.com/releases/cell-ranger/cellranger-8.0.0.tar.gz # tar -xzf cellranger-8.0.0.tar.gz # export PATH=/path/to/cellranger-8.0.0:$PATH # Reference Dataset: Cell Ranger requires a pre-built transcriptome reference. # This reference is typically generated once using the 'cellranger mkref' command # from a genome FASTA file and a gene annotation GTF file (e.g., from GENCODE or Ensembl). # As a placeholder, we'll assume a GRCh38 human transcriptome reference. # Example command to build a reference (replace paths and versions as needed): # cellranger mkref \ # --genome=GRCh38_v8.0.0 \ # --fasta=/path/to/GRCh38.primary_assembly.fa \ # --genes=/path/to/gencode.v38.annotation.gtf \ # --ref-version=8.0.0 # Example: Run cellranger count for a single-cell RNA-seq experiment. # Replace 'sample_id', '/path/to/fastqs', and '/path/to/transcriptome_ref' with actual values. # The '--transcriptome' argument should point to the directory created by 'cellranger mkref'. cellranger count \ --id=sample_id_output \ --transcriptome=/path/to/cellranger_ref/GRCh38_v8.0.0 \ --fastqs=/path/to/fastqs \ --sample=sample_id \ --localcores=16 \ --localmem=64
Raw Source Text
Cell Ranger Genome_build: hg19 Supplementary_files_format_and_content: csv format that contains the umi count for each gene in each barcodes (cells)