- Format:
- Count matrices: h5ad (AnnData)
- Metadata: Parquet
- Data host:
- Google Cloud Storage
- Path:
gs://arc-ctc-tahoe100/
- Statistics
- Sample count: 1344
- Cell count: 100648790
Here's the table formatted with consistent spacing:
| Column Name | Description |
|---|---|
| plate | Plate identifier |
| BARCODE_SUB_LIB_ID | Cell identifier |
| sample | Unique treatment identifier, distinguishes replicated treatments |
| gene_count | Number of genes with at least one count |
| tscp_count | Number of transcripts, aka UMI count |
| mread_count | Number of reads per cell |
| drugname_drugconc | Drug name, concentration, and concentration unit |
| drug | Drug name, parsed out from the drugname_drugconc field |
| cell_line | Cell line Cellosaurus identifier |
| sublibrary | Sublibrary ID (related to library prep and sequencing) |
| BARCODE | Barcode ID |
| pcnt_mito | Percentage of mitochondrial reads |
| S_score | Inferred S phase score |
| G2M_score | Inferred G2M score |
| phase | Inferred cell cycle phase |
| pass_filter | "Full" filters are more stringent on gene_count and tscp_count |
| cell_name | Commonly-used cell name (related to the cell_line field) |
.h5ad.gzfile extensions denote internal gzip compression.- See the Python tutorial on reading in the anndata objects.