In combiz/scFlow: A Single-Cell/Nuclei Analysis Toolkit
### Post-QC Summary
-
Cells
`r prettyNum(metadata$qc_summary$qc_cells_n_cells_passed, big.mark = ",")`
-
Genes
`r prettyNum(metadata$qc_summary$qc_genes_n_genes_passed, big.mark = ",")`
-
Total counts (M)
`r metadata$qc_summary$qc_cells_median_total_counts`
-
Total features (M)
`r metadata$qc_summary$qc_cells_median_total_features_by_counts`
-
Mitochondrial counts (M%)
`r scales::percent(metadata$qc_summary$qc_cells_median_pc_mito, accuracy = 0.01)`
-
Ribosomal counts (M%)
`r scales::percent(metadata$qc_summary$qc_cells_median_pc_ribo, accuracy = 0.01)`
* M is the median for QC-passed cells
### Key Parameters
-
Library size
`r sprintf("%s – %s", metadata$qc_params$min_library_size, prettyNum(metadata$qc_params$max_library_size, big.mark = ","))`
-
Features/Genes
`r sprintf("%s – %s", metadata$qc_params$min_features, prettyNum(metadata$qc_params$max_features, big.mark = ","))`
-
Max. Mito. Counts
`r scales::percent(metadata$qc_params$max_mito, accuracy = 0.1)`
-
Ribo. Counts
`r sprintf("%s – %s", scales::percent(metadata$qc_params$min_ribo, accuracy = 0.1), scales::percent(metadata$qc_params$max_ribo, accuracy = 0.1))`
-
Drop Mito. Genes
`r ifelse(metadata$qc_params$drop_mito, "Yes", "No")`
-
Drop Ribo. Genes
`r ifelse(metadata$qc_params$drop_ribo, "Yes", "No")`
### Steps
Metadata Imported
✔️
Ambient RNA / EmptyDrops Run
`r ifelse(metadata$scflow_steps$emptydrops_annotated, "✔️", "❌")`
Thresholding
✔️
️
Gene/Cell QC Metric Annotation
✔️
️
Doublets/Multiplets Identified
`r ifelse(metadata$scflow_steps$singlets_annotated, sprintf("(%s) ✔️" , metadata$scflow_steps$singlets_method), "❌")`
combiz/scFlow documentation built on Feb. 25, 2024, 10:25 a.m.
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### Post-QC Summary
- Cells `r prettyNum(metadata$qc_summary$qc_cells_n_cells_passed, big.mark = ",")`
- Genes `r prettyNum(metadata$qc_summary$qc_genes_n_genes_passed, big.mark = ",")`
- Total counts (M) `r metadata$qc_summary$qc_cells_median_total_counts`
- Total features (M) `r metadata$qc_summary$qc_cells_median_total_features_by_counts`
- Mitochondrial counts (M%) `r scales::percent(metadata$qc_summary$qc_cells_median_pc_mito, accuracy = 0.01)`
- Ribosomal counts (M%) `r scales::percent(metadata$qc_summary$qc_cells_median_pc_ribo, accuracy = 0.01)`
* M is the median for QC-passed cells
### Key Parameters
- Library size `r sprintf("%s – %s", metadata$qc_params$min_library_size, prettyNum(metadata$qc_params$max_library_size, big.mark = ","))`
- Features/Genes `r sprintf("%s – %s", metadata$qc_params$min_features, prettyNum(metadata$qc_params$max_features, big.mark = ","))`
- Max. Mito. Counts `r scales::percent(metadata$qc_params$max_mito, accuracy = 0.1)`
- Ribo. Counts `r sprintf("%s – %s", scales::percent(metadata$qc_params$min_ribo, accuracy = 0.1), scales::percent(metadata$qc_params$max_ribo, accuracy = 0.1))`
- Drop Mito. Genes `r ifelse(metadata$qc_params$drop_mito, "Yes", "No")`
- Drop Ribo. Genes `r ifelse(metadata$qc_params$drop_ribo, "Yes", "No")`
### Steps
Metadata Imported
✔️
Ambient RNA / EmptyDrops Run
`r ifelse(metadata$scflow_steps$emptydrops_annotated, "✔️", "❌")`
Thresholding
✔️
️
Gene/Cell QC Metric Annotation
✔️
️
Doublets/Multiplets Identified
`r ifelse(metadata$scflow_steps$singlets_annotated, sprintf("(%s) ✔️" , metadata$scflow_steps$singlets_method), "❌")`
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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