Summary
Experiment summary
| Input reads | 100,639,120 |
|---|---|
| Estimated cells | 4,982 |
| Reads per cell (mean) | 20,201 |
| UMIs per cell (median) | 6,656 |
| Genes per cell (median) | 3,110 |
Barcode rank plot
Alignment / feature summary
| Pass reads | 95,739,969 |
|---|---|
| Mapped | 94,461,955 |
| Unmapped | 1,278,014 |
| Supplementary | 4,868,193 |
| Unique genes | 20,529 |
| Unique isoforms | 69,894 |
- Input reads: The total number of reads in the input data.
- Estimated cells: The estimated number of real cells identified by the workflow.
- Mean reads per cell: The average number of reads per real cell.
- Median UMI counts per cell: The median number of unique molecular identifiers (UMIs) per cell.
- Median genes per cell: The median number of unique genes identified per real cell.
The dashed line indicates the read count threshold that was determined by the workflow. Barcodes to the left of this point are considered "real cells", and those to the right are considered as non-cell barcodes and are not included in the downstream analysis.
- Pass reads: The total number of reads that passed the input filtering stages of the analysis. This number excludes reads where the expected adapters were not found.
- Mapped: The number of primary alignments.
- Unmapped: The number of reads that were not mapped to the reference genome.
- Supplementary: The number of supplementary alignments. These can be indicative of fusion genes or chimeric reads.
- Unique genes/isoforms: The total number of features identified across all cells.
Read summary
Read assignment summary
| Full length | Valid barcode | Gene assigned | Transcript assigned | |
|---|---|---|---|---|
| count | 96,796,977 | 75,882,498 | 54,485,006 | 27,940,114 |
| % full length reads | 100.00% | 78.39% | 56.29% | 28.86% |
- Full length: Proportion of reads containing adapters in the expected configuration. Full-length reads are carried forward in the workflow to attempt to assign barcode/UMI.
- Valid barcodes: Proportion of reads that have been assigned corrected cell barcodes and UMIs. All reads with valid barcodes are used in the subsequent stages of the workflow.
- Gene assigned: Proportion of reads unambiguously assigned to a gene.
- Transcript assigned: Proportion of reads unambiguously assigned a transcript.
Adapter configuration
Full length reads are defined as those flanked by primers/adapters in the expected orientations: adapter1---cDNA---adapter2.
These full length reads can then be oriented in the same way and are used in the next stages of the workflow. If `full_length_only` is set to `false` reads with all primer configurations are analysed.
Every library prep will contain some level of non-standard adapter configuration artifacts. These are not used for subsequent stages of the workflow. These plots show the proportions of different adapter configurations within each sample, which can help in diagnosing library preparation issues. For most applications, the majority of reads should be full_length.
The adapters used to identify read segments vary slightly between the supported kits. They are:
3prime, multiome and visium kits:
- Adapter1: Read1
- Adapter2: TSO
5prime kit:
- Adapter1: Read1
- Adapter2: Non-Poly(dT) RT primer
Saturation
Sequencing saturation is an indication of how well the diversity of a library has been captured in an experiment. As sequencing depth increases, the number of detected genes and unique molecular identifiers (UMIs) will also increase at a rate that depends on the complexity of the input library. The curve gradient indicates the rate at which new genes or UMIs are being recovered; as saturation increases the the curve flattens. All metrics are calculated through random sampling of the complete dataset.
- Sequencing saturation: The total number of unique cDNA molecules observed having sampled sequencing reads. Calculated as 1 - (number of unique UMIs / number of reads).
- Gene saturation: Unique genes observed per cell. Calculated as a median across cells, after sampling the expression matrix.
- UMI saturation: Unique UMIs observed per cell. Calculated as a median across cells, after sampling the expression matrix.
UMAP projections
This section presents various UMAP projections of the data. UMAP is an unsupervised algorithm that projects the multidimensional single cell expression data into two dimensions. This could reveal structure in the data representing different cell types or cells that share common regulatory pathways, for example. The UMAP algorithm is stochastic; analysing the same data multiple times with UMAP, using identical parameters, can lead to visually different projections. In order to have some confidence in the observed results, it can be useful to run the projection multiple times and so a series of UMAP projections can be viewed below.