The number of reads that mapped to a unique gene in the transcriptome divided by the number of barcodes associated with cell-containing partitions.
Median UMI Counts per Cell
The median number of UMI counts per % cell-associated barcode.
Median Genes per Cell
The median number of genes detected per cell-associated barcode. Detection is defined as the presence of at least 1 UMI count.
11,398
Estimated Number of Cells
2,785
Mean reads per Cell
1,481
Median UMI Counts per Cell
1,013
Median Genes per Cell
Cells
Estimated Number of Cells
The number of barcodes associated with at least one cell.
Fraction Reads in cells
The fraction of valid-barcode, confidently-mapped reads with cell-associated barcodes.
Mean Reads per Cell
The number of reads that mapped to a unique gene in the transcriptome divided by the number of barcodes associated with cell-containing partitions.
Median UMI Counts per Cell
The median number of UMI counts per %s cell-associated barcode.
Median Genes per Cell
The median number of genes detected per cell-associated barcode. Detection is defined as the presence of at least 1 UMI count..
Total Genes Detected
The number of genes with at least one UMI count in any cell.
Barcode Rank Plot
The plot shows the count of filtered UMIs mapped to each barcode.
Sample Name
nasopharyngeal cancer
Species
Human
Type
FFPE
Organ
nasopharynx
Estimated Number of Cells
11,398
Fraction Reads in cells
30.87%
Mean Reads per Cell
2,785
Median UMI Counts per Cell
1,481
Median genes per cell
1,013
Total genes detected
35,777
Sequencing & Mapping
Number of Reads
Total number of read pairs that were assigned to this library in demultiplexing.
Valid Barcodes
Fraction of reads with barcodes that match the whitelist after barcode correction.
Valid UMIs
Fraction of reads with valid UMIs; i.e. UMI sequences that do not contain Ns and that are not homopolymers.
Sequencing Saturation
The fraction of reads originating from an already-observed UMI. This is a function of library complexity and sequencing depth. More specifically, this is the fraction of confidently mapped, valid cell-barcode, valid UMI reads that had a non-unique (cell-barcode, UMI, gene).
Q30 Bases in RNA Read
Fraction of RNA read bases with Q-score ≥ 30.
Reads Mapped to Genome
Fraction of reads that mapped to the genome.
Reads Mapped Confidently to Genome
Fraction of reads that mapped uniquely to the genome.
Reads Mapped Confidently to Transcriptome
Fraction of reads that mapped to a unique gene in the transcriptome. These reads are considered for UMI counting.
Reads Mapped to Exonic Regions
Fraction of reads that mapped to an exonic region of the genome.
Reads Mapped to Intronic Regions
Fraction of reads that mapped to an intronic region of the genome.
Reads Mapped to LncRNA Regions
Fraction of reads that mapped to an lncRNA region of the genome.
Sequencing
Number of Reads
447,452,664
Valid Barcodes
88.78%
Valid UMIs
100.0%
Sequencing Saturation
48.61%
Q30 Bases in RNA Read
91.79%
Mapping
Reads Mapped to Genome
79.23%
Reads Mapped Confidently to Genome
46.89%
Reads Mapped Confidently to Transcriptome
40.78%
Reads Mapped to Exonic Regions
11.39%
Reads Mapped to Intronic Regions
35.09%
Reads Mapped to LncRNA Regions
7.02%
Cluster
Left
The figure is automated clustering each cell-barcode by UMAP algorithm. The cells clustered into the same group have similar expression profiles. Each dot represents a cell, and is colored according to different cluster.
Right
This plot shows that the total UMI counts for each cell-barcode. Two-dimensional horizontal and vertical coordinates of each dot are obtained using the uniform manifold approximation and projection (UMAP) algorithm. Each dot represents a cell and is colored according to UMI counts. Cells with greater UMI counts likely have higher RNA content than cells with fewer ones.
Marker
Table
The differential expression analysis seeks to find, for each cluster, features that are more highly expressed in that cluster relative to the rest of the sample. Here a differential expression test was performed between each cluster and the rest of the sample for each feature. The Log2 fold-change (L2FC) is an estimate of the log2 ratio of expression in a cluster to that in all other cells. A value of 1.0 indicates 2-fold greater expression in the cluster of interest. The p-value is a measure of the statistical significance of the expression difference that by a negative binomial test, and been adjusted for multiple testing via the Benjamini-Hochberg procedure reported as p_val_adj. The pct.1 is the percentage of cells where the feature is detected in that cluster. And the pct.2 is the percentage of cells where the feature is detected in the rest of the sample. The features were filtered by p_value < 0.01, p_val_adj < 0.1, pct.1 ≥ 0.1, pct.1 ≥ 0.1, and avg_log2FC > 0.25 for each cluster were retained. In this table you can click on a column to sort by that value.
This plot illustrating the coverage profile along the gene body. Scaleing all transcripts to 100 nt and calculates the number of reads covering each nucleotide position. The coverage was calculaterd for each position as (NUMi - min(NUMs))/(max(NUMs) - min(NUMs)). Here, NUMi denotes the number of covered reads of position i, min(NUMs) and max(NUMs) are the minimum and maximum number of covered reads in each nucleotide position.
Saturation
Left
The plot shows the Sequencing Saturation metric as a function of downsampled sequencing depth in mean reads per cell. Sequencing Saturation is a measure of the observed library complexity, and approaches 1.0 (100%) when all converted mRNA transcripts have been sequenced. The slope of the curve near the endpoint can be interpreted as an upper bound to the benefit to be gained from increasing the sequencing depth beyond this point.
Right
The plot shows the Median Genes per Cell as a function of downsampled sequencing depth in mean reads per cell. The slope of the curve near the endpoint can be interpreted as an upper bound to the benefit to be gained from increasing the sequencing depth beyond this point.