Sample information : $ID

Estimated number of cells: The total number of barcodes identified as cells.
Median UMI counts per cell: Among barcodes identified as cells, the median number of UMI per barcode.
Median genes per cell: The median number of genes per cell.
Mean reads per cell: The mean number of reads per cell.

$estm_Num_cell

Estimated number of cell

$median_UMI_per_c

Median UMI counts per cell

$median_genes_per_c

Median genes per cell

$mean_r_per_c

Mean reads per cell

Beads to cells

(left) Beads to cells plot: In library, different mRNAs from the same cell will carry the same cell barcode and random unique molecular identifiers (UMIs). However, some mRNA from dead cells mixed in reflection system was inevitability. This graph shows the distribution of UMI counts in each barcode. Barcodes can be determined to be cell-associated based on their UMI counts or by their expression profiles. Therefore, the graph contains both cell-associated (Colored Regions) and background-associated barcodes (Gray Regions).
(right) Histogram: Histograms of number of beads per droplet.

$plot1

$plot3

Summary

Estimated number of cell: Number of cell-associated barcodes. In theory, a cell barcode represents a cell.
Mean reads per cell: The mean number of reads detected per cell-associated barcode.
Mean UMI counts per cell: The average number of UMI counts per cell-associated barcode. This item refers the average abundance of expression in each cell.
Median UMI counts per cell: The median number of UMI counts per cell-associated barcode.
Total genes detected: The total number of genes detected.
Mean genes per cell: The mean number of genes detected per cell-associated barcode. This item refers the activity of gene expression in each cell.
Median genes per cell: The median number of genes detected per cell-associated barcode.
Number of cells used for clustering: The number of cells used for clustering.
Plot: The distribution of nFeature and nCount in violin.

Sample name

$samplename

Species

$species

Estimated number of cell

$estm_Num_cell

Mean reads per cell

$mean_r_per_c

Mean UMI count per cell

$mean_UMI_per_c

Median UMI counts per cell

$median_UMI_per_c

Total genes detected

$total_gene

Mean genes per cell

$mean_genes_per_c

Median genes per cell

$median_genes_per_c

Number of cells used for clustering

$cluster_cell

$plot4

Sequencing

Number of reads: Number of raw off-machine reads obtained by this sequencing program.
Reads pass QC: Number of reads after quality control (QC) that can be used for downstream analysis. QC includes filtering low quality reads and invalid barcodes.
Reads with exactly matched barcodes: Number of reads with barcodes that match the barcode whitelist after correction.
Reads with failed barcodes: Number of reads with invalid barcodes that fail to match the barcode whitelist.
Reads filtered on low quality: Number of reads in low quality. The QC will filter the average base quality < Q20 or 2 bases < Q10 in the first 15 bases.
Reads filtered on unknown sample barcode: When sequencing different library samples, sample barcodes are typically used for demultiplexing. QC will filter reads with unknown sample barcode due to sequence error or other reasons. This item refers to number of reads with Unknown Sample Barcodes.
Q30 bases in cell barcode: Fraction of Cell Barcode bases with Q-score ≥ 30.
Q30 bases in sample barcode: Fraction of Sample Barcode bases with Q-score ≥ 30.
Q30 bases in UMI: Fraction of UMI Bases with Q-score ≥ 30.
Q30 bases in reads: Fraction of RNA read bases with Q-score ≥ 30.

mRNA

Number of reads

$cDNA_num_frag

Reads pass QC

$cDNA_frag_pass_QC

Reads with exactly matched barcodes

$cDNA_frag_exact_bar

Reads with failed barcodes

$cDNA_frag_fail_bar

Reads filtered on low quality

$cDNA_frag_low_qual

Reads filtered on unknown sample barcode

$cDNA_frag_unknow_bar

Q30 bases in cell barcode

$cDNA_Q30_c_bar

Q30 bases in sample barcode

$cDNA_Q30_s_bar

Q30 bases in UMI

$cDNA_Q30_UMI

Q30 bases in reads

$cDNA_Q30_r

Droplet index

Number of Reads

$index_num_frag

Reads pass QC

$index_frag_pass_QC

Reads with exactly matched barcodes

$index_frag_exact_bar

Reads with failed barcodes

$index_frag_fail_bar

Reads filtered on low quality

$index_frag_low_qual

Reads filtered on unknown sample barcode

$index_frag_unknow_bar

Q30 bases in cell barcode

$index_Q30_c_bar

Q30 bases in sample barcode

$index_Q30_s_bar

Q30 bases in UMI

$index_Q30_UMI

Q30 bases in reads

$index_Q30_r

Mapping & Annotation

Raw reads: Number of reads after quality control (QC) that can be used for downstream analysis. QC includes filtering low quality reads and invalid barcodes.
Mapped reads: The number of reads that mapped to the genome.
Plus strand: The number of reads in plus strand.
Minus strand: The number of reads in minus strand.
Mitochondria ratio: The fraction of reads mapped to mitochondria.
Mapping quality corrected reads: The number of reads mapped to quality corrected reads.
Reads mapped to genome(Map Quality≥0): Fraction of reads that mapped to the genome.
Reads mapped to exonic regionse: Fraction of reads that mapped to an exonic region of genome..
Reads mapped to intronic regions: Fraction of reads that mapped to an intronic region of genome.
Reads mapped to both exonic and intronic regions: Fraction of reads that mapped to exonic and intronic region of genome.
Reads mapped antisense to gene: Fraction of reads mapped to transcriptome, but on the opposite strand of their annotated gene. This part of reads will be filtered out cause them are opposite to theoretical direction.
Reads mapped to intergenic regions: Fraction of reads that mapped to intergenic regions of genome.
Reads mapped to gene but failed to interpret type: Fraction of reads that mapped to gene but failed to interpret type.

Raw reads

$raw_r

Mapped reads

$map_r

Plus strand

$plus_strd

Minus strand

$minus_strd

Mitochondria ratio

$mito_ratio

Mapping quality corrected reads

$map_qual_corrt_r

Reads mapped to genome (Map Quality ≥ 0)

$r_m_geno

Reads mapped to exonic regions

$r_m_ex

Reads mapped to intronic regions

$r_m_intro

Reads mapped to both exonic and intronic regions

$r_m_ex_intro

Reads mapped antisense to gene

$r_m_anti

Reads mapped to intergenic regions

$r_m_inter

Reads mapped to gene but failed to interpret type

$r_m_gene_fail

Cluster

Left: 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.
Right: 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.

$plot2

$plot5

Marker

Table: The table shows the top 10 differentially expressed genes for each cluster. Here a differential expression test was performed between each cluster and the rest of the sample for each gene. The p-value is a measure of the statistical significance of the expression difference, the smaller the P value, the closer to the theoretical value. Avg_logFC is log fold-chage of the average expression between the markers for every cluster compared to all remaining cells. In this table, if the adjusted P value ≥ 0.10, the gene is grayed out.

$table

Cell Annotation

(left)Predicated cell type: The cell types for each of single cells from the human dataset were inferred by SingleR based on similarity to the Human Primary Cell Atlas (HPCA), for mouse dataset used by scMCA based on the person correlation with the Mouse Cell Atlas. UMAP plots of all cells colored by cell-type identity.The annotation results are just for reference only and are used to determine if the library quality is acceptable.

$plot8

Saturation

Left: The plot shows the Sequencing Saturation metric as a function of downsampled sequencing depth in mean reads per cell, up to the observed sequencing depth. 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, up to the observed sequencing depth. 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.

$plot6

$plot7