1. Distribution of Key Quality Control Metrics

Figure 1: These violin plots display the distribution of quality control metrics for each cell. Metrics include the number of genes detected, total transcript counts, and the percentage of mitochondrial transcripts.

2. UMAP visualization of cells colored by sample

Figure 2: Sample level distribution of clustering pattern of cells with the help of UMAP embeddings.

3. Stacked barplot of cell types distributed across samples

Figure 3: The bar plot showcases the distribution and abundance of different cell types within each sample. Each color in a bar represents a different cell type with the height of the color segment indicating the count of that cell type in the sample.

4. Stacked barplot of clusters distributed across samples

Figure 4: The bar plot showcases the distribution and abundance of different clusters within each sample. Each color in a bar represents a different cluster with the height of the color segment indicating the count of that cluster in the sample.

5. Stacked barplot of cell-types distributed across clusters

Figure 5: The bar plot showcases the distribution and abundance of different cell types within each cluster. Each color in a bar represents a different cell-type with the height of the color segment indicating the count of that cell-type in the cluster.

6. Distribution of (a) Cell Counts (b) Median Gene Counts (c) Median Mitochondrial Genes, across Samples

Figure 6a: The bar plot visualizes the total count of cells detected in each sample. Each bar corresponds to a different sample, with its height representing the number of cells.

This plot provides an understanding of the sample distribution in terms of cellularity. A wide variance in cell numbers across samples might indicate inconsistencies in cell isolation, sample preparation, or sequencing depth. Consistent cell counts across samples, however, would suggest a more uniform sampling process.

Figure 6b:  The bar plot illustrates the median number of genes detected in each sample. Each bar represents a different sample, and its height corresponds to the median gene counts.

Consistently low gene counts might indicate low sequencing depth or poor-quality samples. On the other hand, large variances between samples or cell types might point to technical biases or true biological differences.

Figure 6c: The bar plot showcases the median percentage of mitochondrial gene transcripts across samples.

Consistently high mitochondrial gene percentages across samples might indicate a widespread issue with cell viability, while sporadic high values could suggest sample-specific issues which can be removed before downstream analysis

7. Gene Counts Distribution

Figure 7: The plot provides a smoothed representation of the distribution of detected genes across cells.

8. UMI Count Distribution

Figure 8: The plot provides a smoothed representation of the distribution of UMIs across cells.

9. UMI vs Gene counts distribution scatter plots colored by density

Figure 9: The scatter plot provides a visual representation of the relationship between the number of unique molecular identifiers (UMIs) and the number of genes detected in single cells. The color intensity indicates the density of data points in a particular region of the plot, allowing for the identification of trends and patterns.

1. Metadata information

2. Data Matrix

3. Cell Clusters in umap Embeddings Colored by Samples: Re-Processed and Polly Datasets

Figure 1a: Sample level distribution of clustering pattern of cells with the help of umap embeddings on the existing on polly data.

Figure 1b: Sample level distribution of clustering pattern of cells with the help of umap embeddings on the re - processed data to validate reproducibility of results.

4. Cell Clusters in umap Embeddings Colored by 'Author Cell Types': Comparison Between Polly and Re-Processed Datasets

Figure 2a: Author cell type level distribution of clustering pattern of cells with the help of umap embeddings on the existing on polly data.

Figure 2b: Author cell type level distribution of clustering pattern of cells with the help of umap embeddings on the re - processed data to validate reproducibility of results.

5. Cell Clusters in umap Embeddings Colored by 'Curated Cell Types': Comparison Between Polly Dataset and Re-Processed Data

Figure 5a: Curated cell type level distribution of clustering pattern of cells with the help of umap embeddings on the existing on polly data.

Figure 5b: Curated cell type level distribution of clustering pattern of cells with the help of umap embeddings on the re - processed data to validate reproducibility of results.

6. Violin plot visualization for doublet

Figure 5: Sanity check of detected doublets

1. Expression of Marker Genes Across Cell Types

Figure 1: The dot plot showcases the expression levels (often represented by dot size) and prevalence (often represented by dot color intensity) of specific marker genes across different cell types.

2. Expression of Marker Genes Across Clusters

Figure 2: The dot plot showcases the expression levels (often represented by dot size) and prevalence (often represented by dot color intensity) of specific marker genes across different clusters.

3. umap plots for categorical metadata

Figure 3: The umap visualization represents cells in a reduced dimensional space, with colors indicating various categorical attributes.

4. umap plots for Polly curated metadata

Figure 4: This umap visualization represents cells in a reduced dimensional space, with colors indicating the Polly curated fields.

5. Sunburst plots for metadata fields

Figure 5: A Sunburst plot illustrating the distribution of data. It reflects user-defined custom fields if specified; otherwise, it represents standard fields.