PMID_29988129
Dataset Information
This report has been verified by Polly as per framework version 1.0 Learn More
QUALITY CONTROL CONTENT
- Distribution of Key Quality Control Metrics
- UMAP Visualization of Cells Colored by Sample
- Stacked Barplot of Cell Types Distributed Across Samples
- Stacked Barplot of Clusters Distributed Across Samples
- Stacked Barplot of Cell-types Distributed Across Clusters
- Distribution of (a) Cell Counts (b) Median Gene Counts (c) Median Mitochondrial Genes, Across Samples
- Gene Counts Distribution
- UMI Count Distribution
- UMI Vs Gene Counts Distribution Scatter Plots Colored by Density
1. Distribution of Key Quality Control Metrics
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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.
A good-quality dataset would typically have a reasonable number of genes detected per cell and a moderate total transcript count. High mitochondrial transcript percentages can indicate low-quality, dying cells. Please Note: certain datasets do not have mitochondrial genes (MT-), thus figure for percentage of mitochondrial transcripts may be empty.
2. UMAP Visualization of Cells Colored by Sample
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Figure 2: Sample level distribution of clustering pattern of cells with the help of UMAP embeddings.
If cells from the same sample cluster together distinctly from cells of other samples, it may indicate the presence of batch effects. Ideally, cells should be mixed and group based on their biological characteristics rather than their originating sample, indicating that the data is free of significant batch effects and the samples are comparable.
3. Stacked Barplot of Cell Types Distributed Across Samples
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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.
A uniform distribution of cell types across samples, may suggest that the sample preparation and preprocessing methods used were effective and there was minimal bias or variation in the processing steps. In some cases, if the experiment design ensures enrichment of a cell-type in a sample, then a non-uniform distribution is also valid.
4. Stacked Barplot of Clusters Distributed Across Samples
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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.
Generally, a uniform distribution of clusters across samples, suggests there was minimal bias or variation in the processing steps.
5. Stacked Barplot of Cell-types Distributed Across Clusters
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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.
Generally, each cluster should have only one cell-type to indicate accurate cell-type annotation. A corner-cases are observed when the authors have only provided cell ID to cell-type mapping and no marker genes. These need to manually rectified.
6. Distribution of (a) Cell Counts (b) Median Gene Counts (c) Median Mitochondrial Genes, Across Samples
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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.
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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.
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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
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Figure 7: The plot provides a smoothed representation of the distribution of detected genes across cells.
This plot gives an idea about the average gene richness in cells. High variability might indicate a mix of high and low-quality cells.
8. UMI Count Distribution
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Figure 8: The plot provides a smoothed representation of the distribution of UMIs across cells.
This plot offers insight into the typical transcriptomic depth of the dataset. A broad distribution might indicate variability in sequencing depth across cells.
9. UMI Vs Gene Counts Distribution Scatter Plots Colored by Density
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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.
Ideally, one would expect to see a positive correlation between UMIs and genes, indicating that cells with more transcripts also express more unique genes. Areas with higher density may represent the most typical cells in the dataset, while outliers could indicate low-quality cells or potential doublets.
QUALITY ASSURANCE CONTENT
- Metadata Information
- Data Matrix
- Cell Clusters in UMAP Embeddings Colored by Samples: Re-processed and Polly Datasets
- Cell Clusters in UMAP Embeddings Colored by 'Author Cell Types': Comparison Between Polly and Re-processed Datasets
- Cell Clusters in UMAP Embeddings Colored by 'Curated Cell Types': Comparison Between Polly Dataset and Re-processed Data
- Cell Type Frequency Distribution
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.
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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.
The plot visualizes the distribution of samples across various clusters. For both Polly and reprocessed dataset, these should appear very similar. Additionally the plot for Polly datasets can be used to understand if there is any batch-effect.
Sample Clustering: If samples are grouped in a diverse manner, where cells from the same sample are not closely clustered together, this suggests no batch effects on samples.Batch Effect Evidence: If the opposite is true, with cells from the same sample clustering together, there might be evidence of batch effects on samples.Biological Variation Check: It's essential to ensure that any batch effects observed are not due to inherent biological differences between samples.Distribution Visualization: The plot also illustrates how samples are spread across different clusters, providing insights into their distribution.Limitation of Reprocessed dataset: Note that using the UMAP/tSNE plot for reprocessed dataset may not be a valid approach to assess batch effects on samples, particularly when dealing with re-processed data primarily focused on reproducibility checks.
4. Cell Clusters in UMAP Embeddings Colored by 'Author Cell Types': Comparison Between Polly and Re-processed Datasets
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Figure 2a: Author cell type level distribution of clustering pattern of cells with the help of UMAP embeddings on the existing on polly data.
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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.
Cell Type Distribution (author-defined): The plot visualizes the distribution of author-defined cell types across various clusters. As a quality check, for both Polly and reprocessed dataset, these should appear very similar.
Cell Type Similarity: UMAP plot also reveals the degree of similarity between different cell types. If cell types A and B are closely clustered, their gene expression patterns are similar, indicating biological similarities between these cell types.
5. Cell Clusters in UMAP Embeddings Colored by 'Curated Cell Types': Comparison Between Polly Dataset and Re-processed Data
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Figure 3a: Curated cell type level distribution of clustering pattern of cells with the help of UMAP embeddings on the existing on polly data.
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Figure 3b: 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.
Cell Type Distribution by Elucidata (Curation Experts): The plot visualizes how curated cell types are distributed among different clusters. As a quality check, For both Polly and reprocessed dataset, these should appear very similar.
Cell Type Relationships: It shows the proximity of different cell types within the clusters. If cell types A and B cluster closely, it suggests similar gene expression patterns between them, indicating biological similarities between these cell types.
6. Cell Type Frequency Distribution
Table 1: Table displaying author cell types, curated cell types and the number of cells for each cell-type
Authors frequently supply cell types that may not adhere to ontological standards or utilize abbreviations and marker gene names. These are substituted with ontological terms. The table offers insight into the degree of alignment between the ontological terms and the terms provided by the authors.
1. Expression of Marker Genes Across Cell Types
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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.
Marker genes that are predominantly expressed in specific cell types validate the identified cell populations and help in characterizing and annotating them.
2. Expression of Marker Genes Across Clusters
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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.
This visualization aids in understanding the heterogeneity within the dataset and can hint at different cellular states or subtypes within a cell type.
3. UMAP Plots for Categorical Metadata
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Figure 3: The UMAP visualization represents cells in a reduced dimensional space, with colors indicating various categorical attributes.
A uniform distribution of cell types across samples, may suggest that the sample preparation and preprocessing methods used were effective and there was minimal bias or variation in the processing steps. In some cases, if the experiment design ensures enrichment of a cell-type in a sample, then a non-uniform distribution is also valid.
4. UMAP Plots for Polly curated metadata
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Figure 4: This UMAP visualization represents cells in a reduced dimensional space, with colors indicating the Polly curated fields.