Quick testing of CIDR implementation

Plot PCA for ethnicity from any given VCF file combined with 1000 genomes data

This visualization plots principal component analysis (PCA) results from a user-provided VCF file, combined with 1000 Genomes reference data, to infer ethnic ancestry. It shows a scatterplot of PC1 versus PC2, where each point represents a sample, colored by population assignment. The code uses the `SNPRelate` package to convert the VCF to GDS format, perform LD-based SNP pruning, and run PCA, while `ggplot2` renders the final plot. Ethnicity labels are merged from an external population table, and a custom publication theme colors the points.

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Use Seurat to find specific markers of each cell type

This example demonstrates how to identify specific marker genes for each cell type in a Seurat single-cell RNA-seq analysis. It shows a workflow that compares every cell type against all others using the `FindMarkers` function with a ROC test, then filters the results to retain only markers expressed in fewer than 50% of cells in all other populations. The code uses the `tidyverse` and `Seurat` libraries to perform pairwise comparisons, calculate the difference in expression percentages, and summarize the average AUC values. The final output is a curated list of the top 10 markers per cell type, emphasizing those with high discriminatory power.

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Cell cycle assignment for single cell data using scran and cyclone with gene symbols instead of ensembl IDs

This example demonstrates how to assign cell cycle phases to single-cell RNA sequencing data by converting the pretrained marker gene pairs in the scran package from Ensembl IDs to gene symbols. It shows the resulting phase assignments being added as metadata to a Seurat object for downstream analysis. The code uses the scran cyclone function, the org.Hs.eg.db database for ID conversion via bitr, and dplyr for data manipulation. The visualization implicitly relies on the Seurat object’s structure to store the new cell cycle labels, though no plot is generated in the provided script.

Use Seurat to find specific markers of each cell type (modified from Dan Skelly)

This example shows a dot plot of the top five specific marker genes for each cell type in a single-cell RNA-sequencing dataset, with dot size and color encoding expression level and percentage of cells expressing each gene. The visualization relies on Seurat’s `FindMarkers` function with a ROC test to perform all pairwise comparisons between cell types, filtering for markers expressed in fewer than 50% of cells in other clusters and retaining those with a mean AUC above 0.65. The code uses tidyverse functions like `mutate`, `filter`, `summarize`, and `group_by` to process the marker data, and Seurat’s `DotPlot` to render the final figure. Marker lists are written to TSV files, and the plot is saved as a PNG.

Plot mean expression of a gene vs other parameters and highlight specific genes using ggrepel

This example uses the scater package to calculate quality metrics for single-cell RNA-seq data, then plots mean gene expression against percent dropout and percent total expression. The ggplot2 code highlights a user-defined set of genes of interest in red, while ggrepel’s `geom_text_repel` labels those genes to avoid overlapping points. Two separate scatterplots are generated from the same `gene_info` data frame, each saved as an SVG, showing how specific genes relate to global expression distributions. The analysis relies on the `newSCESet` and `calculateQCMetrics` functions from scater to derive the plotted metrics.

Check correlation between number of genic reads and genes and transcripts detected per cell

This example uses scatter plots to examine the correlation between the number of genic reads and the number of genes or transcripts detected per cell. Two separate plots are generated, each coloring points by the third variable—either transcript count or gene count—to reveal potential relationships. The code relies on the ggplot2 library in R, using `ggplot()` with `geom_point()` and an `aes()` mapping for the x-axis, y-axis, and color. The data is read from a tab-separated table via `read.table()`, and the visualizations are based on the summary statistics in the `DGE_info` data frame.

Scatterplot of Breast Cancer

This scatterplot shows the relationship between clump thickness and cell size in the Breast Cancer dataset, with each point representing a sample. The code loads the CSV data from a GitHub gist using `d3.csv`, parses the numeric fields, and maps them to x and y positions via `d3.scaleLinear` with an auto-computed extent. Circles are rendered as SVG elements, and axes are added using `d3.axisLeft` and `d3.axisBottom`. The visualization fills the browser window, with margins and a fixed point radius, demonstrating a basic D3 v6 data-joining workflow.

RRidhima Saxena

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