Single-cell RNA-seq Analysis Tool

Manage Checkpoints

User Guide

Welcome to baSeq's Single-Cell RNA Sequencing Analysis Tool! Follow these steps to get started:

Upload your data: Use the paperclip icon in the sidebar to upload your single-cell RNA-seq data files.
Start your analysis: Click on "1. Describe and plot data" to begin your analysis.
Proceed with analysis: Follow subsequent analysis steps to explore and interpret your data.
Save checkpoints: Use the bookmark icon in the sidebar to save and load analysis checkpoints during your session. This is recommented between each analysis step
Chat about your data Click on the logo in the bottom left to chat with the model about your data and plots.

Each analysis step is designed to help you gain deeper insights into your single-cell data. Provide additional context or use standardized parameters by leaving input boxes blank. If results aren't as expected, revisit previous steps to adjust parameters or context.

Analysis Controls ℹ️ Click the buttons to start analysing your data. First click will open a text box for submitting context to the analysis step. Leave it blank for standardized parameters. Press submit to run analysis.

This step serves as a baseline visualization of your analysis. It will produce plots showing the distribution of counts, mitochondrial genes, and total counts vs the number of genes. You can revisit this step to see how your analysis affects these visualizations as you progress.

This step removes outliers from your data. It is important to remove outliers to ensure that the analysis is not skewed by extreme values.

This step removes doublets from your data. Doublets are cells that are mistakenly identified as two separate cells. Removing doublets ensures that the analysis is not skewed by these errors.

This step normalizes the data to ensure that the analysis is not skewed by differences in sequencing depth or other technical factors.

This step identifies highly variable features in your data. These features are important for distinguishing between different cell types and states.

This step performs Principal Component Analysis (PCA) on your data. PCA is used to reduce the dimensionality of the data while preserving the most important features.

This step integrates data from multiple samples or batches to remove batch effects and harmonize the data for downstream analysis.

This step computes the nearest neighbors of each cell in the data. Nearest neighbors are used to identify cell types and states based on the similarity of their gene expression profiles.

This step performs clustering on the data to identify groups of cells with similar gene expression profiles. Clustering is used to identify cell types and states in the data.

This step ranks genes based on their importance in distinguishing between different cell types and states. The ranked genes can be used to identify marker genes for specific cell types.

This step identifies genes that are differentially expressed between different cell types or states. Differential gene expression analysis is used to identify genes that are specific to certain cell types or states.

This step explains the results of the differential gene expression analysis. It provides insights into the biological significance of the differentially expressed genes and their role in distinguishing between cell types or states.

This step annotates cells based on their gene expression profiles. Cell annotation assigns cell types to individual cells based on the expression of marker genes. NOTE: it is a good idea to provide your sample type to the model in this step!

This step subsets your data based on the provided cluster, and highlights the most expressed genes in the given subset. NOTE: tell the model which cluster you wish to subset.

This step performs trajectory analysis to infer the developmental paths of cells in the data. Trajectory analysis is used to identify the order of cell states and transitions between them.

This step analyzes cell communication networks in the data. Cell communication analysis is used to identify interactions between different cell types and states.

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Single-Cell RNA Sequencing Analysis Results

Status:

We would greatly appreciate hearing your opinions and feedback through our survey: https://forms.gle/ywrhkj5er8PKbTYS7

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Example inputs:

"Change the colormap to viridis"
"Plot ribosomal counts"

Example inputs:

"Remove cells with more than 10% mitochondrial genes"
"Filter cells with less than 5000 total counts"

Example inputs:

"Use a different method than scrublet"
"Filter cells based on cell cycle scores"

Example inputs:

"Set target_sum to 1e6"
"Scale data using the Z-score method"

Example inputs:

"Identify top 1000 variable features"
"set minimum mean expression to 0.015"
"set maximum mean expression to 4"
"set minimum dispersion to 0.6"

Example inputs:

"Perform PCA using the top 50 principal components"
"Plot PCA results colored by cell type"

Example inputs:

"Color the batches in green and red etc. instead"

Example inputs:

"Compute 10 nearest neighbors for each cell"
"Plot UMAP results colored by nearest neighbors"

Example inputs:

"Perform Louvain clustering on the data"
"Change leiden resolution to 0.75"

Example inputs:

"Rank genes in cluster 1 and 2"
"Select top 20 marker genes"
"Use a leiden resolution "

Example inputs:

"Perform differential gene expression analysis between cell types"
"Identify differentially expressed genes based on log-fold change"

Example inputs:

"My sample is PBMCs"
"My sample is tonsil tissue"
"Etc."

Example inputs:

"Subset cluster 3"
"Subset cluster 1 and 2"
"Etc."

Example inputs:

"change trajectory threshold to 0.25"
""

Example inputs:

"Plot 5 most prominent ligand receptor interactions"
"change to a different colormap"

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Manage Checkpoints

User Guide

Single-Cell RNA Sequencing Analysis Results