This is the lineage tracing module of scLTdb, which offers a range of functions to visualize and re-analyze lineage tracing (cell fate) data modalities within scLT datasets.

This page offers four functions:

1. Unique barcode number in each cell type (state).

2. Number of cells with detected barcodes in each cell type (state).

3. Clone size statistics: visualize the number of cells carry the same clonal barcode.

4. Clone size ranges: visualize clone size across different ranges.

5. Correlation between clone size and clone related cell type number: violin plots of clone size (cell number) and clone-related cell type number.

This is the lineage tracing module of scLTdb, which offers a range of functions to visualize and re-analyze lineage tracing (cell fate) data modalities within scLT datasets.

1. Fate outcomes: this function can assist users to re-map cell fate of targeted populations by visualizing the barcode propagation across various cell types or states:

Users can adjust the ‘fate outcome heatmap’ by the following two ways:

(1). The normalization methods can be selected through the button located on the top of the heatmap. There are two available methods for barcode count normalization: 'log' and 'proportion'. The 'log' method represents log10 normalization of barcode counts, whereas the 'proportion' method represents the proportion of each barcode across all cell types (states).

(2). Users can adjust the column order (cell type order) of the heatmap. This can be done by simply rolling the cell types (states) bars located on the left panel of the heatmap.

2. Fate bias summary: visualize clone fate bias in specific cell types (states). In single-cell lineage tracing data, clone fate bias refers to a particular clone showing a differentiation bias into certain downstream cell types or states.

This is the lineage tracing module of scLTdb, which offers a range of functions to visualize and re-analyze lineage tracing (cell fate) data modalities within scLT datasets.

Here is the 'Lineage relationships' function within the lineage tracing module. This function is used to compare the lineage similarity between cell types (states) based on the number and frequency of barcodes present in each cell type (state). If two cell types share many barcodes at similar frequencies, they are likely to have arisen from a common developmental pathway; if not, they probably developed more independently.

Users can select specific cell types to reconstruct lineage relationships by clicking on ‘Select cell type (state)’.

If this dataset contains Cas9-edited barcodes, this page will present phylogenetic analysis result in the 'Phylogenetic tree' function.

This is the integration module of scLTdb, which offers a range of functions to perform integrative analysis of cell fate and gene expression.

This page offers two functions:

1. Differentially Expressed Genes (DEGs) analysis for cells with different cell fate biases. To utilize this function, users are required to first choose a specific cell type (state) and then select a particular cell fate bias group. This analysis reveals the transcriptional hallmarks between the two different cell fate biases within the selected cell type (state).

2. Enrichment analysis (gene ontology) for cell fate related DEGs.

3. Motif enrichment analysis for cell fate related DARs. This function only works for datasets that contain chromatin accessibility modality.

Please note that if the selected cell fate bias group has no significant DEGs, the scLTdb will display the message ‘No significant DEGs for selected fate bias group’. If cell fate related DEGs cannot be enriched for any functions in the gene ontology database, the scLTdb will display the message ‘Enrichment analysis cannot identify any significant Gene Ontology (GO) terms’.

This is the integration module of scLTdb, which offers a range of functions to perform integrative analysis of cell fate and gene expression.

This page offers three functions:

1. Differentially Expressed Genes (DEGs) analysis for two clones within a specific cell type (state). To utilize this function, users are required to first choose a specific cell type (state) and then select two large clones within this cell type (state). This analysis reveals the transcriptional hallmarks between the two selected large clones.

2. Enrichment analysis (gene ontology) for clone size related DEGs.

3. Motif enrichment analysis for cell fate related DARs. This function only works for datasets that contain chromatin accessibility modality

Please note that if this dataset has no significant large clone DEGs, the scLTdb will display the message ‘No significant DEGs for selected two large clones’. If clone DEGs cannot be enriched for any functions in the gene ontology database, the scLTdb will display the message ‘Enrichment analysis cannot identify any significant Gene Ontology (GO) terms’.

The table below presents the top 5 clones with the largest size in a specific cell type (state).

This is the integration module of scLTdb, which offers a range of functions to perform integrative analysis of cell fate and gene expression.

This page presents a violin plot to display gene expression variation across different cell fate biases within a particular cell type. To utilize this function, users need to first select a specific cell type (state) and enter the gene name into the input box.

This function is only designed for datasets that include the gene expression modality.