Analyzing High-Throughput Single Cell Sequencing Data
Load 10X data as data.frame
Make BED Files
Run CCA on the main data
Make a gene model for clustering
Create an object of class iCellR.
Impute data
Normalize ADT data. This function takes data frame and Normalizes ADT ...
Normalize data
Find optimal number of PCs for clustering
Prepare VDJ data
iCellR KNN Network
Pseudotime
Pseudotime Tree
Calculate the number of UMIs and genes per cell and percentage of mito...
RphenoGraph clustering
Run anchor alignment on the main data.
Add image data to iCellR object
Add CITE-seq antibody-derived tags (ADT)
Add V(D)J recombination data
Merge RNA and ADT data
Create bubble heatmaps for genes in clusters or conditions.
Read 10X image data
Calculate Cell cycle phase prediction
Cell cycle phase prediction
Filter cells
Cell gating
Create heatmaps or dot plots for genes in clusters to find thier cell ...
Change the cluster number or re-name them
Make 2D and 3D scatter plots for clonotypes.
Create a data frame of mean expression of genes per cluster
Calculate cluster and conditions frequencies
Sort and relabel the clusters randomly or based on pseudotime
Remove the cells that are in a cluster
Plotting tSNE, PCA, UMAP, Diffmap and other dim reductions
Plot nGenes, UMIs and perecent mito
Merge multiple data frames and add the condition names to their cell i...
Scale data
Down sample conditions
Find model genes from PCA data
K Nearest Neighbour Search
Find marker genes for each cluster
Assign cluster number to cell ids
Make scatter, box and bar plots for genes
Make statistical information for each gene across all the cells (SD, m...
Gene-gene correlation. This function helps to visulaize and calculate ...
Create heatmaps for genes in clusters or conditions.
Demultiplexing HTOs
Cell cycle phase prediction
iCellR Batch Alignment (IBA)
iCellR Clustering
Load h5 data as data.frame
Clustering the data
Differential expression (DE) analysis
Run diffusion map on PCA data (PHATE - Potential of Heat-Diffusion for...
Impute the main data
iCellR KNN Network
Run MNN alignment on the main data.
Run tSNE on PCA Data. Barnes-Hut implementation of t-Distributed Stoch...
Run PCA on the main data
Clustering the data
Run tSNE on the Main Data. Barnes-Hut implementation of t-Distributed ...
Run UMAP on PCA Data (Computes a manifold approximation and projection...
Plot nGenes, UMIs and perecent mito, genes, clusters and more on spati...
Plot nGenes, UMIs and percent mito
Choose top marker genes
VDJ stats
Create MA and Volcano plots.
A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.05.05.078550> and Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.03.31.019109> for more details.