R: Analyzing High-Throughput Single Cell Sequencing Data

# Analyzing High-Throughput Single Cell Sequencing Data

## Documentation for package ‘iCellR’ version 1.7.0

- DESCRIPTION file

## Help Pages

add.10x.image | Add image data to iCellR object
add.adt | Add CITE-seq antibody-derived tags (ADT)
add.vdj | Add V(D)J recombination data
adt.rna.merge | Merge RNA and ADT data
bubble.gg.plot | Create bubble heatmaps for genes in clusters or conditions.
capture.image.10x | Read 10X image data
cc | Calculate Cell cycle phase prediction
cell.cycle | Cell cycle phase prediction
cell.filter | Filter cells
cell.gating | Cell gating
cell.type.pred | Create heatmaps or dot plots for genes in clusters to find thier cell types using ImmGen data.
change.clust | Change the cluster number or re-name them
clono.plot | Make 2D and 3D scatter plots for clonotypes.
clust.avg.exp | Create a data frame of mean expression of genes per cluster
clust.cond.info | Calculate cluster and conditions frequencies
clust.ord | Sort and relabel the clusters randomly or based on pseudotime
clust.rm | Remove the cells that are in a cluster
clust.stats.plot | Plotting tSNE, PCA, UMAP, Diffmap and other dim reductions
cluster.plot | Plot nGenes, UMIs and perecent mito
data.aggregation | Merge multiple data frames and add the condition names to their cell ids
data.scale | Scale data
down.sample | Down sample conditions
find.dim.genes | Find model genes from PCA data
findMarkers | Find marker genes for each cluster
find_neighbors | K Nearest Neighbour Search
g2m.phase | A dataset of G2 and M phase genes
gate.to.clust | Assign cluster number to cell ids
gene.plot | Make scatter, box and bar plots for genes
gene.stats | Make statistical information for each gene across all the cells (SD, mean, expression, etc.)
gg.cor | Gene-gene correlation. This function helps to visulaize and calculate gene-gene correlations.
heatmap.gg.plot | Create heatmaps for genes in clusters or conditions.
hto.anno | Demultiplexing HTOs
i.score | Cell cycle phase prediction
iba | iCellR Batch Alignment (IBA)
iclust | iCellR Clustering
load.h5 | Load h5 data as data.frame
load10x | Load 10X data as data.frame
make.bed | Make BED Files
make.gene.model | Make a gene model for clustering
make.obj | Create an object of class iCellR.
myImp | Impute data
norm.adt | Normalize ADT data. This function takes data frame and Normalizes ADT data.
norm.data | Normalize data
opt.pcs.plot | Find optimal number of PCs for clustering
prep.vdj | Prepare VDJ data
pseudotime | Pseudotime
pseudotime.knetl | iCellR KNN Network
pseudotime.tree | Pseudotime Tree
qc.stats | Calculate the number of UMIs and genes per cell and percentage of mitochondrial genes per cell and cell cycle genes.
Rphenograph | RphenoGraph clustering
run.anchor | Run anchor alignment on the main data.
run.cca | Run CCA on the main data
run.clustering | Clustering the data
run.diff.exp | Differential expression (DE) analysis
run.diffusion.map | Run diffusion map on PCA data (PHATE - Potential of Heat-Diffusion for Affinity-Based Transition Embedding)
run.impute | Impute the main data
run.knetl | iCellR KNN Network
run.mnn | Run MNN alignment on the main data.
run.pc.tsne | Run tSNE on PCA Data. Barnes-Hut implementation of t-Distributed Stochastic Neighbor Embedding
run.pca | Run PCA on the main data
run.phenograph | Clustering the data
run.tsne | Run tSNE on the Main Data. Barnes-Hut implementation of t-Distributed Stochastic Neighbor Embedding
run.umap | Run UMAP on PCA Data (Computes a manifold approximation and projection)
s.phase | A dataset of S phase genes
spatial.plot | Plot nGenes, UMIs and perecent mito, genes, clusters and more on spatial image
stats.plot | Plot nGenes, UMIs and percent mito
top.markers | Choose top marker genes
vdj.stats | VDJ stats
volcano.ma.plot | Create MA and Volcano plots.