Analyzing High-Throughput Single Cell Sequencing Data

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Documentation for package ‘iCellR’ version 1.5.1

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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
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.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
demo.obj An object of class iCellR for demo
down.sample Down sample conditions
find.dim.genes Find model genes from PCA data
findMarkers Find marker genes for each cluster
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
iba iCellR Batch Alignment (IBA)
iclust iCellR Clustering
load.h5 Load h5 data as data.frame
load10x Load 10X data as data.frame
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.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.
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
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.