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iCellR (version 1.5.1)

Analyzing High-Throughput Single Cell Sequencing Data

Description

A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq and CITE-Seq. 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) and Khodadadi-Jamayran, et al (2020) for more details.

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Install

install.packages('iCellR')

Monthly Downloads

422

Version

1.5.1

License

GPL-2

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Repository

https://github.com/rezakj/iCellR

Maintainer

Alireza Khodadadi-Jamayran

Last Published

June 17th, 2020

Functions in iCellR (1.5.1)

Change the cluster number or re-name them

Make 2D and 3D scatter plots for clonotypes.

clust.stats.plot

Plotting tSNE, PCA, UMAP, Diffmap and other dim reductions

Remove the cells that are in a cluster

clust.cond.info

Calculate cluster and conditions frequencies

Create a data frame of mean expression of genes per cluster

Create heatmaps or dot plots for genes in clusters to find thier cell types using ImmGen data.

Plot nGenes, UMIs and perecent mito

data.aggregation

Merge multiple data frames and add the condition names to their cell ids

Find marker genes for each cluster

Gene-gene correlation. This function helps to visulaize and calculate gene-gene correlations.

Make statistical information for each gene across all the cells (SD, mean, expression, etc.)

Assign cluster number to cell ids

Make scatter, box and bar plots for genes

A dataset of G2 and M phase genes

Add V(D)J recombination data

Prepare VDJ data

Down sample conditions

Find model genes from PCA data

Load h5 data as data.frame

Load 10X data as data.frame

iCellR KNN Network

Run UMAP on PCA Data (Computes a manifold approximation and projection)

run.diffusion.map

Run diffusion map on PCA data (PHATE - Potential of Heat-Diffusion for Affinity-Based Transition Embedding)

Run MNN alignment on the main data.

Plot nGenes, UMIs and percent mito

pseudotime.tree

Pseudotime Tree

Calculate the number of UMIs and genes per cell and percentage of mitochondrial genes per cell and cell cycle genes.

Find optimal number of PCs for clustering

An object of class iCellR for demo

heatmap.gg.plot

Create heatmaps for genes in clusters or conditions.

Normalize ADT data. This function takes data frame and Normalizes ADT data.

Demultiplexing HTOs

iCellR Batch Alignment (IBA)

A dataset of S phase genes

Choose top marker genes

Clustering the data

make.gene.model

Make a gene model for clustering

Create an object of class iCellR.

iCellR Clustering

Differential expression (DE) analysis

Impute the main data

Clustering the data

Run tSNE on the Main Data. Barnes-Hut implementation of t-Distributed Stochastic Neighbor Embedding

Run anchor alignment on the main data.

volcano.ma.plot

Create MA and Volcano plots.

Run CCA on the main data

Run tSNE on PCA Data. Barnes-Hut implementation of t-Distributed Stochastic Neighbor Embedding

Run PCA on the main data

Add CITE-seq antibody-derived tags (ADT)

Merge RNA and ADT data

Cell cycle phase prediction

Calculate Cell cycle phase prediction