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iCellR (version 1.6.1)

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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Version

Install

install.packages('iCellR')

Monthly Downloads

495

Version

1.6.1

License

GPL-2

Issues

Pull Requests

Stars

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Repository

https://github.com/rezakj/iCellR

Maintainer

Alireza Khodadadi-Jamayran

Last Published

March 4th, 2021

Functions in iCellR (1.6.1)

Plot nGenes, UMIs and perecent mito

Find marker genes for each cluster

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

heatmap.gg.plot

Create heatmaps for genes in clusters or conditions.

K Nearest Neighbour Search

data.aggregation

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

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

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

Remove the cells that are in a cluster

Change the cluster number or re-name them

capture.image.10x

Read 10X image data

clust.stats.plot

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

Down sample conditions

Find model genes from PCA data

Load h5 data as data.frame

Find optimal number of PCs for clustering

Assign cluster number to cell ids

Create a data frame of mean expression of genes per cluster

A dataset of G2 and M phase genes

Make 2D and 3D scatter plots for clonotypes.

pseudotime.knetl

iCellR KNN Network

Impute the main data

Demultiplexing HTOs

iCellR KNN Network

pseudotime.tree

Pseudotime Tree

Cell cycle phase prediction

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

clust.cond.info

Calculate cluster and conditions frequencies

Sort and relabel the clusters randomly or based on pseudotime

Make scatter, box and bar plots for genes

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

Run CCA on the main data

Run anchor alignment on the main data.

Clustering the data

iCellR Batch Alignment (IBA)

make.gene.model

Make a gene model for clustering

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

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

Create an object of class iCellR.

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

Prepare VDJ data

Run MNN alignment on the main data.

Load 10X data as data.frame

An object of class iCellR for demo

Differential expression (DE) analysis

Plot nGenes, UMIs and percent mito

Clustering the data

Plot nGenes, UMIs and perecent mito, genes, clusters and more on spatial image

iCellR Clustering

run.diffusion.map

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

volcano.ma.plot

Create MA and Volcano plots.

Run PCA on the main data

Choose top marker genes

A dataset of S phase genes

Calculate Cell cycle phase prediction

RphenoGraph clustering

Add image data to iCellR object

Cell cycle phase prediction

Merge RNA and ADT data

Add CITE-seq antibody-derived tags (ADT)

Add V(D)J recombination data