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iCellR (version 1.6.4)

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

495

Version

1.6.4

License

GPL-2

Issues

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Stars

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Repository

https://github.com/rezakj/iCellR

Maintainer

Alireza Khodadadi-Jamayran

Last Published

April 27th, 2021

Functions in iCellR (1.6.4)

Merge RNA and ADT data

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

heatmap.gg.plot

Create heatmaps for genes in clusters or conditions.

pseudotime.knetl

iCellR KNN Network

Calculate Cell cycle phase prediction

run.diffusion.map

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

Impute the main data

clust.stats.plot

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

Add CITE-seq antibody-derived tags (ADT)

Load h5 data as data.frame

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

Remove the cells that are in a cluster

Load 10X data as data.frame

Add V(D)J recombination data

An object of class iCellR for demo

Run anchor alignment on the main data.

A dataset of S phase genes

Run CCA on the main data

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

Cell cycle phase prediction

A dataset of G2 and M phase genes

Demultiplexing HTOs

Assign cluster number to cell ids

RphenoGraph clustering

Add image data to iCellR object

Cell cycle phase prediction

Plot nGenes, UMIs and percent mito

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

Find optimal number of PCs for clustering

Prepare VDJ data

clust.cond.info

Calculate cluster and conditions frequencies

Make 2D and 3D scatter plots for clonotypes.

Create a data frame of mean expression of genes per cluster

Down sample conditions

Sort and relabel the clusters randomly or based on pseudotime

Find model genes from PCA data

iCellR Batch Alignment (IBA)

iCellR Clustering

Create an object of class iCellR.

iCellR KNN Network

Find marker genes for each cluster

Clustering the data

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

Run MNN alignment on the main data.

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

Change the cluster number or re-name them

K Nearest Neighbour Search

Differential expression (DE) analysis

Clustering the data

volcano.ma.plot

Create MA and Volcano plots.

Make scatter, box and bar plots for genes

make.gene.model

Make a gene model for clustering

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

pseudotime.tree

Pseudotime Tree

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

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

Choose top marker genes

Run PCA on the main data

Plot nGenes, UMIs and perecent mito

capture.image.10x

Read 10X image data

data.aggregation

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