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iCellR (version 1.6.5)

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, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). 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

1.6.5

License

GPL-2

Issues

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Repository

https://github.com/rezakj/iCellR

Maintainer

Alireza Khodadadi-Jamayran

Last Published

October 9th, 2021

Functions in iCellR (1.6.5)

Cell cycle phase prediction

Add V(D)J recombination data

Add image data to iCellR object

RphenoGraph clustering

Add CITE-seq antibody-derived tags (ADT)

Calculate Cell cycle phase prediction

Merge RNA and ADT data

capture.image.10x

Read 10X image data

Plot nGenes, UMIs and perecent mito

Create a data frame of mean expression of genes per cluster

Make 2D and 3D scatter plots for clonotypes.

Find marker genes for each cluster

Find model genes from PCA data

clust.cond.info

Calculate cluster and conditions frequencies

Sort and relabel the clusters randomly or based on pseudotime

Cell cycle phase prediction

Down sample conditions

Remove the cells that are in a cluster

K Nearest Neighbour Search

data.aggregation

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

iCellR Batch Alignment (IBA)

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

heatmap.gg.plot

Create heatmaps for genes in clusters or conditions.

Make scatter, box and bar plots for genes

Assign cluster number to cell ids

Demultiplexing HTOs

Prepare VDJ data

Change the cluster number or re-name them

Clustering the data

Load 10X data as data.frame

A dataset of S phase genes

clust.stats.plot

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

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

pseudotime.knetl

iCellR KNN Network

A dataset of G2 and M phase genes

Clustering the data

pseudotime.tree

Pseudotime Tree

Run MNN alignment on the main data.

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

Run anchor alignment on the main data.

volcano.ma.plot

Create MA and Volcano plots.

Run PCA on the main data

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

Run CCA on the main data

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

Differential expression (DE) analysis

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

Plot nGenes, UMIs and percent mito

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

run.diffusion.map

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

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

Choose top marker genes

Create an object of class iCellR.

make.gene.model

Make a gene model for clustering

Find optimal number of PCs for clustering

Load h5 data as data.frame

iCellR KNN Network

Impute the main data

iCellR Clustering