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iCellR (version 1.6.7)

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

1.6.7

License

GPL-2

Issues

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Repository

https://github.com/rezakj/iCellR

Maintainer

Alireza Khodadadi-Jamayran

Last Published

January 29th, 2024

Functions in iCellR (1.6.7)

data.aggregation

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

Make scatter, box and bar plots for genes

iCellR Clustering

Assign cluster number to cell ids

iCellR Batch Alignment (IBA)

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

A dataset of G2 and M phase genes

Cell cycle phase prediction

Demultiplexing HTOs

Create an object of class iCellR.

Find model genes from PCA data

Load 10X data as data.frame

Load h5 data as data.frame

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

pseudotime.knetl

iCellR KNN Network

clust.stats.plot

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

Plot nGenes, UMIs and perecent mito

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

run.diffusion.map

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

Run PCA on the main data

iCellR KNN Network

Run MNN alignment on the main data.

Impute the main data

Clustering the data

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

A dataset of S phase genes

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

Choose top marker genes

Differential expression (DE) analysis

Clustering the data

heatmap.gg.plot

Create heatmaps for genes in clusters or conditions.

K Nearest Neighbour Search

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

Find optimal number of PCs for clustering

Find marker genes for each cluster

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

Prepare VDJ data

Plot nGenes, UMIs and percent mito

Run anchor alignment on the main data.

volcano.ma.plot

Create MA and Volcano plots.

make.gene.model

Make a gene model for clustering

Run CCA on the main data

pseudotime.tree

Pseudotime Tree

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

Add CITE-seq antibody-derived tags (ADT)

capture.image.10x

Read 10X image data

Calculate Cell cycle phase prediction

Add V(D)J recombination data

Create bubble heatmaps for genes in clusters or conditions.

Cell cycle phase prediction

RphenoGraph clustering

Merge RNA and ADT data

Create a data frame of mean expression of genes per cluster

Sort and relabel the clusters randomly or based on pseudotime

Remove the cells that are in a cluster

Down sample conditions

Add image data to iCellR object

Change the cluster number or re-name them

Make 2D and 3D scatter plots for clonotypes.

clust.cond.info

Calculate cluster and conditions frequencies

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