Update README.md

# plot1cell: a package for advanced single cell data visualization

This package allows users to visualize the single cell data on the R object or output files generated by Seurat. It is currently under active development. 

This R package allows users to visualize the single cell data on the R object or output files generated by Seurat. It is currently under active development. 

## Installation

plot1cell R package can be easily installed from Github using devtools. Please make sure you have installed <a href="https://satijalab.org/seurat/">Seurat 4.0</a>, <a href="https://github.com/jokergoo/circlize">circlize</a> and <a href="https://github.com/jokergoo/ComplexHeatmap">ComplexHeatmap</a> packages.

devtools::install_github("TheHumphreysLab/plot1cell")

## or the development version, devtools::install_github("HaojiaWu/plot1cell")

## You might need to install the dependencies below if they are not available in your R library directory.

## You might need to install the dependencies below if they are not available in your R library.

bioc.packages <- c("biomaRt","GenomeInfoDb","EnsDb.Hsapiens.v86","GEOquery","simplifyEnrichment","ComplexHeatmap")

BiocManager::install(bioc.packages)

dev.packages <- c("chris-mcginnis-ucsf/DoubletFinder","Novartis/hdf5r","mojaveazure/loomR")

```

## Usage

We provide some example codes to help generate figures from user's provided Seurat object. The Seurat object input to plot1cell should be a final object with completed clustering and cell type annotation. If a seurat object is not available, we suggest to use the demo data from Satija's lab (https://satijalab.org/seurat/articles/integration_introduction.html). To demonstrate the plotting functions in plot1cell, we re-created a Seurat object from our recent paper <a href="https://www.pnas.org/doi/10.1073/pnas.2005477117">Kirita et al, PNAS 2020</a> by integrating the count matrices we uploaded to GEO (GSE139107).

We provide some example codes to help generate figures from user's provided Seurat object. The Seurat object input to plot1cell should be a final object with complete clustering and cell type annotation. If a seurat object is not available, we suggest to use the demo data from Satija's lab (https://satijalab.org/seurat/articles/integration_introduction.html). To demonstrate the plotting functions in plot1cell, we re-created a Seurat object from our recent paper <a href="https://www.pnas.org/doi/10.1073/pnas.2005477117">Kirita et al, PNAS 2020</a> by integrating the count matrices we uploaded to GEO ([GSE139107](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE139107)).

```

library(plot1cell)

iri.integrated <- Install.example() 

# Please note that this Seurat object is just for demo purpose and 

# is not exactly the same as we published on PNAS.

# is not exactly the same as the one we published on PNAS.

# It takes about 2 hours to run in a linux server with 500GB RAM and 32 CPU cores.

# You can skip this step and use your own Seurat object instead

```

![alt text](https://github.com/HaojiaWu/Plot1cell/blob/master/data/circlize_plot.png) <br />

### 2. Dotplot to show gene expression across groups

Here is an example to use plot1cell to show one gene expression across different cell types across groups.

Here is an example to use plot1cell to show one gene expression across different cell types in different groups.

```

png(filename =  'dotplot_single.png', width = 4, height = 6,units = 'in', res = 100)

complex_dotplot_single(seu_obj = iri.integrated, feature = "Havcr1",groups = "Group")

dev.off()

```

![alt text](https://github.com/HaojiaWu/Plot1cell/blob/master/data/dotplot_single.png) <br />

If the group factor can be classified by another factor, complex_dotplot_single allows splitting the group factor by another group factor too. Here is an example for demo.

If the group factor can be classified by another factor, ```complex_dotplot_single``` allows splitting the group factor by another group factor too. Here is an example for demo.

```

iri.integrated@meta.data$Phase<-plyr::mapvalues(iri.integrated@meta.data$Group, from = levels(iri.integrated@meta.data$Group), to = c("Healthy",rep("Injury",3), rep("Recovery",2)))

iri.integrated@meta.data$Phase<-as.character(iri.integrated@meta.data$Phase)

```

![alt text](https://github.com/HaojiaWu/Plot1cell/blob/master/data/dotplot_more_groups.png) <br />

Each group factor can be further splitted by its own factor by setting the splitby argument. Note that in this case, the order of the group factors needs to match the order of splitby factors.

Each group factor can be further splitted by its own factor if the ```splitby argument``` is provided. Note that in this case, the order of the group factors needs to match the order of splitby factors.

```

iri.integrated@meta.data$ReplicateID<-plyr::mapvalues(iri.integrated@meta.data$Replicates, from = names(table((iri.integrated@meta.data$Replicates))), to = c(rep("Rep1",3),rep("Rep2",3), rep("Rep3",1)))

iri.integrated@meta.data$ReplicateID<-as.character(iri.integrated@meta.data$ReplicateID)

Many more functions will be added in the future package development. For questions, please raise an issue in this github page or contact <a href="https://humphreyslab.com">TheHumphreysLab</a>. 

### 9. Attributions

This package uses many methods from Seurat (https://github.com/satijalab/seurat) to process the data for ploting. The circlize and heatmap plots were generated by the circlize (https://github.com/jokergoo/circlize) and ComplexHeatmap (https://github.com/jokergoo/ComplexHeatmap) packages. The Upset plot was generated by the ComplexUpset package (https://github.com/krassowski/complex-upset). Most of the graphs were generated by ggplot2 (https://github.com/tidyverse/ggplot2). The package benefits from the following dependencies.

This package uses many methods from Seurat (https://github.com/satijalab/seurat) to process the data for ploting. The circlize and heatmap plots were generated by the circlize (https://github.com/jokergoo/circlize) and ComplexHeatmap (https://github.com/jokergoo/ComplexHeatmap) packages. The Upset plot was generated by the ComplexUpset package (https://github.com/krassowski/complex-upset). Most of other graphs were generated using ggplot2 (https://github.com/tidyverse/ggplot2). The package benefits from the following dependencies.

```

    Seurat,