README.md

In RyanYip-Kat/yipCat: Some function like ArchR style in Seurat Object

yipCat

Description

YipCat package can realize some of the more commonly used analysis requirements of transcription, such as trajectory analysis,heatmap, cell interaction,imputeweight,cellphonedb,agescore etc , you can view the function description inside the package. Mainly the implementation style of the diagram is more advanced to look at, in particular, the mass spectrometry streaming data is also integrated and can achieve the interaction of cellranger cloupe files, very convenient.

Installtation

you can install this package via command:

install.packages("yipCat_1.0.3.tar.gz")
# or 
devtools::install_github("RyanYip-Kat/yipCat")

requirement

require(Seurat)
require(dplyr)
require(ggplot2)
require(S4Vectors)
require(nabor)
...

trajectory

The implementation of spline trajectory, the specific method description mainly draws on part of the ArchR method, the diagram is as follows：

you can caculate trajecory by follwing commands,for example

seurat<-addSeuratTrajectory(object=seurat,trajectory=c("memory B","Naive B","Plasma"),groupBy="label_fine",embedding="pca") # caculate trajecory
se<-getSeuratTrajectory(seurat)  # get trajectory object
ArchR::plotTrajectoryHeatmap(se)  # plot trajectory heatmap

cacaulte ImputeWeight

computes imputations weights that describe each cell as a linear combination of many cells based on a MAGIC diffusion matrix.

for example

seurat<-ImputeWeights(seurat,reducedDims="pca",nRep=2,sampleCells=5000)  # caculate impute weight
weight<-getImputeWeight(seurat)  # return impute weight
EmbPlot(seurat,colorBy="matrix",features="S100A9",embedding="umap",imputeWeights=NULL)  # first plot
EmbPlot(seurat,colorBy="matrix",features="S100A9",embedding="umap",imputeWeights=weight)  # second plot

first plot second plot

cell-cell interaction : cellphonedb

this package also wrap cellphonedb ,you can choose relative functions to realize what you want for example

exportCellPhoneDB(seurat,cells=cells,features=features,selectCol="label_fine",runCPDB=TRUE)  # selectCol : which column to caculate cell-cell interaction,runCPDB=TRUE,run cellphonedb backgroup
CPDBDotplot  #  cellphonedb result dotplot
CPDBHeatmaps  # cellphonedb result heatmap

cellphonedb plot

calculate DoubletScores

For each sample in the SeuratObject provided, this function will independently assign inferred doublet information to each cell. This allows for removing strong heterotypic doublet-based clusters downstream. A doublet results from a droplet that contained two cells, causing the ATAC-seq or scRNA data to be a mixture of the signal from each cell.

seurat<-calDoubletScores(seurat,sampleCol="Sample",threads=8) #  caculate doublet score
scoreDF<-filterDoublets(seurat)  # get doublet score data frame

Cytof data

This package can realize the processing of cytof data, the cytof data is sampled by sample, the default is 20,000 samples, and then processed into Seurat Object, can achieve umap, tsne, pca and other de-dimensional, cytof visualization, and can be used with the impute weight method;In addition, cytof data can be interacted with cellranger to generate close files for easy cell classification by researchers

library(DropletUtils)
sample_csv<-system.file("extdata", "cytofSample.csv", package = "yipCat")
config_csv<-system.file("extdata", "cytofConfig.csv", package = "yipCat")
seurat<-Cytof2Seurat(sample_csv=sample_csv,config_csv=config_csv,N=20000,path2barcode10X="3M-february-2018.txt")

after run bellow command,will get data matrix in 10X format(eg,filtered_feature_bc_matrix) then run shell script to convert matrix into relative gzip 10X matrix

bash inst/extdata/cytofTo10x.sh filtered_feature_bc_matrix

and,can run cellrnager reanalysis via

generateCloupe(...)

ROGUE (refer to https://www.jianshu.com/p/1ddf52885833)

example:

wrapperROGUE(object=seurat,auto=TRUE,labelCol="label_fine",sampleCol="sample_id")

Volcano plot for difference analysis markers

the markers dataframe support seurat and scanpy

data("deDF")
seurat_df<-deDF[["seurat"]]
p<-DAPlot(seurat_df)

For additional usage, check out the package's function description

Issues using yipCat?

If this does not fix your problem, please report an issue on Github with the Bug Report form.

RyanYip-Kat/yipCat documentation built on Dec. 18, 2021, 11:55 a.m.