README.md
In bioinformatist/CrossSCC: What the Package Does (One Line, Title Case)
CrossSCC
The goal of CrossSCC is to classify Single-Cell data(as
expression matrix of scRNA-seq data) Crossing batch into
Clusters using Gaussian Mixture Model, and to find out the mapping
relationship in clusters.
Installation
You can install the latest version of CrossSCC with:
install.packages("remotes")
remotes::install_github("bioinformatist/CrossSCC")
Besides, you also need a database
org.HsSimple.eg.db
provided by us: First download the latest release
tarball,
then run:
install.packages("./org.HsSimple.eg.db", repos=NULL)
Example dataset
Seven cell types
This dataset is part of
GSE81861
from this paper.
First, the FPKM matrix file of all cells was
downloaded:
aria2c ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE81nnn/GSE81861/suppl/GSE81861_Cell_Line_FPKM.csv.gz
pigz -d GSE81861_Cell_Line_FPKM.csv.gz
Then pre-process dataset in R:
library(data.table)
library(tidyverse)
library(org.Hs.eg.db)
library(usethis)
library(genefilter)
library(Biobase)
cl <- fread('GSE81861_Cell_Line_FPKM.csv')
names(cl)[1] <- 'Gene'
cl[, Ensembl := str_match(Gene, ".+_(.+)\\.\\d+$")[, 2]]
symbols <- mapIds(org.Hs.eg.db, keys = cl$Ensembl, keytype = "ENSEMBL", column="ENTREZID", multiVals = 'first')
cl[, Entrez := symbols[Ensembl]]
cl[, c('Gene', 'Ensembl'):= NULL]
cl <- cbind(cl[, .(Entrez)], cl[, .SD, .SDcols = !(names(cl) %like% "_B2_")])
cl[, var := rowVars(.SD), .SDcols = -c('Entrez')]
cl <- cl[, max.var := max(var), by = 'Entrez'][var != 0 & max.var == var & !is.na(Entrez), ]
cl[, grep("var", colnames(cl)) := NULL]
cl <- as.data.frame(cl) %>% remove_rownames %>% column_to_rownames(var = "Entrez")
cl <- cl[, !(names(cl) %in% 'Entrez')]
cl.7 <- new("ExpressionSet", exprs=as.matrix(cl), annotation = 'org.Hs.eg.db')
# setwd() back to package root directory
use_data(cl.7, compress = 'xz')
Immune Cells
From this research article
coming with
GSE140228:
aria2c ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE140nnn/GSE140228/suppl/GSE140228_read_counts_Smartseq2.csv.gz
pigz -d GSE140228_read_counts_Smartseq2.csv.gz
aria2c ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE140nnn/GSE140228/suppl/GSE140228_cell_info_Smartseq2.tsv.gz
pigz -d GSE140228_cell_info_Smartseq2.tsv.gz
Then pre-process dataset in R:
library(data.table)
library(org.Hs.eg.db)
library(tidyverse)
library(usethis)
rowVars <- function(x, ...) {
rowSums((x - rowMeans(x, ...))^2, ...)/(dim(x)[2] - 1)
}
immu <- fread('GSE140228_read_counts_Smartseq2.csv')
info <- fread('GSE140228_cell_info_Smartseq2.tsv')
setnames(immu, info$Barcode, paste0(info$celltype_sub, '_', info$Barcode))
symbols <- mapIds(org.Hs.eg.db, keys = immu$gene, keytype = "SYMBOL", column="ENTREZID", multiVals = 'first')
immu[, Entrez := symbols[gene]]
immu[, gene := NULL]
immu[, var := rowVars(.SD), .SDcols = -c('Entrez')]
immu <- immu[, max.var := max(var), by = 'Entrez'][var != 0 & max.var == var & !is.na(Entrez), ]
immu[, grep("var", colnames(immu)) := NULL]
immu <- as.data.frame(immu) %>% remove_rownames %>% column_to_rownames(var = "Entrez")
immu <- immu[, !(names(immu) %in% 'Entrez')]
immu <- new("ExpressionSet", exprs=as.matrix(immu), annotation = 'org.Hs.eg.db')
use_data(immu, compress = 'xz')
How to use & performance
With dataset “Seven cell types”
library(CrossSCC)
data("cl.7")
(handsome.zuo <- CrossSCC(cl.7, ncores = 16, mean.posterior.cutoff = 0.2814, ovl.cutoff = 0.1265, mean.posterior.weight = 1.0000, ovl.weight = 0.3586, lambda.cutoff = 0.9277, verbose = FALSE))
library(data.tree)
handsome.zuo$Get('sampleNames', filterFun = isLeaf, simplify = FALSE)
library(stringr)
cl1 <- factor(str_match(colnames(cl.7), '__(.+?)_')[, 2])
levels(cl1) <- seq_len(7)
cl1 <- as.character(cl1)
cl2 <- unname(lapply(rapply(handsome.zuo$Get('sampleNames', filterFun = isLeaf), enquote, how = 'unlist'), eval))
cl2 <- vapply(colnames(cl.7), function(y) which(vapply(cl2, function(x) y %in% x,
logical(1))), 2333, USE.NAMES = FALSE)
library(clues)
adjustedRand(cl1, cl2, 'Rand')
With dataset “Immune Cells”
The parameters have NOT optimized now for this part.
data("immu")
(handsome.zuo <- CrossSCC(immu, ncores = 16, mean.posterior.cutoff = 0.4459, ovl.cutoff = 0.4243, mean.posterior.weight = 0.2988, ovl.weight = 0.3771, lambda.cutoff = 0.6736, verbose = FALSE))
handsome.zuo$Get('sampleNames', filterFun = isLeaf, simplify = FALSE)
cl1 <- sort(factor(str_match(colnames(immu), '(.+?)_.+')[, 2]))
levels(cl1) <- seq_len(40)
cl1 <- as.character(cl1)
cl2 <- unname(lapply(rapply(handsome.zuo$Get('sampleNames', filterFun = isLeaf), enquote, how = 'unlist'), eval))
cl2 <- vapply(colnames(immu), function(y) which(vapply(cl2, function(x) y %in% x,
logical(1))), 2333, USE.NAMES = FALSE)
library(clues)
adjustedRand(cl1, cl2, 'Rand')
Interactive visualization
plot_CrossSCC(handsome.zuo)
Find best parameter combination by Bayesion Optimization
With dataset “Seven cell types”
library(rBayesianOptimization)
library(CrossSCC)
library(data.tree)
library(stringr)
library(clues)
data("cl.7")
test.CrossSCC <- function(mean.posterior.cutoff, ovl.cutoff, mean.posterior.weight, ovl.weight, lambda.cutoff) {
tryCatch(
{handsome.zuo <- CrossSCC(cl.7, ncores = 16, verbose = FALSE,
mean.posterior.cutoff = mean.posterior.cutoff,
ovl.cutoff = ovl.cutoff, mean.posterior.weight = mean.posterior.weight,
ovl.weight = ovl.weight, lambda.cutoff = lambda.cutoff)
cl1 <- factor(str_match(colnames(cl.7), '__(.+?)_')[, 2])
levels(cl1) <- seq_len(7)
cl1 <- as.character(cl1)
cl2 <- unname(lapply(rapply(handsome.zuo$Get('sampleNames', filterFun = isLeaf), enquote, how = 'unlist'), eval))
cl2 <- vapply(colnames(cl.7), function(y) which(vapply(cl2, function(x) y %in% x,
logical(1))), 2333, USE.NAMES = FALSE)
list(Score = adjustedRand(cl1, cl2, 'Rand'), Pred = 0)}, error=function(e) list(Score = 0, Pred = 0)
)
}
opt.res <- BayesianOptimization(test.CrossSCC,
bounds = list(mean.posterior.cutoff = c(0, 0.5), ovl.cutoff = c(0, 0.5),
mean.posterior.weight = c(0, 1), ovl.weight = c(0, 1),
lambda.cutoff = c(0.5, 1)),
init_points = 20, n_iter = 100)
With dataset “Immune Cells”
library(rBayesianOptimization)
library(CrossSCC)
library(data.tree)
library(stringr)
library(clues)
data("immu")
test.CrossSCC <- function(mean.posterior.cutoff, ovl.cutoff, mean.posterior.weight, ovl.weight, lambda.cutoff) {
tryCatch(
# For one type contains only 11 samples, the minimum group size filter should be turned off
{handsome.zuo <- CrossSCC(immu, ncores = 16, verbose = FALSE,
mean.posterior.cutoff = mean.posterior.cutoff,
ovl.cutoff = ovl.cutoff, mean.posterior.weight = mean.posterior.weight,
ovl.weight = ovl.weight, lambda.cutoff = lambda.cutoff, min.group.size = 11)
# Original sample is disordered
cl1 <- sort(factor(str_match(colnames(immu), '(.+?)_.+')[, 2]))
levels(cl1) <- seq_len(40)
cl1 <- as.character(cl1)
# table(cl1)
cl2 <- unname(lapply(rapply(handsome.zuo$Get('sampleNames', filterFun = isLeaf), enquote, how = 'unlist'), eval))
cl2 <- vapply(colnames(immu), function(y) which(vapply(cl2, function(x) y %in% x,
logical(1))), 2333, USE.NAMES = FALSE)
list(Score = adjustedRand(cl1, cl2, 'Rand'), Pred = 0)}, error=function(e) list(Score = 0, Pred = 0)
)
}
opt.res <- BayesianOptimization(test.CrossSCC,
bounds = list(mean.posterior.cutoff = c(0, 0.5), ovl.cutoff = c(0, 0.5),
mean.posterior.weight = c(0, 1), ovl.weight = c(0, 1),
lambda.cutoff = c(0.5, 1)),
init_points = 20, n_iter = 100)
Acknowledgement
- Thanks to ScreenToGif
for producing gif images for this repo.
- Thanks to Dr. Qi Zhao at SYSUCC for
suggestions on tree data structure and user experience.
bioinformatist/CrossSCC documentation built on April 24, 2020, 9:32 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
CrossSCC
The goal of CrossSCC is to classify Single-Cell data(as expression matrix of scRNA-seq data) Crossing batch into Clusters using Gaussian Mixture Model, and to find out the mapping relationship in clusters.
Installation
You can install the latest version of CrossSCC with:
install.packages("remotes")
remotes::install_github("bioinformatist/CrossSCC")
Besides, you also need a database org.HsSimple.eg.db provided by us: First download the latest release tarball, then run:
install.packages("./org.HsSimple.eg.db", repos=NULL)
Example dataset
Seven cell types
This dataset is part of GSE81861 from this paper. First, the FPKM matrix file of all cells was downloaded:
aria2c ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE81nnn/GSE81861/suppl/GSE81861_Cell_Line_FPKM.csv.gz
pigz -d GSE81861_Cell_Line_FPKM.csv.gz
Then pre-process dataset in R:
library(data.table)
library(tidyverse)
library(org.Hs.eg.db)
library(usethis)
library(genefilter)
library(Biobase)
cl <- fread('GSE81861_Cell_Line_FPKM.csv')
names(cl)[1] <- 'Gene'
cl[, Ensembl := str_match(Gene, ".+_(.+)\\.\\d+$")[, 2]]
symbols <- mapIds(org.Hs.eg.db, keys = cl$Ensembl, keytype = "ENSEMBL", column="ENTREZID", multiVals = 'first')
cl[, Entrez := symbols[Ensembl]]
cl[, c('Gene', 'Ensembl'):= NULL]
cl <- cbind(cl[, .(Entrez)], cl[, .SD, .SDcols = !(names(cl) %like% "_B2_")])
cl[, var := rowVars(.SD), .SDcols = -c('Entrez')]
cl <- cl[, max.var := max(var), by = 'Entrez'][var != 0 & max.var == var & !is.na(Entrez), ]
cl[, grep("var", colnames(cl)) := NULL]
cl <- as.data.frame(cl) %>% remove_rownames %>% column_to_rownames(var = "Entrez")
cl <- cl[, !(names(cl) %in% 'Entrez')]
cl.7 <- new("ExpressionSet", exprs=as.matrix(cl), annotation = 'org.Hs.eg.db')
# setwd() back to package root directory
use_data(cl.7, compress = 'xz')
Immune Cells
From this research article coming with GSE140228:
aria2c ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE140nnn/GSE140228/suppl/GSE140228_read_counts_Smartseq2.csv.gz
pigz -d GSE140228_read_counts_Smartseq2.csv.gz
aria2c ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE140nnn/GSE140228/suppl/GSE140228_cell_info_Smartseq2.tsv.gz
pigz -d GSE140228_cell_info_Smartseq2.tsv.gz
Then pre-process dataset in R:
library(data.table)
library(org.Hs.eg.db)
library(tidyverse)
library(usethis)
rowVars <- function(x, ...) {
rowSums((x - rowMeans(x, ...))^2, ...)/(dim(x)[2] - 1)
}
immu <- fread('GSE140228_read_counts_Smartseq2.csv')
info <- fread('GSE140228_cell_info_Smartseq2.tsv')
setnames(immu, info$Barcode, paste0(info$celltype_sub, '_', info$Barcode))
symbols <- mapIds(org.Hs.eg.db, keys = immu$gene, keytype = "SYMBOL", column="ENTREZID", multiVals = 'first')
immu[, Entrez := symbols[gene]]
immu[, gene := NULL]
immu[, var := rowVars(.SD), .SDcols = -c('Entrez')]
immu <- immu[, max.var := max(var), by = 'Entrez'][var != 0 & max.var == var & !is.na(Entrez), ]
immu[, grep("var", colnames(immu)) := NULL]
immu <- as.data.frame(immu) %>% remove_rownames %>% column_to_rownames(var = "Entrez")
immu <- immu[, !(names(immu) %in% 'Entrez')]
immu <- new("ExpressionSet", exprs=as.matrix(immu), annotation = 'org.Hs.eg.db')
use_data(immu, compress = 'xz')
How to use & performance
With dataset “Seven cell types”
library(CrossSCC)
data("cl.7")
(handsome.zuo <- CrossSCC(cl.7, ncores = 16, mean.posterior.cutoff = 0.2814, ovl.cutoff = 0.1265, mean.posterior.weight = 1.0000, ovl.weight = 0.3586, lambda.cutoff = 0.9277, verbose = FALSE))
library(data.tree)
handsome.zuo$Get('sampleNames', filterFun = isLeaf, simplify = FALSE)
library(stringr)
cl1 <- factor(str_match(colnames(cl.7), '__(.+?)_')[, 2])
levels(cl1) <- seq_len(7)
cl1 <- as.character(cl1)
cl2 <- unname(lapply(rapply(handsome.zuo$Get('sampleNames', filterFun = isLeaf), enquote, how = 'unlist'), eval))
cl2 <- vapply(colnames(cl.7), function(y) which(vapply(cl2, function(x) y %in% x,
logical(1))), 2333, USE.NAMES = FALSE)
library(clues)
adjustedRand(cl1, cl2, 'Rand')
With dataset “Immune Cells”
The parameters have NOT optimized now for this part.
data("immu")
(handsome.zuo <- CrossSCC(immu, ncores = 16, mean.posterior.cutoff = 0.4459, ovl.cutoff = 0.4243, mean.posterior.weight = 0.2988, ovl.weight = 0.3771, lambda.cutoff = 0.6736, verbose = FALSE))
handsome.zuo$Get('sampleNames', filterFun = isLeaf, simplify = FALSE)
cl1 <- sort(factor(str_match(colnames(immu), '(.+?)_.+')[, 2]))
levels(cl1) <- seq_len(40)
cl1 <- as.character(cl1)
cl2 <- unname(lapply(rapply(handsome.zuo$Get('sampleNames', filterFun = isLeaf), enquote, how = 'unlist'), eval))
cl2 <- vapply(colnames(immu), function(y) which(vapply(cl2, function(x) y %in% x,
logical(1))), 2333, USE.NAMES = FALSE)
library(clues)
adjustedRand(cl1, cl2, 'Rand')
Interactive visualization
plot_CrossSCC(handsome.zuo)
Find best parameter combination by Bayesion Optimization
With dataset “Seven cell types”
library(rBayesianOptimization)
library(CrossSCC)
library(data.tree)
library(stringr)
library(clues)
data("cl.7")
test.CrossSCC <- function(mean.posterior.cutoff, ovl.cutoff, mean.posterior.weight, ovl.weight, lambda.cutoff) {
tryCatch(
{handsome.zuo <- CrossSCC(cl.7, ncores = 16, verbose = FALSE,
mean.posterior.cutoff = mean.posterior.cutoff,
ovl.cutoff = ovl.cutoff, mean.posterior.weight = mean.posterior.weight,
ovl.weight = ovl.weight, lambda.cutoff = lambda.cutoff)
cl1 <- factor(str_match(colnames(cl.7), '__(.+?)_')[, 2])
levels(cl1) <- seq_len(7)
cl1 <- as.character(cl1)
cl2 <- unname(lapply(rapply(handsome.zuo$Get('sampleNames', filterFun = isLeaf), enquote, how = 'unlist'), eval))
cl2 <- vapply(colnames(cl.7), function(y) which(vapply(cl2, function(x) y %in% x,
logical(1))), 2333, USE.NAMES = FALSE)
list(Score = adjustedRand(cl1, cl2, 'Rand'), Pred = 0)}, error=function(e) list(Score = 0, Pred = 0)
)
}
opt.res <- BayesianOptimization(test.CrossSCC,
bounds = list(mean.posterior.cutoff = c(0, 0.5), ovl.cutoff = c(0, 0.5),
mean.posterior.weight = c(0, 1), ovl.weight = c(0, 1),
lambda.cutoff = c(0.5, 1)),
init_points = 20, n_iter = 100)
With dataset “Immune Cells”
library(rBayesianOptimization)
library(CrossSCC)
library(data.tree)
library(stringr)
library(clues)
data("immu")
test.CrossSCC <- function(mean.posterior.cutoff, ovl.cutoff, mean.posterior.weight, ovl.weight, lambda.cutoff) {
tryCatch(
# For one type contains only 11 samples, the minimum group size filter should be turned off
{handsome.zuo <- CrossSCC(immu, ncores = 16, verbose = FALSE,
mean.posterior.cutoff = mean.posterior.cutoff,
ovl.cutoff = ovl.cutoff, mean.posterior.weight = mean.posterior.weight,
ovl.weight = ovl.weight, lambda.cutoff = lambda.cutoff, min.group.size = 11)
# Original sample is disordered
cl1 <- sort(factor(str_match(colnames(immu), '(.+?)_.+')[, 2]))
levels(cl1) <- seq_len(40)
cl1 <- as.character(cl1)
# table(cl1)
cl2 <- unname(lapply(rapply(handsome.zuo$Get('sampleNames', filterFun = isLeaf), enquote, how = 'unlist'), eval))
cl2 <- vapply(colnames(immu), function(y) which(vapply(cl2, function(x) y %in% x,
logical(1))), 2333, USE.NAMES = FALSE)
list(Score = adjustedRand(cl1, cl2, 'Rand'), Pred = 0)}, error=function(e) list(Score = 0, Pred = 0)
)
}
opt.res <- BayesianOptimization(test.CrossSCC,
bounds = list(mean.posterior.cutoff = c(0, 0.5), ovl.cutoff = c(0, 0.5),
mean.posterior.weight = c(0, 1), ovl.weight = c(0, 1),
lambda.cutoff = c(0.5, 1)),
init_points = 20, n_iter = 100)
Acknowledgement
- Thanks to ScreenToGif for producing gif images for this repo.
- Thanks to Dr. Qi Zhao at SYSUCC for suggestions on tree data structure and user experience.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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