README.md
In gongx030/tcm: TCM visualizes trajectories and cell populations from single cell data
tcm
TCM is an R package for visualizing temporal single cell RNA-seq data.
The paper can be found https://www.nature.com/articles/s41467-018-05112-9.
```TCM visualizes trajectories and cell populations from single cell data
Wuming Gong, Il-Youp Kwak, Naoko Koyano-Nakagawa, Wei Pan & Daniel J. Garry
Nature Communications volume 9, Article number: 2749 (2018)
## 1. Installation
The recommended installation method for `tcm` is using `install_github` command from `devtools` library. You will first have to have [devtools](https://github.com/hadley/devtools) package installed.
```r
library(devtools)
install_github('gongx030/tcm')
A number of needed packages are installed in this process.
2. Quick Start
We first load the tcm and related packages:
library(tcm)
library(SummarizedExperiment)
2.1 Visualizing a simulated temporal scRNA-seq dataset with 2,000 genes, 500 cells and five lineages.
Let us first simulate a simple temporal scRNA-seq data with 2,000 genes, 500 cells and five different lineages. The single cell data are sampled across five time points following a sequentail differentiation model. The dropout noise was added using an exponential decay model.
set.seed(144)
sim <- sim.rnaseq.ts(N = 2000, M = 500, n.lineage = 5, n.time.points = 5)
X <- assays(sim)$count
time.table <- colData(sim)$time.table
Note that X is the simulated gene by cell read count matrix (2,000 by 500), and time.table is a cell by time point binary matrix (500 by 5), indicating the timestamp of each cell.
We run TCM on the simulated temporal scRNA-seq.
mf <- tcm(X, time.table = time.table)
We then plot the dimension reduction results from TCM:
col.lineage <- rainbow(5) # color of cells from each lineage
bg.cell <- col.lineage[colData(sim)$lineage] # color of each cell
plot(mf, pch = 21, bg = bg.cell, cex = 2.25)
legend(par('usr')[2], par('usr')[4], 1:5, bty = 'n', xpd = NA, pt.bg = col.lineage, pch = 21, col = 'black', cex = 1.75)
Let us the performance of t-SNE and diffusion map on the same simulated dataset:
We first scale and log-transform the raw read counts data.
X.log <- scale(log(X + 1))
We visualize the simulated temporal scRNA-seq data by t-SNE:
library(Rtsne)
y <- Rtsne(t(X.log))$Y
plot(y[, 1], y[, 2], pch = 21, cex = 1.5, bg = bg.cell, col = 'black', xlab = '', ylab = '', xaxt = 'n', yaxt = 'n', main = 'tsne')
Similarly, we visualize the dimension reduction results from diffusion map:
library(diffusionMap)
y <- diffuse(dist(t(X.log)))$X[, c(1, 2)]
plot(y[, 1], y[, 2], pch = 21, cex = 1.5, bg = bg.cell, col = 'black', xlab = '', ylab = '', xaxt = 'n', yaxt = 'n', main = 'diffusion map')
We find that for a relatively easy dataset, both t-SNE and diffusion map are able to separate three lineages. However, the visualization produced by TCM show much improved lineage trajectories.
2.2 Visualizing a large-scale simulated temporal scRNA-seq dataset with 2,000 genes, 10,000 cells and five lineages.
set.seed(2)
sim <- sim.rnaseq.ts(N = 2000, M = 10000, n.lineage = 5, n.time.points = 3)
X <- assays(sim)$count
time.table <- colData(sim)$time.table
We will use stochastic variational inference for optimizaing TCM, with batch size of 2,000 cells and using four CPU cores. It will take ~15 mins on an Intel Xeon 2.6GHz computer.
mf <- tcm(X, time.table = time.table, control = list(optimization.method = 'stochastic', batch.size = 2000, mc.cores = 4))
Visualization of the dimension reduction results:
col.lineage <- rainbow(5)
bg.cell <- col.lineage[colData(sim)$lineage]
plot(mf, pch = 21, bg = bg.cell, cex = 0.5)
legend(par('usr')[2], par('usr')[4], 1:5, bty = 'n', xpd = NA, pt.bg = col.lineage, pch = 21, col = 'black', cex = 1.75)
3. Case studies
3.1 Single cell PCR of early mouse embryonic development (Guo et al.)
library(scDatasets)
data(guo)
X <- assays(guo)$count
time.table <- colData(guo)$time.table
X <- preprocess(X)
set.seed(1)
mf <- tcm(X, time.table = time.table)
cg <- factor(colnames(time.table)[max.col(time.table)], colnames(time.table))
col.cg <- rainbow(nlevels(cg))
names(col.cg) <- levels(cg)
bg.cell <- col.cg[as.numeric(cg)]
plot(mf, pch = 21, bg = bg.cell, cex = 2.25)
3.2 Single cell RNA-seq of the development of human primordial germ cells (PGC) and neighboring somatic cells from weeks 4 to 19 post-gestation (Guo et al.)
library(scDatasets)
data(guo2)
X <- assays(guo2)$count
X <- preprocess(X)
time.table <- colData(guo2)$time.table
cg <- factor(colData(guo2)[['source.name']])
col.cg <- rainbow(nlevels(cg))
names(col.cg) <- levels(cg)
bg.cell <- col.cg[as.numeric(cg)]
bg.cell[colData(guo2)[['developmental.stage']] == '17 week gestation' & colData(guo2)[['gender']] == 'female' & colData(guo2)[['source.name']] == 'Primordial Germ Cells'] <- 'gold'
bg.cell[colData(guo2)[['developmental.stage']] == '19 week gestation' & colData(guo2)[['gender']] == 'male' & colData(guo2)[['source.name']] == 'Primordial Germ Cells'] <- 'purple'
set.seed(1)
mf <- tcm(X, time.table = time.table)
plot(mf, pch = 21, bg = bg.cell, cex = 2.25)
3.3 Single cell RNA-seq of hESC derived mesodermal lineages (Loh et al.)
library(scDatasets)
data(loh)
X <- assays(loh)$count
time.table <- colData(loh)$time.table
X <- preprocess(X)
# assign cell color
cn <- colData(loh)[['sample_alias']]
lab2name <- c(
'H7hESC' = 'hESC(D0)',
'H7_derived_APS' = 'Anterior PS(D1)',
'H7_derived_DLL1pPXM' = 'Paraxial Mesoderm(D2)',
'H7_dreived_D2.25_Smtmrs' = 'Somitomeres(D2.25)',
'H7_derived_ESMT' = 'Early Somite(D3)',
'H7_derived_MPS' = 'Mid PS(D1)',
'H7_derived_D2LtM' = 'Lateral Mesoderm(D2)',
'H7_derived_D3GARPpCrdcM' = 'Cardiac Mesoderm(D3-D4)'
)
cg <- factor(cn, names(lab2name), lab2name)
col.cg <- rainbow(nlevels(cg))
names(col.cg) <- levels(cg)
bg.cell <- col.cg[as.numeric(cg)]
mf <- tcm(X, time.table = time.table)
plot(mf, pch = 21, bg = bg.cell, cex = 2.25)
3.4 Single cell RNA-seq of human myoblast differentiation (Trapnell et al.)
library(scDatasets)
data(trapnell)
X <- assays(trapnell)$count
time.table <- colData(trapnell)$time.table
X <- preprocess(X, min.expressed.gene = 0)
cg <- colData(trapnell)[['time']]
col.cg <- rainbow(nlevels(cg))
names(col.cg) <- levels(cg)
bg.cell <- col.cg[as.numeric(cg)]
set.seed(1)
ls <- landscape(type = 'temporal.convolving', time.points = ncol(time.table), K = 15, n.prototype = 15, n.circle = 10, n.prev = 2)
mf <- tcm(X, time.table = time.table, ls = ls)
plot(mf, pch = 21, bg = bg.cell, cex = 2.25)
Visualizaion of expression levels of some key genes:
par(mfrow = c(2, 2))
par(mar = c(2, 2, 2, 2))
plot(mf, pch = 21, bg = num2color(log(X['ENSG00000118194', ] + 1)), cex = 1.25, main = 'TNNT2')
plot(mf, pch = 21, bg = num2color(log(X['ENSG00000134853', ] + 1)), cex = 1.25, main = 'PDGFRA')
plot(mf, pch = 21, bg = num2color(log(X['ENSG00000125414', ] + 1)), cex = 1.25, main = 'MYH2')
plot(mf, pch = 21, bg = num2color(log(X['ENSG00000115461', ] + 1)), cex = 1.25, main = 'IGFBP5')
3.5 Single cell RNA-seq of human iPSC-CM differentiations
library(scDatasets)
data(iPSC)
X <- assays(iPSC)$count
time.table <- colData(iPSC)$time.table
X <- preprocess(X, min.expressed.gene = 0)
cg <- colData(iPSC)[['time']]
col.cg <- rainbow(nlevels(cg))
names(col.cg) <- levels(cg)
bg.cell <- col.cg[as.numeric(cg)]
set.seed(1)
ls <- landscape(type = 'temporal.convolving', time.points = ncol(time.table), K = 15, n.prototype = 15, n.circle = 10, n.prev = 2)
mf <- tcm(X, time.table = time.table, ls = ls, init = list(method = 'backward', update.beta = TRUE))
plot(mf, pch = 21, bg = bg.cell, cex = 2.25)
legend(par('usr')[2], par('usr')[4], colnames(time.table), bty = 'n', xpd = NA, pt.bg = col.cg, pch = 21, col = 'black', cex = 1.75)
Visualizaion of expression levels of some key genes:
par(mfrow = c(2, 3))
par(mar = c(2, 2, 2, 2))
plot(mf, pch = 21, bg = num2color(log(X['POU5F1', ] + 1)), cex = 1.75, main = 'POU5F1')
plot(mf, pch = 21, bg = num2color(log(X['ISL1', ] + 1)), cex = 1.75, main = 'ISL1')
plot(mf, pch = 21, bg = num2color(log(X['MYL2', ] + 1)), cex = 1.75, main = 'MYL2')
plot(mf, pch = 21, bg = num2color(log(X['CDH11', ] + 1)), cex = 1.75, main = 'CDH11')
plot(mf, pch = 21, bg = num2color(log(X['NR2F2', ] + 1)), cex = 1.75, main = 'NR2F2')
plot(mf, pch = 21, bg = num2color(log(X['NPPA', ] + 1)), cex = 1.75, main = 'NPPA')
3.6 Single-cell RNA-Seq of human preimplantation embryo development at E3, E4, E5, E6 and E7
library(scDatasets)
data(petropoulos)
X <- assays(petropoulos)$count
time.table <- colData(petropoulos)$time.table
X <- preprocess(X, min.expressed.gene = 0)
cg <- colData(petropoulos)[['time']]
col.cg <- rainbow(nlevels(cg))
names(col.cg) <- levels(cg)
bg.cell <- col.cg[as.numeric(cg)]
set.seed(1)
mf <- tcm(X, time.table = time.table)
plot(mf, pch = 21, bg = bg.cell, cex = 2.25)
legend(par('usr')[2], par('usr')[4], colnames(time.table), bty = 'n', xpd = NA, pt.bg = col.cg, pch = 21, col = 'black', cex = 1.75)
Visualizaion of expression levels of some key genes:
par(mfrow = c(1, 2), mar = c(2, 2, 2, 2))
plot(mf, pch = 21, bg = num2color(log(X['SOX2', ] + 1)), cex = 0.75, main = 'SOX2')
plot(mf, pch = 21, bg = num2color(log(X['CDX2', ] + 1)), cex = 0.75, main = 'CDX2')
3.7 Single-cell RNA-Seq of mouse embryonic development from zygote to late blastocyst stage
library(scDatasets)
data(deng)
X <- assays(deng)$count
time.table <- colData(deng)$time.table
X <- preprocess(X, min.expressed.gene = 0)
cg <- factor(colnames(time.table)[max.col(time.table)], colnames(time.table))
col.cg <- rainbow(nlevels(cg))
names(col.cg) <- levels(cg)
bg.cell <- col.cg[as.numeric(cg)]
set.seed(1)
mf <- tcm(X, time.table = time.table, init = list(method = 'backward', update.beta = TRUE))
plot(mf, pch = 21, bg = bg.cell, cex = 2.25)
legend(par('usr')[2], par('usr')[4], colnames(time.table), bty = 'n', xpd = NA, pt.bg = col.cg, pch = 21, col = 'black', cex = 1.75)
Visualizaion of expression levels of some key genes:
par(mfrow = c(1, 2), mar = c(2, 2, 2, 2))
plot(mf, pch = 21, bg = num2color(log(X['Sox2', ] + 1)), cex = 1.25, main = 'Sox2')
plot(mf, pch = 21, bg = num2color(log(X['Cdx2', ] + 1)), cex = 1.25, main = 'Cdx2')
4. Session Information
> sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS release 6.9 (Final)
Matrix products: default
BLAS: /panfs/roc/msisoft/R/3.4.3/lib64/R/lib/libRblas.so
LAPACK: /panfs/roc/msisoft/R/3.4.3/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid parallel stats4 stats graphics grDevices datasets utils methods base
other attached packages:
[1] scDatasets_0.0.3 wordcloud_2.5 RColorBrewer_1.1-2 plotrix_3.7 gplots_3.0.1 igraph_1.2.1 MASS_7.3-47 cluster_2.0.6 FNN_1.1 gtools_3.5.0 fields_9.6 maps_3.2.0
[13] spam_2.1-2 dotCall64_0.9-5.2 irlba_2.3.2 Matrix_1.2-12 SummarizedExperiment_1.8.1 DelayedArray_0.4.1 matrixStats_0.53.1 Biobase_2.38.0 GenomicRanges_1.30.3 GenomeInfoDb_1.14.0 IRanges_2.12.0 S4Vectors_0.16.0
[25] BiocGenerics_0.24.0 BiocInstaller_1.28.0
loaded via a namespace (and not attached):
[1] Rcpp_0.12.16 compiler_3.4.3 XVector_0.18.0 bitops_1.0-6 tools_3.4.3 zlibbioc_1.24.0 digest_0.6.15 memoise_1.1.0 lattice_0.20-35 pkgconfig_2.0.1 commonmark_1.4 GenomeInfoDbData_1.0.0 withr_2.1.2 stringr_1.3.0
[15] roxygen2_6.0.1 xml2_1.2.0 caTools_1.17.1 devtools_1.13.5 R6_2.2.2 gdata_2.18.0 magrittr_1.5 KernSmooth_2.23-15 stringi_1.1.7 RCurl_1.95-4.10 slam_0.1-42
gongx030/tcm documentation built on June 4, 2019, 7:26 p.m.
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tcm
TCM is an R package for visualizing temporal single cell RNA-seq data.
The paper can be found https://www.nature.com/articles/s41467-018-05112-9.
```TCM visualizes trajectories and cell populations from single cell data Wuming Gong, Il-Youp Kwak, Naoko Koyano-Nakagawa, Wei Pan & Daniel J. Garry Nature Communications volume 9, Article number: 2749 (2018)
## 1. Installation
The recommended installation method for `tcm` is using `install_github` command from `devtools` library. You will first have to have [devtools](https://github.com/hadley/devtools) package installed.
```r
library(devtools)
install_github('gongx030/tcm')
A number of needed packages are installed in this process.
2. Quick Start
We first load the tcm and related packages:
library(tcm)
library(SummarizedExperiment)
2.1 Visualizing a simulated temporal scRNA-seq dataset with 2,000 genes, 500 cells and five lineages.
Let us first simulate a simple temporal scRNA-seq data with 2,000 genes, 500 cells and five different lineages. The single cell data are sampled across five time points following a sequentail differentiation model. The dropout noise was added using an exponential decay model.
set.seed(144)
sim <- sim.rnaseq.ts(N = 2000, M = 500, n.lineage = 5, n.time.points = 5)
X <- assays(sim)$count
time.table <- colData(sim)$time.table
Note that X is the simulated gene by cell read count matrix (2,000 by 500), and time.table is a cell by time point binary matrix (500 by 5), indicating the timestamp of each cell.
We run TCM on the simulated temporal scRNA-seq.
mf <- tcm(X, time.table = time.table)
We then plot the dimension reduction results from TCM:
col.lineage <- rainbow(5) # color of cells from each lineage
bg.cell <- col.lineage[colData(sim)$lineage] # color of each cell
plot(mf, pch = 21, bg = bg.cell, cex = 2.25)
legend(par('usr')[2], par('usr')[4], 1:5, bty = 'n', xpd = NA, pt.bg = col.lineage, pch = 21, col = 'black', cex = 1.75)
Let us the performance of t-SNE and diffusion map on the same simulated dataset:
We first scale and log-transform the raw read counts data.
X.log <- scale(log(X + 1))
We visualize the simulated temporal scRNA-seq data by t-SNE:
library(Rtsne)
y <- Rtsne(t(X.log))$Y
plot(y[, 1], y[, 2], pch = 21, cex = 1.5, bg = bg.cell, col = 'black', xlab = '', ylab = '', xaxt = 'n', yaxt = 'n', main = 'tsne')
Similarly, we visualize the dimension reduction results from diffusion map:
library(diffusionMap)
y <- diffuse(dist(t(X.log)))$X[, c(1, 2)]
plot(y[, 1], y[, 2], pch = 21, cex = 1.5, bg = bg.cell, col = 'black', xlab = '', ylab = '', xaxt = 'n', yaxt = 'n', main = 'diffusion map')
We find that for a relatively easy dataset, both t-SNE and diffusion map are able to separate three lineages. However, the visualization produced by TCM show much improved lineage trajectories.
2.2 Visualizing a large-scale simulated temporal scRNA-seq dataset with 2,000 genes, 10,000 cells and five lineages.
set.seed(2)
sim <- sim.rnaseq.ts(N = 2000, M = 10000, n.lineage = 5, n.time.points = 3)
X <- assays(sim)$count
time.table <- colData(sim)$time.table
We will use stochastic variational inference for optimizaing TCM, with batch size of 2,000 cells and using four CPU cores. It will take ~15 mins on an Intel Xeon 2.6GHz computer.
mf <- tcm(X, time.table = time.table, control = list(optimization.method = 'stochastic', batch.size = 2000, mc.cores = 4))
Visualization of the dimension reduction results:
col.lineage <- rainbow(5)
bg.cell <- col.lineage[colData(sim)$lineage]
plot(mf, pch = 21, bg = bg.cell, cex = 0.5)
legend(par('usr')[2], par('usr')[4], 1:5, bty = 'n', xpd = NA, pt.bg = col.lineage, pch = 21, col = 'black', cex = 1.75)
3. Case studies
3.1 Single cell PCR of early mouse embryonic development (Guo et al.)
library(scDatasets)
data(guo)
X <- assays(guo)$count
time.table <- colData(guo)$time.table
X <- preprocess(X)
set.seed(1)
mf <- tcm(X, time.table = time.table)
cg <- factor(colnames(time.table)[max.col(time.table)], colnames(time.table))
col.cg <- rainbow(nlevels(cg))
names(col.cg) <- levels(cg)
bg.cell <- col.cg[as.numeric(cg)]
plot(mf, pch = 21, bg = bg.cell, cex = 2.25)
3.2 Single cell RNA-seq of the development of human primordial germ cells (PGC) and neighboring somatic cells from weeks 4 to 19 post-gestation (Guo et al.)
library(scDatasets)
data(guo2)
X <- assays(guo2)$count
X <- preprocess(X)
time.table <- colData(guo2)$time.table
cg <- factor(colData(guo2)[['source.name']])
col.cg <- rainbow(nlevels(cg))
names(col.cg) <- levels(cg)
bg.cell <- col.cg[as.numeric(cg)]
bg.cell[colData(guo2)[['developmental.stage']] == '17 week gestation' & colData(guo2)[['gender']] == 'female' & colData(guo2)[['source.name']] == 'Primordial Germ Cells'] <- 'gold'
bg.cell[colData(guo2)[['developmental.stage']] == '19 week gestation' & colData(guo2)[['gender']] == 'male' & colData(guo2)[['source.name']] == 'Primordial Germ Cells'] <- 'purple'
set.seed(1)
mf <- tcm(X, time.table = time.table)
plot(mf, pch = 21, bg = bg.cell, cex = 2.25)
3.3 Single cell RNA-seq of hESC derived mesodermal lineages (Loh et al.)
library(scDatasets)
data(loh)
X <- assays(loh)$count
time.table <- colData(loh)$time.table
X <- preprocess(X)
# assign cell color
cn <- colData(loh)[['sample_alias']]
lab2name <- c(
'H7hESC' = 'hESC(D0)',
'H7_derived_APS' = 'Anterior PS(D1)',
'H7_derived_DLL1pPXM' = 'Paraxial Mesoderm(D2)',
'H7_dreived_D2.25_Smtmrs' = 'Somitomeres(D2.25)',
'H7_derived_ESMT' = 'Early Somite(D3)',
'H7_derived_MPS' = 'Mid PS(D1)',
'H7_derived_D2LtM' = 'Lateral Mesoderm(D2)',
'H7_derived_D3GARPpCrdcM' = 'Cardiac Mesoderm(D3-D4)'
)
cg <- factor(cn, names(lab2name), lab2name)
col.cg <- rainbow(nlevels(cg))
names(col.cg) <- levels(cg)
bg.cell <- col.cg[as.numeric(cg)]
mf <- tcm(X, time.table = time.table)
plot(mf, pch = 21, bg = bg.cell, cex = 2.25)
3.4 Single cell RNA-seq of human myoblast differentiation (Trapnell et al.)
library(scDatasets)
data(trapnell)
X <- assays(trapnell)$count
time.table <- colData(trapnell)$time.table
X <- preprocess(X, min.expressed.gene = 0)
cg <- colData(trapnell)[['time']]
col.cg <- rainbow(nlevels(cg))
names(col.cg) <- levels(cg)
bg.cell <- col.cg[as.numeric(cg)]
set.seed(1)
ls <- landscape(type = 'temporal.convolving', time.points = ncol(time.table), K = 15, n.prototype = 15, n.circle = 10, n.prev = 2)
mf <- tcm(X, time.table = time.table, ls = ls)
plot(mf, pch = 21, bg = bg.cell, cex = 2.25)
Visualizaion of expression levels of some key genes:
par(mfrow = c(2, 2))
par(mar = c(2, 2, 2, 2))
plot(mf, pch = 21, bg = num2color(log(X['ENSG00000118194', ] + 1)), cex = 1.25, main = 'TNNT2')
plot(mf, pch = 21, bg = num2color(log(X['ENSG00000134853', ] + 1)), cex = 1.25, main = 'PDGFRA')
plot(mf, pch = 21, bg = num2color(log(X['ENSG00000125414', ] + 1)), cex = 1.25, main = 'MYH2')
plot(mf, pch = 21, bg = num2color(log(X['ENSG00000115461', ] + 1)), cex = 1.25, main = 'IGFBP5')
3.5 Single cell RNA-seq of human iPSC-CM differentiations
library(scDatasets)
data(iPSC)
X <- assays(iPSC)$count
time.table <- colData(iPSC)$time.table
X <- preprocess(X, min.expressed.gene = 0)
cg <- colData(iPSC)[['time']]
col.cg <- rainbow(nlevels(cg))
names(col.cg) <- levels(cg)
bg.cell <- col.cg[as.numeric(cg)]
set.seed(1)
ls <- landscape(type = 'temporal.convolving', time.points = ncol(time.table), K = 15, n.prototype = 15, n.circle = 10, n.prev = 2)
mf <- tcm(X, time.table = time.table, ls = ls, init = list(method = 'backward', update.beta = TRUE))
plot(mf, pch = 21, bg = bg.cell, cex = 2.25)
legend(par('usr')[2], par('usr')[4], colnames(time.table), bty = 'n', xpd = NA, pt.bg = col.cg, pch = 21, col = 'black', cex = 1.75)
Visualizaion of expression levels of some key genes:
par(mfrow = c(2, 3))
par(mar = c(2, 2, 2, 2))
plot(mf, pch = 21, bg = num2color(log(X['POU5F1', ] + 1)), cex = 1.75, main = 'POU5F1')
plot(mf, pch = 21, bg = num2color(log(X['ISL1', ] + 1)), cex = 1.75, main = 'ISL1')
plot(mf, pch = 21, bg = num2color(log(X['MYL2', ] + 1)), cex = 1.75, main = 'MYL2')
plot(mf, pch = 21, bg = num2color(log(X['CDH11', ] + 1)), cex = 1.75, main = 'CDH11')
plot(mf, pch = 21, bg = num2color(log(X['NR2F2', ] + 1)), cex = 1.75, main = 'NR2F2')
plot(mf, pch = 21, bg = num2color(log(X['NPPA', ] + 1)), cex = 1.75, main = 'NPPA')
3.6 Single-cell RNA-Seq of human preimplantation embryo development at E3, E4, E5, E6 and E7
library(scDatasets)
data(petropoulos)
X <- assays(petropoulos)$count
time.table <- colData(petropoulos)$time.table
X <- preprocess(X, min.expressed.gene = 0)
cg <- colData(petropoulos)[['time']]
col.cg <- rainbow(nlevels(cg))
names(col.cg) <- levels(cg)
bg.cell <- col.cg[as.numeric(cg)]
set.seed(1)
mf <- tcm(X, time.table = time.table)
plot(mf, pch = 21, bg = bg.cell, cex = 2.25)
legend(par('usr')[2], par('usr')[4], colnames(time.table), bty = 'n', xpd = NA, pt.bg = col.cg, pch = 21, col = 'black', cex = 1.75)
Visualizaion of expression levels of some key genes:
par(mfrow = c(1, 2), mar = c(2, 2, 2, 2))
plot(mf, pch = 21, bg = num2color(log(X['SOX2', ] + 1)), cex = 0.75, main = 'SOX2')
plot(mf, pch = 21, bg = num2color(log(X['CDX2', ] + 1)), cex = 0.75, main = 'CDX2')
3.7 Single-cell RNA-Seq of mouse embryonic development from zygote to late blastocyst stage
library(scDatasets)
data(deng)
X <- assays(deng)$count
time.table <- colData(deng)$time.table
X <- preprocess(X, min.expressed.gene = 0)
cg <- factor(colnames(time.table)[max.col(time.table)], colnames(time.table))
col.cg <- rainbow(nlevels(cg))
names(col.cg) <- levels(cg)
bg.cell <- col.cg[as.numeric(cg)]
set.seed(1)
mf <- tcm(X, time.table = time.table, init = list(method = 'backward', update.beta = TRUE))
plot(mf, pch = 21, bg = bg.cell, cex = 2.25)
legend(par('usr')[2], par('usr')[4], colnames(time.table), bty = 'n', xpd = NA, pt.bg = col.cg, pch = 21, col = 'black', cex = 1.75)
Visualizaion of expression levels of some key genes:
par(mfrow = c(1, 2), mar = c(2, 2, 2, 2))
plot(mf, pch = 21, bg = num2color(log(X['Sox2', ] + 1)), cex = 1.25, main = 'Sox2')
plot(mf, pch = 21, bg = num2color(log(X['Cdx2', ] + 1)), cex = 1.25, main = 'Cdx2')
4. Session Information
> sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS release 6.9 (Final)
Matrix products: default
BLAS: /panfs/roc/msisoft/R/3.4.3/lib64/R/lib/libRblas.so
LAPACK: /panfs/roc/msisoft/R/3.4.3/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid parallel stats4 stats graphics grDevices datasets utils methods base
other attached packages:
[1] scDatasets_0.0.3 wordcloud_2.5 RColorBrewer_1.1-2 plotrix_3.7 gplots_3.0.1 igraph_1.2.1 MASS_7.3-47 cluster_2.0.6 FNN_1.1 gtools_3.5.0 fields_9.6 maps_3.2.0
[13] spam_2.1-2 dotCall64_0.9-5.2 irlba_2.3.2 Matrix_1.2-12 SummarizedExperiment_1.8.1 DelayedArray_0.4.1 matrixStats_0.53.1 Biobase_2.38.0 GenomicRanges_1.30.3 GenomeInfoDb_1.14.0 IRanges_2.12.0 S4Vectors_0.16.0
[25] BiocGenerics_0.24.0 BiocInstaller_1.28.0
loaded via a namespace (and not attached):
[1] Rcpp_0.12.16 compiler_3.4.3 XVector_0.18.0 bitops_1.0-6 tools_3.4.3 zlibbioc_1.24.0 digest_0.6.15 memoise_1.1.0 lattice_0.20-35 pkgconfig_2.0.1 commonmark_1.4 GenomeInfoDbData_1.0.0 withr_2.1.2 stringr_1.3.0
[15] roxygen2_6.0.1 xml2_1.2.0 caTools_1.17.1 devtools_1.13.5 R6_2.2.2 gdata_2.18.0 magrittr_1.5 KernSmooth_2.23-15 stringi_1.1.7 RCurl_1.95-4.10 slam_0.1-42
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