This package provides a new algorithm that maps multi-branching cell developmental pathways and aligns individual cells along the continuum of developmental trajectories. Mpath computationally reconstructs cell developmental pathways as a multi-destination journey on a map of connected landmarks wherein individual cells are placed in order along the paths connecting the landmarks. To achieve that, it first identifies clusters of cells and designates landmark clusters each defines a discrete cellular state. Subsequently it identifies and counts cells that are potentially transitioning from one landmark state to the next based on transcriptional similarities. It then uses the cell counts to infer putative transitions between landmark states giving rise to a state transition network. After that, Mpath sorts individual cells according to their various stages during transition to resemble the landmark-to-landmark continuum. Lastly, Mpath detects genes that were differentially expressed along the single-cell trajectories and identifies candidate regulatory markers.
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#### Install and load Mpath package
install.packages("Mpath_1.0.tar.gz",repos = NULL, type="source")
library(Mpath)
#################################################
###### Analysis of mouse DC dataset GSE60783 ####
#################################################
path <- getwd()
setwd(paste(path,"/GSE60783",sep=""))
##### remove low detection rate genes
rpkmFile="TPM_GSE60783_noOutlier.txt";
rpkmQCFile="TPM_GSE60783_noOutlier_geneQC0.05anyGroup.txt";
sampleFile="sample_GSE60783_noOutlier.txt";
QC_gene(rpkmFile=rpkmFile,
rpkmQCFile=rpkmQCFile,
sampleFile=sampleFile,threshold=0.05,method="any")
### Mpath using spleenic CD4 vs CD8 DEGs
rpkmFile = "TPM_GSE60783_noOutlier_geneQC0.05anyGroup_CD4vsCD8DEG.txt";
sampleFile = "sample_GSE60783_noOutlier.txt";
find_optimal_cluster_number(rpkmFile = rpkmFile,
sampleFile = sampleFile,
min_cluster_num = 7, max_cluster_num = 15,
diversity_cut = 0.6, size_cut = 0.05)
### Landmark designation
rpkmFile = "TPM_GSE60783_noOutlier_geneQC0.05anyGroup_CD4vsCD8DEG.txt";
baseName = "TPM_GSE60783_noOutlier_geneQC0.05anyGroup_CD4vsCD8DEG";
sampleFile = "sample_GSE60783_noOutlier.txt";
landmark_cluster <- landmark_designation(rpkmFile = rpkmFile,
baseName = baseName,
sampleFile = sampleFile,
method = "diversity_size",
numcluster = 11, diversity_cut=0.6,
size_cut=0.05)
### Plot hierachical clustering
dataFile = "TPM_GSE60783_noOutlier_geneQC0.05anyGroup_CD4vsCD8DEG.txt";
SC_hc_colorCode(dataFile = dataFile,
cuttree_k = 11,
sampleFile= "sample_GSE60783_noOutlier.txt",
width = 22, height = 10, iflog2 = TRUE,
colorPalette = c("red","green","blue"))
### Construct weighted neighborhood network
exprs = "TPM_GSE60783_noOutlier_geneQC0.05anyGroup_CD4vsCD8DEG.txt";
baseName = "TPM_GSE60783_noOutlier_geneQC0.05anyGroup_CD4vsCD8DEG";
neighbor_network <- build_network(exprs = exprs,
landmark_cluster = landmark_cluster,
baseName = baseName)
### TrimNet: trim edges of lower weights
baseName = "TPM_GSE60783_noOutlier_geneQC0.05anyGroup_CD4vsCD8DEG";
trimmed_net <- trim_net(neighbor_network,textSize=30,
baseName = baseName,
method = "mst")
### plot trimmed net and color-code the nodes by gene expression
rpkmFile="TPM_GSE60783_noOutlier.txt";
lmFile="TPM_GSE60783_noOutlier_geneQC0.05anyGroup_CD4vsCD8DEG_landmark_cluster.txt";
color_code_node_2(networkFile=trimmed_net,
rpkmFile=rpkmFile,
lmFile=lmFile,
geneName=c("Irf8","Id2","Batf3"),
baseName="cDC1_marker",
seed=NULL)
### Re-order the cells on the path connecting landmark
### "CDP_2","CDP_1","PreDC_9","PreDC_3"
exprs = "TPM_GSE60783_noOutlier_geneQC0.05anyGroup_CD4vsCD8DEG.txt";
ccFile = "TPM_GSE60783_noOutlier_geneQC0.05anyGroup_CD4vsCD8DEG_landmark_cluster.txt";
order <- nbor_order(exprs = exprs,
ccFile = ccFile,
lm_order = c("CDP_2","CDP_1","preDC_9","preDC_3"),
if_bb_only=FALSE,
method=1)
### identify genes that changed along the cell re-ordering
deg <- vgam_deg(exprs = "TPM_GSE60783_noOutlier_geneQC0.05anyGroup.txt",
order = order,
lm_order = c("CDP_2","CDP_1","preDC_9","preDC_3"),
min_expr=1,
p_threshold=0.05)
### plot heatmap of genes that changed along the cell re-ordering
heatmap_nbor(exprs = "TPM_GSE60783_noOutlier_geneQC0.05anyGroup.txt",
cell_order = "CDP_2_CDP_1_preDC_9_preDC_3_order.txt",
plot_genes = "CDP_2_CDP_1_preDC_9_preDC_3_vgam_deg0.05.txt",
cell_annotation = "sample_GSE60783_noOutlier.txt",
num_gene_cluster = 6,
hm_height = 15, hm_width = 10,
baseName = "CDP_2_CDP_1_preDC_9_preDC_3_order_vgam_deg0.05")
###################################################
### Analysis of human myoblast dataset GSE52529 ###
###################################################
setwd(paste(path,"/GSE52529",sep=""))
### remove low detection rate genes
QC_gene(rpkmFile = "GSE52529_fpkm_matrix_nooutliers.txt",
rpkmQCFile = "GSE52529_fpkm_matrix_nooutliers_geneQC0.05anyGroup.txt",
sampleFile = "sample_nooutlier.txt",threshold=0.05,method="any")
rpkmFile = "GSE52529_fpkm_matrix_nooutliers_ANOVA_p0.05_DEG.txt";
sampleFile = "sample_nooutlier.txt";
find_optimal_cluster_number(rpkmFile = rpkmFile,
sampleFile = sampleFile,
min_cluster_num = 7, max_cluster_num = 18,
diversity_cut = 0.9, size_cut = 0.05)
rpkmFile = "GSE52529_fpkm_matrix_nooutliers_ANOVA_p0.05_DEG.txt";
baseName = "GSE52529_fpkm_matrix_nooutliers_ANOVA_p0.05_DEG";
landmark_cluster <- landmark_designation(rpkmFile = rpkmFile,
baseName = baseName,
sampleFile = "sample_nooutlier.txt",
method = "diversity_size",
numcluster = 14, diversity_cut=0.9,
size_cut=0.05)
SC_hc_colorCode(dataFile = "GSE52529_fpkm_matrix_nooutliers_ANOVA_p0.05_DEG.txt",
cuttree_k = 14,
sampleFile= "sample_nooutlier.txt",
width = 22, height = 10, iflog2 = TRUE)
### Construct weighted neighborhood network
exprs = "GSE52529_fpkm_matrix_nooutliers_ANOVA_p0.05_DEG.txt";
neighbor_network <- build_network(exprs = exprs,
landmark_cluster = landmark_cluster)
### TrimNet: trim edges of lower weights
trimmed_net <- trim_net(neighbor_network,textSize=30,
baseName = "GSE52529_fpkm_matrix_nooutliers_ANOVA_p0.05_DEG",
method = "mst")
### Color code the landmarks with marker expression
networkFile="GSE52529_fpkm_matrix_nooutliers_ANOVA_p0.05_DEG_state_transition_mst.txt";
lmFile = "GSE52529_fpkm_matrix_nooutliers_ANOVA_p0.05_DEG_landmark_cluster.txt";
rpkmFile = "GSE52529_fpkm_matrix_nooutliers_geneSymbol.txt";
geneName=c("SPHK1","PBX1","XBP1","ZIC1","MZF1","CUX1","ARID5B","POU2F1","CDK1","MYOG");
color_code_node_2(networkFile = networkFile,
rpkmFile = rpkmFile,
lmFile = lmFile,
geneName = geneName,
baseName = "Marker",
seed=3)
### path 1: "T0_2","T0_1","T24_8","T48_10","T72_13"
ccFile = "GSE52529_fpkm_matrix_nooutliers_ANOVA_p0.05_DEG_landmark_cluster.txt";
order <- nbor_order(exprs = "GSE52529_fpkm_matrix_nooutliers_ANOVA_p0.05_DEG.txt",
ccFile = ccFile,
lm_order = c("T0_2","T0_1","T24_8","T48_10","T72_13"),
if_bb_only=TRUE,
method=1)
deg <- vgam_deg(exprs = "GSE52529_fpkm_matrix_nooutliers_geneQC0.05anyGroup.txt",
order = order,
lm_order = c("T0_2","T0_1","T24_8","T48_10","T72_13"),
min_expr=1,
p_threshold=0.05)
heatmap_nbor(exprs = "GSE52529_fpkm_matrix_nooutliers_geneQC0.05anyGroup.txt",
cell_order = order,
plot_genes = row.names(deg),
cell_annotation = "sample_nooutlier.txt",
num_gene_cluster = 6,
hm_height = 15, hm_width = 10,
baseName = "GSE52529_path1_order_vgam_deg0.05")
### path 2: "T0_2","T0_1","T24_7","T48_4","T72_14"
ccFile = "GSE52529_fpkm_matrix_nooutliers_ANOVA_p0.05_DEG_landmark_cluster.txt";
order <- nbor_order(exprs = "GSE52529_fpkm_matrix_nooutliers_ANOVA_p0.05_DEG.txt",
ccFile = ccFile,
lm_order = c("T0_2","T0_1","T24_7","T48_4","T72_14"),
if_bb_only = TRUE,
method=1)
deg <- vgam_deg(exprs = "GSE52529_fpkm_matrix_nooutliers_geneQC0.05anyGroup.txt",
order = order,
lm_order = c("T0_2","T0_1","T24_7","T48_4","T72_14"),
min_expr=1,
p_threshold=0.05)
heatmap_nbor(exprs = "GSE52529_fpkm_matrix_nooutliers_geneQC0.05anyGroup.txt",
cell_order = order,
plot_genes = row.names(deg),
cell_annotation = "sample_nooutlier.txt",
num_gene_cluster = 6,
hm_height = 15, hm_width = 10,
baseName = "GSE52529_path2_order_vgam_deg0.05")
### Generate Figure 5
ccFile = "GSE52529_fpkm_matrix_nooutliers_ANOVA_p0.05_DEG_landmark_cluster.txt";
order1 <- nbor_order(exprs = "GSE52529_fpkm_matrix_nooutliers_ANOVA_p0.05_DEG.txt",
ccFile = ccFile,
lm_order = c("T0_2","T0_1","T24_8","T48_10","T72_13"),
if_bb_only=TRUE,
method=1)
ccFile = "GSE52529_fpkm_matrix_nooutliers_ANOVA_p0.05_DEG_landmark_cluster.txt";
order2 <- nbor_order(exprs = "GSE52529_fpkm_matrix_nooutliers_ANOVA_p0.05_DEG.txt",
ccFile = ccFile,
lm_order = c("T0_2","T0_1","T24_7","T48_4","T72_14"),
if_bb_only=TRUE,
method=1)
deg1 <- read.table("T0_2_T0_1_T24_8_T48_10_T72_13_vgam_deg0.05.txt",sep="\t",header=T)
deg2 <- read.table("T0_2_T0_1_T24_7_T48_4_T72_14_vgam_deg0.05.txt",sep="\t",header=T)
deg <- unique(c(as.character(deg1[,1]),as.character(deg2[,1])))
heatmap_nbor(exprs = "GSE52529_fpkm_matrix_nooutliers_ANOVA_p0.05_DEG.txt",
cell_order = c(order1,order2),
plot_genes = deg,
cell_annotation = "sample_nooutlier.txt",
num_gene_cluster = 7,
hm_height = 15, hm_width = 10,
baseName = "Path12_method1orderedbackbone_progression_heatmap",
n_linechart = list(order1,order2))
## End(Not run)
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