017‐ Spatial Transcriptomics (ST) analysis - rezakj/iCellR GitHub Wiki

Spatial Transcriptomics (ST) analysis example

In this example, we have downloaded 2 samples from 10X genomics website. You can get the data from these links: Anterior and Posterior. To make it easier you can also use the commands below to download from our server.

# download sample data 
url = "https://genome.med.nyu.edu/results/external/iCellR/example7_Spatial_Transcriptomic/V1_Mouse_Brain_Sagittal_Anterior_Section_2_filtered_feature_bc_matrix.tar.gz"

# download the file
download.file(url = url,
    destfile = "V1_Mouse_Brain_Sagittal_Anterior_Section_2_filtered_feature_bc_matrix.tar.gz",
    method = "auto")


url ="https://genome.med.nyu.edu/results/external/iCellR/example7_Spatial_Transcriptomic/V1_Mouse_Brain_Sagittal_Anterior_Section_2_spatial.tar.gz"

# download the file
download.file(url = url,
    destfile = "V1_Mouse_Brain_Sagittal_Anterior_Section_2_spatial.tar.gz",
    method = "auto")

url ="https://genome.med.nyu.edu/results/external/iCellR/example7_Spatial_Transcriptomic/V1_Mouse_Brain_Sagittal_Posterior_Section_2_filtered_feature_bc_matrix.tar.gz"

# download the file
download.file(url = url,
    destfile = "V1_Mouse_Brain_Sagittal_Posterior_Section_2_filtered_feature_bc_matrix.tar.gz",
    method = "auto")

url ="https://genome.med.nyu.edu/results/external/iCellR/example7_Spatial_Transcriptomic/V1_Mouse_Brain_Sagittal_Posterior_Section_2_spatial.tar.gz"

# download the file
download.file(url = url,
    destfile = "V1_Mouse_Brain_Sagittal_Posterior_Section_2_spatial.tar.gz",
    method = "auto") 
#########################
##### untar
untar("V1_Mouse_Brain_Sagittal_Anterior_Section_2_filtered_feature_bc_matrix.tar.gz")
untar("V1_Mouse_Brain_Sagittal_Anterior_Section_2_spatial.tar.gz")

file.rename("spatial","spatial_Anterior2")
file.rename("filtered_feature_bc_matrix","filtered_feature_bc_matrix_Anterior2")

untar("V1_Mouse_Brain_Sagittal_Posterior_Section_2_filtered_feature_bc_matrix.tar.gz")
untar("V1_Mouse_Brain_Sagittal_Posterior_Section_2_spatial.tar.gz")

file.rename("spatial","spatial_Posterior2")
file.rename("filtered_feature_bc_matrix","filtered_feature_bc_matrix_Posterior2")

• Load the data

library(iCellR)

Anterior2 <- load10x("filtered_feature_bc_matrix_Anterior2",gene.name = 2)
Posterior2 <- load10x("filtered_feature_bc_matrix_Posterior2",gene.name = 2)

# if you want to analyze both samples
Samples <- c("Anterior2","Posterior2")
my.data <- data.aggregation(samples = Samples, condition.names = Samples)

# if you want to analyze 1 sample
# my.data <- load10x("filtered_feature_bc_matrix_Posterior2",gene.name = 2)

my.obj <- make.obj(my.data)


Anterior2 <- capture.image.10x("spatial_Anterior2")
Posterior2 <- capture.image.10x("spatial_Posterior2")

# if you want to analyze both samples
Samples <- c("Anterior2","Posterior2")
my.obj <- add.10x.image(my.obj,
         image.data.list = Samples, condition.names = Samples)

# if one sample
# My.image <- image.capture.10x("Post2_spatial")
# my.obj <- add.10x.image(my.obj, image.data.list = "My.image")

my.obj
###################################
,--. ,-----.       ,--.,--.,------.
`--''  .--./ ,---. |  ||  ||  .--. '
,--.|  |    | .-. :|  ||  ||  '--'.'
|  |'  '--'\   --. |  ||  ||  |
`--' `-----' `----'`--'`--'`--' '--'
###################################
An object of class iCellR version: 1.6.0
Raw/original data dimentions (rows,columns): 31053,6118
Data conditions in raw data: Anterior2,Posterior2 (2825,3293)
Row names: A030001D20Rik,A030003K21Rik,A030005K14Rik ...
Columns names: Anterior2_AAACAAGTATCTCCCA.1,Anterior2_AAACACCAATAACTGC.1,Anterior2_AAACAGAGCGACTCCT.1 ...
###################################
  QC stats performed:FALSE, PCA performed:FALSE
  Clustering performed:FALSE, Number of clusters:0
  tSNE performed:FALSE, UMAP performed:FALSE, DiffMap performed:FALSE
  Main data dimensions (rows,columns): 0,0
  Normalization factors:,...
  Imputed data dimensions (rows,columns):0,0
############## scVDJ-seq ###########
VDJ data dimentions (rows,columns):0,0
############## CITE-seq ############
  ADT raw data  dimensions (rows,columns):0,0
  ADT main data  dimensions (rows,columns):0,0
  ADT columns names:...
  ADT row names:...
############## scATAC-seq ############
  ATAC raw data  dimensions (rows,columns):0,0
  ATAC main data  dimensions (rows,columns):0,0
  ATAC columns names:...
  ATAC row names:...
############## Spatial ###########
Spatial data dimentions (rows,columns):9984,5
########### iCellR object ##########

The rest of the analysis is just like regular scRNA-Seq. Filter, normalize, run PCA, tSNE, UMAP, KNetL map and cluster. Then you can start ploting as below:

A=spatial.plot(my.obj,col.by = "clusters",conds.to.plot = "Anterior2",interactive= F)
B=spatial.plot(my.obj,col.by = "clusters",conds.to.plot = "Posterior2",interactive= F)
C= cluster.plot(my.obj,plot.type = "tsne",interactive = F,cell.size = 0.5,cell.transparency = 1, anno.clust=T)
D= cluster.plot(my.obj,plot.type = "tsne",col.by = "conditions",interactive = F,cell.size = 0.5,cell.transparency = 1, anno.clust=T)
E=spatial.plot(my.obj,col.by = "gene", gene = c("Cd4"), conds.to.plot = "Anterior2",interactive= F, scaleValue = TRUE)
F=spatial.plot(my.obj,col.by = "gene", gene = c("Cd4"), conds.to.plot = "Posterior2",interactive= F, scaleValue = TRUE)

library(gridExtra)
png('AllClusts.png', width = 8, height = 8, units = 'in', res = 300)
grid.arrange(A,B,C,D,E,F)
dev.off()