SlideCNA is a method to call copy number alterations (CNA) from spatial transcriptomics data (adapted for Slide-seq data). SlideCNA uses expression smoothing across the genome to extract changes in copy number and implements a spatio-molecular binning process to boost signal and consolidate reads. Based on the CNA profiles, SlideCNA can identify clusters across space.
Example Jupyter notebooks of SlideCNA applied to Slide-seq, snRNA-seq, and Slide-seq with TACCO bead splitting data are available here: https://github.com/dkzhang777/SlideCNA_Analysis.
Create a new conda environment using the SlideCNA_env.yml file from the SlideCNA repository:
conda env create -f "https://github.com/dkzhang777/SlideCNA/blob/main/SlideCNA_env.yml"(For older versions of conda one needs to download the environment.yml and use the local file for installation.)
library(devtools)
devtools::install_github("dkzhang777/SlideCNA@main", force=TRUE)
library(SlideCNA)Prepare a Seurat Data object of the Slide-seq data that contains counts matrix and metadata with cell type annotations. Metadata should contain the following columns in the provided format:
bc (chr): bead labels \ seurat_clusters (fct): Seurat-defined clusters*\ pos_x (dbl): x-coordinate bead position\ pos_y (dbl): y-coordinate bead position\ cluster_type (chr): annotation of the bead as 'Normal' (Non-malignant) or 'Malignant'
* Seurat clusters are included to guide interpretation of cell type annotations. RCTD-defined cell types and malignant/non-malignant status should generally correspond to distinct Seurat clusters.
run_slide_cna(so,
md,
gene_pos,
plotDir,
OUPUT_DIRECTORY,
spatial=TRUE,
roll_mean_window=101,
avg_bead_per_bin=12,
pos=TRUE, pos_k=55,
ex_k=1,
hc_function_bin='ward.D2',
spatial_vars_to_plot=c("seurat_clusters", "bin_all", "N_bin",
"umi_bin", "cluster_type"),
scale_bin_thresh_hard=TRUE,
lower_bound_cnv=0.6,
upper_bound_cnv=1.4,
hc_function_cnv='ward.D2',
hc_function_cnv_heatmap = 'ward.D2',
quantile_plot_cluster_label="seurat_clusters",
hc_function_silhouette='ward.D2',
max_k_silhouette=10,
plot_silhouette=TRUE,
hc_function_plot_clones="ward.D2",
chrom_ord = c('chr1', 'chr2', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7',
'chr8', 'chr9','chr10', 'chr11', 'chr12', 'chr13',
'chr14', 'chr15', 'chr16', 'chr17','chr18', 'chr19',
'chr20', 'chr21', 'chr22', 'chr23', 'chrX', 'chrY', 'chrM'),
# Heat map legend chromosome colors
chrom_colors = c(chr1='#8DD3C7', chr2='#FFFFB3', chr3='#BEBADA',
chr4='#FB8072', chr5='#80B1D3', chr6='#FDB462',
chr7='#B3DE69', chr8='#FCCDE5', chr9='#D9D9D9' ,
chr10='#BC80BD', chr11='#CCEBC5', chr12='#FFED6F',
chr13='#1B9E77', chr14='#D95F02', chr15='#7570B3',
chr16='#E7298A', chr17='#66A61E', chr18='#E6AB02',
chr19='#A6761D', chr20='#666666', chr21='#A6CEE3',
chr22='#1F78B4', chrX='#B2DF8A'),
# Ggplot parameters
text_size = 16,
title_size = 18,
legend_size_pt = 4,
legend_height_bar = 1.5)so (Seurat Object): object that contains a counts matrix and metadata with cell type annotations \
md (data.table): metadata of each bead (beads x annotations); contains columns 'bc' with values of bead names, 'cluster_type' with values of 'Normal' or 'Malignant', 'pos_x' with x-coordinate bead positions, 'pos_y' with y-coordinate bead positions, and 'nCount_RNA' with RNA counts per bead \
gene_pos (data.table): table with columns for GENE, chr, start, end, rel_gene_pos (1 : # of genes on chromosome)\
plotDir (char): output plot directory path\
OUTPUT_DIRECTORY (char): output directory path\
spatial (bool): TRUE if using spatial information FALSE if not\
roll_mean_window (int): integer number of adjacent genes for which to average over in pyramidal weighting scheme\
avg_bead_per_bin (int): integer of average number of beads there should be per bin \
pos (bool): TRUE if doing spatial and expressional binning, FALSE if just expressional binning\
pos_k (numeric): positional weight\
ex_k (numeric): expressional weight\
hc_function_bin (char): hierarchical clustering function for binning. Choose from "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC)\
spatial_vars_to_plot (char): vector of features to plot/columns of metadata\
scale_bin_thresh_hard (bool): TRUE if using strict, absolute thresholds for expression thresholds and FALSE if adjusting depending on the sample\
lower_bound_cnv (numeric): lower threshold for CNV scores\
upper_bound_cnv (numeric): upper threshold for CNV scores \
hc_function_cnv (char): hierarchical clustering function to use for CNV-calling. Choose from "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC)\
hc_function (char): hierarchical clustering function to use for visualzing CNV heat map. This should be consistent with hc_function_cnv. Choose from "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC)\
quantile_plot_cluster_label (char): column name to keep in quantile plot\
hc_function_silhouette (char): hierarchical clustering function to use for the Silhouette method. Choose from "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC)\
max_k_silhouette (int): max number of clusters to evaluate (2 to max_k_silhouette) in Silhouette method\
hc_function_plot_clones (char): hierarchical clustering function to use in plotting clones. Choose from "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC)\
chrom_ord (char): vector of order and names of chromosomes\
chrom_colors (char): vector of which colors each chromosome should be in heat map\
text_size (int): size of text in some ggplots\
title_size (int): size of title in some ggplots\
legend_size_pt (int): size of legend text size in some ggplots\
legend_height_bar (int): height of legend bar in some ggplots
Results will appear in OUTPUT_DIRECTORY and plotDir