SlideCNA

Introduction

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.

Installation

New Conda Environment

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/inst/SlideCNA_env.yml"

Install SlideCNA through R from Github:

library(devtools)
devtools::install_github("dkzhang777/SlideCNA@main", force=TRUE)
library(SlideCNA)

Preparation

Preparation of Slide-seq data raw counts matrix and meta data with cell type annotations. Metadata should contain the following columns in the provided format:

bc (chr): bead labels
cluster_type (chr): annotation of the bead as 'Normal' (Non-malignant) or 'Malignant'

and, if using spatially-aware binning:

pos_x (dbl): x-coordinate bead position
pos_y (dbl): y-coordinate bead position

Running SlideCNA

run_slide_cna(counts, 
              beads_df, 
              gene_pos, 
              output_directory, 
              plot_directory,
              spatial=TRUE,
              roll_mean_window=101,
              avg_bead_per_bin=12,
              pos=TRUE, 
              pos_k=55, 
              ex_k=1)

Parameter Descriptions

counts (data.frame): raw counts (genes x beads)
beads_df (data.frame): annotations of each bead (beads x annotations); contains columns 'bc' for bead names, 'cluster_type' for annotations of 'Normal' or 'Malignant', 'pos_x' for x-coordinate bead positions, and 'pos_y' for y-coordinate bead positions
gene_pos (data.frame): table with columns for GENE, chr, start, end, rel_gene_pos (1 : # of genes on chromosome)
output_directory (char): output directory path
plot_directory (char): output plot 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

Results

Results will appear in output_directory and plot_directory. Key output files are described below:

so.rds Seurat object of Slide-seq data
md.txt metadata of Slide-seq data with Seurat annotations
md_bin.txt metadata of binned Slide-seq data
dat_bin_scaled.txt CNA scores of binned Slide-seq data after applying pyramidal weighting scheme to expression values and normalizing for UMI per bin used for CNA score heat maps and CNA-based clustering
best_k_malig.rds value of optimal number of malignant clusters
cluster_labels_all.txt cluster assignments when performing cluster designation on all binned beads
cluster_labels_malig.txt cluster assignments when performing cluster determination on only malignant binned beads
cluster_markers_all.txt DEGs per cluster when performing cluster designation on all binned beads
cluster_markers_malig.txt DEGs per cluster when performing cluster determination on only malignant binned beads
go_terms_all.txt GO terms per cluster when performing cluster designation on all binned beads
go_terms_malig.txt GO terms per cluster when performing cluster determination on only malignant binned beads