Getting Started
XClone is a software tool designed for analyzing single-cell RNA sequencing data to identify copy number variations within individual cells. CNVs are important in a variety of biological processes, especially in cancer development.
XClone uses probabilistic modelling to estimate the copy number variation status of each gene within each cell in a sample. By analyzing the expression levels of genes and B allele frequency of gene_bins, XClone can determine which cells are likely to have copy number variations and which genes are affected.
In this tutorial, we’ll walk through the steps of using XClone to analyze single-cell RNA sequencing data for CNV analysis. We’ll cover everything from preparing the input data to interpreting the output of the analysis. Whether you’re new to single-cell RNA sequencing or an experienced user, this tutorial will provide a comprehensive introduction to using XClone for CNV analysis.
Public Datasets
TNBC1 data: a triple-negative breast cancer (TNBC) sample that was assayed by droplet-based scRNA-seq (10x Genomics), from Gao et al. (2021).
BCH869 data: a glioma sample BCH869 with histone H3 lysine27-to-methionine mutations (H3K27M-glioma), where 489 malignant cells and 3 non-tumour cells were probed by smart-seq2, from Filbin et al. (2018).
Raw Data matrix (in anndata format) for RDR module and BAF moudle can be downloaded and imported directly (see notebook tutorials). Data download at demo_data.
Examples of XClone and steps for reproducible results are provided in Jupyter Notebook under examples folder.
For start, please refer to tutorials analyzing TNBC1 and BCH869 datasets, or TNBC1_tutorial and BCH869_tutorial records step by step.
XClone on new datasets
import package
import xclone
XClone provides integrated functions (RDR BAF and Combination) for CNV analysis by default whilst specific configurations might need to be adjusted accordingly. For each XClone module, we provide independent Class for configuration. You can specify which module to use by set module = “RDR”, module = “BAF” or module = “Combine” in xclone.XCloneConfig(dataset_name = dataset_name, module = “RDR”). We provide two sets of default base configurations for 10X Genomics scRNA-seq and SMART-seq, default is 10X scRNA-seq. If you want to get default base configurations for SMART-seq dataset, you can set set_smartseq = True in xclone.XCloneConfig().
If you want to change params setting, Please Sub-class and override base configuration file (here lists a few frequently params used), please refer config.py for detailed arguments. After overriding, you can print xconfig.display() to show the updated configurations before you run the module, e.g., RDR_Xdata = xclone.model.run_RDR(RDR_adata, config_file = xconfig).
RDR module
xconfig = xclone.XCloneConfig(dataset_name = dataset_name, module = "RDR", set_smartseq = True)
xconfig.set_figure_params(xclone= True, fontsize = 18)
xconfig.outdir = out_dir
xconfig.cell_anno_key = "cell_type"
xconfig.ref_celltype = "N"
xconfig.top_n_marker = 25
xconfig.marker_group_anno_key = "cell_type"
xconfig.xclone_plot= True
xconfig.plot_cell_anno_key = "Clone_ID"
xconfig.trans_t = 1e-6
xconfig.start_prob = np.array([0.3, 0.4, 0.3])
xconfig.display()
RDR_Xdata = xclone.model.run_RDR(RDR_adata,
config_file = xconfig)
BAF module
xconfig = xclone.XCloneConfig(dataset_name = dataset_name, module = "BAF", set_smartseq = True)
xconfig.set_figure_params(xclone= True, fontsize = 18)
xconfig.outdir = out_dir
xconfig.cell_anno_key = "cell_type"
xconfig.ref_celltype = "N"
xconfig.concentration = 35.5
xconfig.concentration_lower = 20
xconfig.concentration_upper = 100
xconfig.theo_neutral_BAF = 0.5
xconfig.xclone_plot= True
xconfig.plot_cell_anno_key = "Clone_ID"
xconfig.phasing_region_key = "chr"
xconfig.phasing_len = 100
xconfig.WMA_window_size = 6
xconfig.trans_t = 1e-6
xconfig.start_prob = np.array([0.2, 0.15, 0.3, 0.15, 0.2])
t = xconfig.trans_t
xconfig.trans_prob = np.array([[1-4*t, t, t, t,t],[t, 1-4*t, t, t,t],[t, t, 1-4*t, t,t], [t, t, t, 1-4*t, t], [t, t, t, t, 1-4*t]])
xconfig.CNV_N_components = 5
## update
xconfig.BAF_denoise = True
xconfig.display()
BAF_merge_Xdata = xclone.model.run_BAF(BAF_adata,
config_file = xconfig)
Combine module
xconfig = xclone.XCloneConfig(dataset_name = dataset_name, module = "Combine")
xconfig.set_figure_params(xclone= True, fontsize = 18)
xconfig.outdir = out_dir
xconfig.cell_anno_key = "cell_type"
xconfig.ref_celltype = "N"
xconfig.copygain_correct= False
xconfig.xclone_plot= True
xconfig.plot_cell_anno_key = "Clone_ID"
xconfig.merge_loss = False
xconfig.merge_loh = True
xconfig.BAF_denoise = True
xconfig.display()
combine_Xdata = xclone.model.run_combine(RDR_Xdata,
BAF_merge_Xdata,
verbose = True,
run_verbose = True,
config_file = xconfig)