Getting Started

Here, we explain the basics of using CoSpar. CoSpar requires the count matrix not log-transformed. This is specifically assumed in selecting highly variable genes, in computing PCA, and in the HighVar method for initializing the joint optimization using a single clonal time point. CoSpar also assumes that the dataset has more than one time point. However, if you have only a snapshot, you can still manually cluster the cells into more than one time point to use CoSpar.

First, import CoSpar with:

import cospar as cs

For better visualization you can change the matplotlib settings to our defaults with:


If you want to adjust parameters for a particular plot, just pass the parameters into this function.

The workflow of CoSpar is summarized by the following illustration:

Also, below is a summary of the main analyses after we infer the transition map, and its connection with the mathematical formulation in Wang et al. Nat. Biotech. (2022).


Given the gene expression matrix, clonal matrix, and other information, initialize the anndata object using:

adata_orig = cs.pp.initialize_adata_object(adata=None,**params)

The AnnData object adata_orig stores the count matrix (adata_orig.X), gene names (adata_orig.var_names), and temporal annotation of cells (adata_orig.obs['time_info']). Optionally, you can also provide the clonal matrix X_clone, selected PCA matrix X_pca, the embedding matrix X_emb, and the state annotation state_info, which will be stored at adata_orig.obsm['X_clone'], adata_orig.obsm['X_pca'], adata_orig.obsm['X_emb'], and adata_orig.obs['state_info'], respectively.

If an adata object is provided as an input, the initialization function will try to automatically generate the correct data structure, and all annotations associated with the provided adata will remain intact. You can add new annotations to supplement or override existing annotations in the adata object.

If you do not have a dataset yet, you can still play around using one of the built-in datasets, e.g.:

adata_orig = cs.datasets.hematopoiesis_subsampled()

Preprocessing & dimension reduction

Assuming basic quality control (excluding cells with low read count etc.) have been done, we provide basic preprocessing (gene selection and normalization) and dimension reduction related analysis (PCA, UMAP embedding etc.) at cs.pp.*:


The first step get_highly_variable_genes also includes count matrix normalization. The second step, which is optional but recommended, removes cell cycle correlated genes among the selected highly variable genes. In get_X_pca, we apply z-score transformation for each gene expression before computing the PCA. In get_X_emb, we simply use the umap function from scanpy. With get_state_info, we extract state information using leiden clustering implemented in scanpy. In get_X_clone, we faciliate the conversion of the raw clonal data into a cell-by-clone matrix. As mentioned before, this preprocessing assumes that the count matrix is not log-transformed.

Basic clonal analysis

We provide a few plotting functions to help visually exploring the clonal data before any downstream analysis. You can visualize clones on state manifold directly:,**params)

You can generate the barcode heatmap across given clusters to inspect clonal behavior:,**params)

You can quantify the clonal coupling across different fate clusters:,source='X_clone',**params),source='X_clone',**params)

Strong coupling implies the existence of bi-potent or multi-potent cell states at the time of barcoding. You can visualize the fate hierarchy by a simple neighbor-joining method:,source='X_clone',**params),source='X_clone',**params)

Finally, you can infer the fate bias \(-log_{10}(P_{value})\) of each clone towards a designated fate cluster:,**params)

A biased clone towards this cluster has a statistically significant cell fraction within or outside this cluster.

Transition map inference

The core of the software is efficient and robust inference of a transition map by integrating state and clonal information. If the dataset has multiple clonal time points, you can run:


It subsamples the input data at selected time points and computes the transition map, stored at adata.uns['transition_map'] and adata.uns['intraclone_transition_map'], with the latter restricted to intra-clone transitions. Depending on later_time_point, it has two modes of inference:

  1. When later_time_point=None, it infers a transition map between neighboring time points. For example, for clonal_time_points=[‘day1’, ‘day2’, ‘day3’], it computes transitions for pairs (‘day1’, ‘day2’) and (‘day2’, ‘day3’), but not for (‘day1’, ‘day3’).

  2. If later_time_point is specified, it generates a transition map between this time point and each of the earlier time points. In the previous example, if later_time_point=='day3', we infer transitions for pairs (‘day1’, ‘day3’) and (‘day2’, ‘day3’). This applies to the following map inference functions.

If the dataset has only one clonal time point, you can run:

adata=cs.tmap.infer_Tmap_from_one_time_clones(adata_orig,initial_time_points=None, later_time_point=None,initialize_method='OT',**params)

which jointly optimizes the transition map and the initial clonal structure. It requires initializing the transition map using state information alone. We provide two methods for such initialization: 1) OT for using the standard optimal transport approach; 2) HighVar for a customized approach, assuming that cells similar in gene expression across time points share clonal origin. For the OT method, if you wish to utilize the growth rate information as Waddington-OT, you can directly pass the growth rate estimate for each cell to the input AnnaData object at adata_orig.obs["cell_growth_rate"]. Depending on the choice, the initialized map is stored at adata.uns['OT_transition_map'] or adata.uns['HighVar_transition_map']. The final product is stored at adata.uns['transition_map'].

HighVar converts highly variable genes into pseudo multi-time clones and infers a putative map with coherent sparse optimization. We find the HighVar method performs better than the OT method, especially when there are large differentiation effects over the observed time window, or batch effects.

If initial_time_points and later_time_point are not specified, a map with transitions from all time points to the last time point is generated.

If you do not have any clonal information, you can still run:


It is the same as cs.tmap.infer_Tmap_from_one_time_clones except that we assume a pseudo clonal data where each cell at the later time point occupies a unique clone.

We also provide simple methods that infer transition map from clonal information alone:


The result is stored at adata.uns['clonal_transition_map'].

Analysis and visualization

Finally, each of the computed transition maps can be explored on state embedding at the single-cell level using a variety of analysis and plotting functions. There are some common parameters: 1) source, for choosing one of the pre-computed transition maps (or the raw clonal data) for analysis; 2) selected_fates, for visualizing the fate bias towards/against given fate clusters; 3) map_backward, for analyzing forward or backward transitions; 4) method, for different methods in fate probability analysis. See CoSpar basics for more details.

Below, we frame the task in the language of analyzing backward transitions for convenience. To see where a cell came from, run:,**params)

To visualize the fate probability of initial cell states, run:,**params),**params)

To infer the fate bias of initial cell states between two fate clusters, run:,**params),**params)

To infer the dynamic trajectory towards given fate clusters, run:,**params),**params)

or, alternatively if you have data with multiple clonal time points, run:,**params),**params)

The first method ( assumes two input fate clusters and infers each trajectory by thresholding the corresponding fate bias. The second method ( infers the trajectory by iteratively tracing a selected fate cluster all the way back to its putative origin at the initial time point. For both methods, the inferred trajectory for each fate will be saved at adata.obs[f'diff_trajectory_{source}_{fate_name}'], and we can explore the gene expression dynamics along this trajectory using:,**params)

Additionally, the first method ( exports the selected ancestor states selected fate clusters at adata.obs[f'progenitor_{source}_{fate_name}'], which can be used to infer the driver genes for fate bifurcation by running:,**params)

If there are multiple mature fate clusters, you can infer their differentiation coupling from the fate probabilities of initial cells or the raw clonal matrix by:,source='transition_map',**params),source='transition_map',**params)

You can also infer the fate hierarchy from:,source='transition_map',**params),source='transition_map',**params)