celltraj.translate

celltraj.translate.get_predictedFC(state_probs, statesFC)

Predict fold changes based on state probabilities and state-specific fold changes.

Parameters:

  • state_probs (ndarray) – State probability matrix (conditions x states).

  • statesFC (ndarray) – State-specific fold change matrix (states x genes).

Returns:

x_FC_predicted – Predicted fold change matrix (conditions x genes).

Return type:

ndarray

Examples

>>> state_probs = np.random.rand(10, 3)  # Example state probability data
>>> statesFC = np.random.rand(3, 5000)  # Example state-specific fold change data
>>> predicted_fc = get_predictedFC(state_probs, statesFC)
celltraj.translate.get_state_decomposition(x_fc, state_probs, npermutations=500, inds_tm_training=None, save_file=None, visual=False, verbose=True, nchunk=100, gene_names=None, lb=None, ub=None)

Decompose paired bulk average data (e.g. bulk RNAseq or gene expression measurement) into state-specific contributions using least squares optimization.

Parameters:

  • x_fc (ndarray) – Fold change matrix (samples x genes).

  • state_probs (ndarray) – State probability matrix (samples x states).

  • npermutations (int, optional) – Number of permutations for training set decompositions (default is 500).

  • inds_tm_training (ndarray, optional) – Indices of training set conditions (default is None).

  • save_file (str, optional) – File path to save the state-specific fold changes (default is None).

  • visual (bool, optional) – If True, visualizes the decomposition process (default is False).

  • verbose (bool, optional) – If True, provides detailed logs during the decomposition process (default is True).

  • nchunk (int, optional) – Chunk size for logging and saving intermediate results (default is 100).

  • gene_names (ndarray, optional) – Names of the genes (default is None).

  • lb (ndarray, optional) – Lower bounds for the linear least squares optimization (default is None, which sets to zeros).

  • ub (ndarray, optional) – Upper bounds for the linear least squares optimization (default is None, which sets to infinity).

Returns:

x_fc_states – State-specific fold change matrix (states x genes).

Return type:

ndarray

Notes

If the state corresponds to the same RNA level regardless of the ligand treatment, then the measured average fold change for gene g in condition t can be decomposed into a linear combination of state-specific fold changes s_g and state probabilities p_t, such that:

\[x_{tg} = \sum_{i=1}^{n} p_{ti} s_{ig}\]

where: - x_{tg} is the measured fold change for gene g in condition t. - p_{ti} is the probability of state i in condition t. - s_{ig} is the state-specific fold change for state i and gene g. - n is the number of states.

Examples

>>> x_fc = np.random.rand(10, 5000)  # Example fold change data
>>> state_probs = np.random.rand(10, 3)  # Example state probability data
>>> x_fc_states = get_state_decomposition(x_fc, state_probs)
celltraj.translate.get_null_correlations(x_fc, x_fc_states, x_fc_predicted, nrandom=500, seed=None, tmfSet=None)

Calculate null correlations for predicted and real fold changes.

Parameters:

  • x_fc (ndarray) – Measured fold change matrix (conditions x genes).

  • x_fc_states (ndarray) – State-specific fold change matrix (states x genes).

  • x_fc_predicted (ndarray) – Predicted fold change matrix (conditions x genes).

  • nrandom (int, optional) – Number of random permutations for generating null distributions (default is 500).

  • seed (int, optional) – Random seed for reproducibility (default is None).

  • tmfSet (ndarray, optional) – Array of treatment names or identifiers (default is None).

Returns:

  • corrSet_pred (ndarray) – Correlations between predicted and real fold changes for each condition.

  • corrSet_rand (ndarray) – Null correlations between randomly generated state probabilities and real fold changes.

  • corrSet_predrand (ndarray) – Null correlations between predicted fold changes and fold changes from randomly generated state probabilities.

Notes

This function generates null distributions by randomly permuting state probabilities and calculating the corresponding fold changes. The correlations between these null fold changes and the real/predicted fold changes are computed to evaluate the significance of the predictions.

Examples

>>> x_fc = np.random.rand(10, 5000)  # Example fold change data
>>> x_fc_states = np.random.rand(3, 5000)  # Example state-specific fold changes
>>> x_fc_predicted = get_predictedFC(state_probs, x_fc_states)  # Example predicted fold changes
>>> corr_pred, corr_rand, corr_predrand = get_null_correlations(x_fc, x_fc_states, x_fc_predicted)