Selecting Markers¶
- PicturedRocks current implements two categories of marker selection algorithms:
- mutual information-based algorithms
- 1-bit compressed sensing based algorithms
Mutual information¶
TODO: Explanation of how these work goes here.
Before running any mutual information based algorithms, we need a discretized
version of the gene expression matrix, with a limited number of discrete
values (because we do not make any assumptions about the distribution of gene
expression). Such data is stored in
picturedrocks.markers.InformationSet
, but by default, we suggest
using picturedrocks.markers.makeinfoset()
to generate such an object
after appropriate normalization
Iterative Feature Selection¶
All information-theoretic feature selection methods in PicturedRocks are
greedy algorithms. In general, they implement the abstract class
IterativeFeatureSelection
class. See Supervised Feature Selection and Unsupervised Feature Selection
for specific algorithms.
-
class
picturedrocks.markers.mutualinformation.iterative.
IterativeFeatureSelection
(infoset)¶ Abstract Class for Iterative Feature Selection
Auxiliary Classes and Methods¶
-
class
picturedrocks.markers.
InformationSet
(X, has_y=False)¶ Stores discrete gene expression matrix
Parameters: - X (numpy.ndarray) – a (num_obs, num_vars) shape array with
dtype
int
- has_y (bool) – whether the array X has a target label column (a y column) as its last column
- X (numpy.ndarray) – a (num_obs, num_vars) shape array with
-
class
picturedrocks.markers.
SparseInformationSet
(X, y=None)¶ Stores sparse discrete gene expression matrix
Parameters: - X (scipy.sparse.csc_matrix) – a (num_obs, num_vars) shape matrix with
dtype
int
- has_y (bool) – whether the array X has a target label column (a y column) as its last column
- X (scipy.sparse.csc_matrix) – a (num_obs, num_vars) shape matrix with
-
picturedrocks.markers.
makeinfoset
(adata, include_y, k=5)¶ Discretize data and make a Sparse InformationSet object
Parameters: - adata (anndata.AnnData) – The data to discretize. By default data is discretized as round(log2(X + 1)).
- include_y (bool) – Determines if the y (cluster label) column in included in the InformationSet object
Returns: An object that can be used to perform information theoretic calculations.
Return type:
Interactive Marker Selection¶
-
class
picturedrocks.markers.interactive.
InteractiveMarkerSelection
(adata, feature_selection, disp_genes=10, connected=True, show_cells=True, show_genes=True, dim_red='tsne')¶ Run an interactive marker selection GUI inside a jupyter notebook
Parameters: - adata (anndata.AnnData) – The data to run marker selection on. If you want to restrict to a small number of genes, slice your anndata object.
- feature_selection (picturedrocks.markers.mutualinformation.iterative.IterativeFeatureSelection) – An instance of a interative feature selection algorithm class that corresponds to adata (i.e., the column indices in feature_selection should correspond to the column indices in adata)
- disp_genes (int) – Number of genes to display as options (by default, number of genes plotted on the tSNE plot is 3 * disp_genes, but can be changed by setting the plot_genes property after initializing.
- connected (bool) – Parameter to pass to plotly.offline.init_notebook_mode. If your browser does not have internet access, you should set this to False.
- show_cells (bool) – Determines whether to display a tSNE plot of the cells with a drop-down menu to look at gene expression levels for candidate genes.
- show_genes (bool) – Determines whether to display a tSNE plot of genes to visualize gene similarity
- dim_red ({"tsne", "umap"}) – Dimensionality reduction algorithm
Warning
This class requires modules not explicitly listed as dependencies of picturedrocks. Specifically, please ensure that you have ipywidgets installed and that you use this class only inside a jupyter notebook.
-
picturedrocks.markers.interactive.
cife_obj
(H, i, S)¶ The CIFE objective function for feature selection
Parameters: Returns: the candidate feature’s score relative to the selected gene set S
Return type: