add support for layers in AnnData objects

[jkanche]

Currently the app uses the X slot of the AnnData object for extracting expression values. It would be great to supports the layers slot and enable exploration of any assay (or matrix) from the AnnData object

[joshua-gould]

You can join to h5ad files using launch by separating multiple paths by a comma, or in serve by supplying multiple datasets to prepare_data. I prefer this approach because both dimensions of arrays in layers must match X, which is not the case for multimodal data.

[jkanche]

the release notes of anndata mentions they are unifying the layers and X in the upcoming release - https://anndata.readthedocs.io/en/latest/release-notes.html

I am not sure what this entails, but something to keep track of...

[joshua-gould]

@jkanche Is your use case to support multi-modal data?

[jkanche]

Its not, i can imagine a dataset containing many layers - counts, logcounts, normalized counts and so on... it would be nice to choose which layer to use for coloring especially when i color the umap/tsne by expression of a gene. right now, its pretty much whatever X is and the user has less/no control on this.

[joshua-gould]

Do you want to be able to view multiple layers simultaneously for easy comparison? Thanks.

[MubasherMohammed]

@joshua-gould Hi, thanks for this great app! yes, would it be possible to chose which layer to use in anndata object? i.e if someone can chose raw_data or X as well switch between different layers stored in the anndata object?

Thanks

[joshua-gould]

@MubasherMohammed Do you want to be able to view multiple layers simultaneously or would switching between which layer is shown be sufficient? Thanks.

[MubasherMohammed]

@joshua-gould thanks for swift reply! switching between layers could be sufficient. alternatively, some single-cell data objects contain layers of spliced/unspliced reads. it will be great to consider this info to the app in the future.

thanks..

[joshua-gould]

Version 1.1.34 provides support for layers. Please let me know what you think. Thanks.

[MubasherMohammed]

@joshua-gould thanks for the update! i was unable to install 1.1.34 using pip install git+https://github.com/klarman-cell-observatory/cirrocumulus.git@1.1.34 as well pip install git+https://github.com/klarman-cell-observatory/cirrocumulus.git@layers-support
is there any alternative i can use to install supported layers version? thanks

[joshua-gould]

Please run the following command to update to the latest version:
pip install --upgrade cirrocumulus

[MubasherMohammed]

Thanks, it has now successfully updated. Looking at the difference between "Cell Metadata" and "Metadata Categories" it seems they are belonging to <adata.obs> or at least its how my Anndata structure look like:
AnnData object with n_obs × n_vars = 4080 × 1313 obs: 'n_genes', 'umi_counts', 'louvain_1.0', 'louvain_0.6', 'louvain_0.4', 'louvain_1.4', 'clusters', 'S_score', 'G2M_score', 'phase', 'male_signature', 'female_signature', 'cell_class', 'annotation', 'initial_size_spliced', 'initial_size_unspliced', 'initial_size', 'n_counts', 'velocity_self_transition', 'velocity_length', 'velocity_confidence', 'velocity_confidence_transition', 'root_cells', 'end_points', 'velocity_pseudotime', 'latent_time', 'terminal_states', 'terminal_states_probs', 'clusters_gradients', 'initial_states', 'initial_states_probs', 'dpt_pseudotime', 'velocity_pseudotime_categorical' var: 'gene_ids', 'feature_types', 'genome', 'mt', 'ribo', 'hb', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std', 'Accession', 'Chromosome', 'End', 'Start', 'Strand', 'velocity_gamma', 'velocity_qreg_ratio', 'velocity_r2', 'velocity_genes', 'spearmans_score', 'velocity_score', 'fit_alpha', 'fit_beta', 'fit_gamma', 'fit_t_', 'fit_scaling', 'fit_std_u', 'fit_std_s', 'fit_likelihood', 'fit_u0', 'fit_s0', 'fit_pval_steady', 'fit_steady_u', 'fit_steady_s', 'fit_variance', 'fit_alignment_scaling', 'fit_r2', 'fit_diff_kinetics', 'fit_pval_kinetics', 'to C1 corr', 'to C1 qval', 'to C5 corr', 'to C5 qval' uns: 'cell class_colors', 'cell_class_colors', 'clusters_colors', 'dendrogram_cell_class', 'dendrogram_clusters', 'hvg', 'louvain', 'louvain_0.4_colors', 'louvain_0.6_colors', 'louvain_1.0_colors', 'louvain_1.4_colors', 'neighbors', 'pca', 'phase_colors', 'tsne', 'umap', 'wilcoxon', 'annotation_colors', 'velocity_params', 'velocity_graph', 'velocity_graph_neg', 'rank_velocity_genes', 'paga', 'cell_class_sizes', 'recover_dynamics', 'rank_dynamical_genes', 'T_fwd_params', 'eig_fwd', 'terminal_states_colors', 'terminal_states_names', 'clusters_gradients_colors', 'to_terminal_states_names', 'to_terminal_states_colors', 'T_bwd_params', 'eig_bwd', 'initial_states_colors', 'initial_states_names', 'iroot', 'diffmap_evals', 'velocity_pseudotime_categorical_colors' obsm: 'X_pca', 'X_tsne', 'X_umap', 'velocity_pca', 'velocity_umap', 'macrostates_fwd', 'to_terminal_states', 'macrostates_bwd', 'X_diffmap' varm: 'PCs', 'loss', 'fit_pvals_kinetics' layers: 'ambiguous', 'matrix', 'spliced', 'unspliced', 'Ms', 'Mu', 'velocity', 'variance_velocity', 'fit_t', 'fit_tau', 'fit_tau_', 'velocity_u' obsp: 'connectivities', 'distances', 'T_fwd', 'T_bwd'

i have tried to visualize i.e one gene spliced VS unspliced reads in UMAP but i think it will only shows the spliced reads (Expression). could this because of the layers should be 'layers: 'ambiguous', 'matrix', 'spliced', 'unspliced', 'Ms', 'Mu', 'velocity', 'variance_velocity', 'fit_t', 'fit_tau', 'fit_tau_', 'velocity_u''

[joshua-gould]

You can obtain an example h5ad with layers from scvelo using the code:

import scvelo as scv
adata = scv.datasets.pancreas()
adata.write('pancreas.h5ad')

Note-the version 1.1.35 of cirrocumulus fixes an error when more than one layer is selected.

[MubasherMohammed]

I have updated to V1.1.35 and the support for layers works nicely. possible to visualize the spliced vs unspliced reads for a gene.
Excellent!