bayer-group/cellenium

Cellenium is a FAIR and scalable interactive visual analytics app for scRNA-Seq data (single-cell RNA sequencing).

cellenium's Issues

expression plots: show amount of samples next to the boxes / violins

open task prior to release

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for generated cell types using the user annotation the generated DEGs are not shown

it's for sure calculated and written into the database. must be due to some changes for multiomics

add CITE-Seq and ATAC-Seq studies

align ts-vector vs ts-query dictionary (e.g. mouse)

cross-study heatmap should differentiate "low expression" vs. "cell type not measured at all in this study"

e.g. here we do have a very small fraction (0.002150) of cells of astrocyte cell type, but it isn't visible:

log of pvalues = 0 is inf, so they are not shown in volcano plot

study list filter: search for words in title, description; Pubmed ID

user-defined cell annotations: no DEG calculation for multi-omics (h5mu) studies

• first of all, the study h5mu file must be opened the the muon library
• instead of h5ad_obs_index, we should use h5ad_obs_key to safely identify samples
• multiple differential-anything (genes, regulatory regions etc) can be calculated using the modalities in h5mu

clustering for the rna part of the PBMC citeSeq data set looks wrong although correct in jupyter

small support python package for study preparation

Users who are preparing h5ad files for cellenium should use the code in h5ad_preparation.py. There should be a python library built, and being installable via PyPi, for this use case. While the library isn't there, users can copy and update h5ad_preparation.py on their own

make the correlation values of pairwise scatter and correlation mining correspond (do not remove 0)

enable filtering by cell ontology and species

make the autocomplete nicer so they show the ontology type

color change of selected annotation group after gene expression is plotted on top (e.g. pancreas data set, with CellO CD14-positive and VCAN gene)

multiple data layers (e.g. another matrix with imputed values)

Database, API and client already work with study_layer_id to distinguish multiple layers per study. For scRNA data, multiple layers get imported but only the first one is displayed in the UI.

• UI selection for layers (at least in Distribution Analysis view)
• for multiple modalities (h5mu files), the layers are imported from all modalities where they exist
• decide if multiple modalities can be in the same layer - currently it is done that way and it is convenient, but the database column study_layer.omics_type is out of line with the implementation

display species in study overview card

filter studies before fetching data for cross-study view, e.g. filter by tissue

user prepared study upload

• S3 as h5ad study import source, in addition to local file system
• mechanism for granting temp credentials to S3 prefix to users, for upload
• simple admin UI for maintaining study metadata after initial import, and associating user groups for access rights
• discovery of uploaded S3 files
• docker container that can run unattended, imports discovered studies

user annotation coexpression plot should show a (0,0) sample for selecting this case

user requested gene correlation does not yet work for multiomics

as the h5mu is access the modality needs to be handed over for e.g. mdata.mod['gene']

extend gene IDs, symbols, descriptions with further species

Using the Biomart client, we could add data for drosophila, macaca fascicularis and maybe more. This could be done by invoking the get_gene_mappings for these Tax IDs in addition, so that data for these species is added to omics_gene and omics_base just like the other species.

In case species aren't covered by Biomart and we want an "on the fly" definition of any gene IDs, symbols and gene descriptions from the gene annotation dataframe of an imported h5ad file, the import process needs to be extended. Currently, it would just raise any error message for unknown species or unknown genes.

remove annotation select in user annotation view

scale violinplot down to 100% of availabe space

expression plots: option to combine 'equivalent' annotations in one category

Example: many different CD8 T cell subtypes (memory, effector, naive ...) can be all grouped in one CD8 T cell celltype

Maybe there is a lightweight way for a user to reuse a grouping once it is defined (browser local storage?)