Package: scproximite
Similarity Metrics at High Dimensionality - testing for rare cell typesDocsDocumentation and reproducibility are available at:https://ebony-watson.github.io/scProximitEInstallpip install scproximiteNote: scproximite was developed using Python 3.8, of you have any issues we recommend using conda and creating a new
environment before installing:conda create --name scproximite python=3.8conda activate scproximitepip install scproximiteRun tutorialsGet tutorial data from zeonodo:https://zenodo.org/record/6443267(DOI: 10.5281/zenodo.6443266)Add to thedata/frameworkfolderRunjupyter notebookin thetutorialsfolderYou should now be able to run the tutorial notebooks. Note if you don't haveRinstalled you won't be able to
run the notebook that usesRmetrics:Proximity_Metrics_R.ipynb.DatasetsCellsiusA benchmark dataset of ~â€‰12,000 single-cell transcriptomes from eight human cell lines. The eight human cell lines were individually profiled by bulk RNA-seq, and mixed in four batches containing mixtures of two or three cell lines each for scRNA-seq profiling.Batch1: IMR90 and HCT116 (50/50)IMR90 is a fibroblast cell line, isolated from fetal lung. Female.HCT116 is from human colon carcinoma with epithelium-like morphology. Male.Batch2: A549 and Ramos (50/50)A549 is from human lung carcinoma, cell type is epithelial. Male.Ramos cells are from Burkittâ€™s lymphoma. They are lymphoblasts with B-cell characteristics. Ramos cells are very small (7-10um), so we usually find that they have fewer detected features and lower total count than other cell lines. Male.Batch3: HEK293 and H1437 (50/50)HEK293 is a cell line form human embryonic kidney cells. Female.H1437 is from lung adenocarcinoma (i.e. origin is epithelial / glandular). Male.DA234 (Batch 4): Jurkat, K562, Ramos (40% Jurkat, 55% K562 and 5% Ramos)Jurkat is a T-cell lymphoblast cell line. Male.K562 is a lymphoblast cell line wih granulocyte/erythrocyte/monocyte characteristics (fairly undifferentiated). Female.Cell-type annotation:Correlation of the single-cell to bulk expression profiles was used for cell type assignment, & Single cells were assigned to the cell type correlating most with their expression profile. Cells were excluded if their z-score correlation < 0.2, or if they correlated strongly with more than one bulk expression profile (likely doublets).SubsetsCell-typeCompleteSubset 1Subset 2HCT116174314001600HEK293200216002000IMR901039500100A549132040080Ramos1892350125H143711162703K562160638070Jurkat9621006Datasets are pre-annotated with cell_idx, Batch, cell_line, cell_cycle_phase, gene names etc. and a range of QC metrics (would not necessarily trust).
Data is downloaded as an R data object, and were subsequently processed in R. Then convereted from seurat to anndata object using SCEasy.Final datasets are located in RDM under code/DimensionalityReduction_Aim2/data/Cellsius/:Cellsius_Complete_Raw(sceasy).h5ad (Full dataset of all 8 cell lines, only pre-cursor filtering)Cellsius_Subset1_Raw(sceasy).h5ad (Subset 1 dataset of all 8 cell lines, only pre-cursor filtering)Cellsius_Subset2_Raw(sceasy).h5ad (Subset 2 dataset of all 8 cell lines, only pre-cursor filtering)subset1_sce_cleaned(SCEeasy).h5ad (Subset 1 dataset of all 8 cell lines, pre-cursor + some additional filtering)subset2_sce_cleaned(sceasy).h5ad (Subset 2 dataset of all 8 cell lines, pre-cursor + some additional filtering)None of the datasets have been normalised/transformed/scaledFiltering:Precursor (done by authors prior to uploading data publically):â‰¥ 10.5 genes per cell [log2]â‰¥ 12.0 total UMIs / cell [log2]â‰¥ 10% mitochondrial genesAdditional:Outliersâ‰¥ 3 counts in at least 1 cellSourced from:https://zenodo.org/record/3238275#.YWYVKBx_VhEPaper:https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1739-7