Data files

For each module, GSEA analysis was conducted by using web-tool g:profiler and only biological process (BP) were retained. Parameters for GSEA are default.

Calculated by using R package GOSemSim.

Modules were identified by WGCNA. hub genes of each modules were identified based on intramodular degree.

Including 91 genes and 1222 Breast cancer patient This is an input data for WGCNA

Including 289 genes and 1222 Breast cancer patient This is an input data for WGCNA

Including 277 genes and 1222 Breast cancer patients. This is an input data for WGCNA.

Including 294 genes and 1222 Breast cancer Patient. This is an input data for WGCNA

Consensus between selected mapping methods.

Additional file 2

Additional File 1

Hallmark gene sets belong to different mapping schemes.

derived from Ulhen's research published in 2017.

The excel presents the mapping from GO terms to individual cancer hallmarks retrieved from selected papers.

It includes annotation number data at June 2012, June 2016 and Jan 2021.

related to Figure 6 and additional file 6

it can be used to construct the GO network

It can be used to construct the GO network

columns are ' id' , 'ncbi_taxa_id' , 'common_names' , 'lineage_string' , 'genus' , 'species' , 'parent_id' , 'left_value' , 'right_value' , 'taxonomic_rank'.

columns are ' id' , 'ncbi_taxa_id' , 'common_names' , 'lineage_string' , 'genus' , 'species' , 'parent_id' , 'left_value' , 'right_value' , 'taxonomic_rank'.

columns are ' id' , 'ncbi_taxa_id' , 'common_names' , 'lineage_string' , 'genus' , 'species' , 'parent_id' , 'left_value' , 'right_value' , 'taxonomic_rank'.

columns are 'id' ,'name', 'term_type' , 'GOID', 'is_obsolete','is_root' and 'is_relation'

columns are ' id' , 'ncbi_taxa_id' , 'common_names' , 'lineage_string' , 'genus' , 'species' , 'parent_id' , 'left_value' , 'right_value' , 'taxonomic_rank'.

columns are 'id' ,'name', 'term_type' , 'GOID', 'is_obsolete','is_root' and 'is_relation'

Visualization of the workflow demonstrating a step-by-step explanation for a sc-SynO analysis. a) Several or one snRNA-Seq or scRNA-Seq fastq datasets can be used as an input. Here, we identify our cell population of interest and provide raw or normalized read counts of this specific population to sc-SynO for training. b) Further information for cluster annotation and processed count data are serving as input for the core algorithm. c) Based on the data input, we utilize the LoRAS synthetic ...

Validation of the sc-SynO model for the first use case of cardiac glial cell annotation. UMAP representation of the manually clustered Bl6 dataset of Wolfien et al. (2020) Precicted cells of sc-SynO are highlighted in blue, cells not chosen are grey. UMAP representation of the manually clustered dataset of Vidal (2019). Precicted cells of sc-SynO are highlighted in blue, cells not chosen are grey. Average expression of the respective top five cardiac glial cell marker genes for both validation ...

Validation of the sc-SynO model for the second use case of proliferative cardiomyocytes annotation. a) UMAP representation of the manually clustered single-nuclei dataset of Linscheid et al. (2019) Precicted cells of sc-SynO are highlighted in blue (based on top 20 selected features in the training model), red (based on top 100 selected features in the training model) cells not chosen are grey. b) UMAP representation of the manually clustered dataset of Vidal et al. (2020). PPrecicted cells of ...