Automated Author ProfileMair, Anna
Department of Internal Medicine V, Hematology & Oncology, Comprehensive Cancer Center Innsbruck (CCCI) and Tyrolean Cancer Research Institute (TKFI), Medical University of Innsbruck, Innsbruck, Austria
Mair, Anna
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.6 (sum of 3 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Microwell-based (BD Rhapsody) scRNA-seq of Hairy Cell Leukemia Patients published in Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target, Jan-Paul Bohn et al. Submitted.The files will be made available upon publication. Description of the files01_raw_counts: count matrices as CSV as generated by the BD Rhapsody WTA analysis pipeline10_prepare_adata: Load BD Rhapsody WTA analysis pipeline outputs into AnnData objects and add metadata.20_scrnaseq_qc: Use a nextflow pipeline (stored in lib/single-cell-analysis-nf) to perform threshold-based filtering of single-cell data and apply SOLO for doublet detection.30_merge_adata: Merge samples into a single AnnData object, train a scVI model for batch effect removal, and annotate cell-types based on unsupervised clustering40_cluster_analysis: Identify and investigate subclusters representing cell-states that go beyond the major cell-types50_de_analysis: Generate pseudobulk and perform differential gene expression analysis using DESeq2 (based on a wrapper script stored in lib/deseq2_workflow)70_downstream_analysis: Perform pathway analyses and generate figures for publication based on the data generated in the previous stepscontainers: Conda environments used for the analysis packed up as singularity containers.
Authors
- Bohn, Jan-Paul ;
- Sturm, Gregor ;
- Mair, Anna ;
- Scheiber, Alexandra ;
- Kugler, Valentina ;
- Feichtner, Andreas ;
- Fritz, Alexandra ;
- Torres-Quesada, Omar ;
- Jaeger, Ulrich ;
- Brunner, Andrea ;
- Pircher, Andreas ;
- Sopper, Sieghart ;
- Eduard, Stefan ;
- Trajanoski, Zlatko ;
- Salcher, Stefan ;
- Wolf, Dominik
Microwell-based (BD Rhapsody) scRNA-seq of Hairy Cell Leukemia Patients published in Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target, Jan-Paul Bohn et al. Submitted.The files will be made available upon publication. Description of the files01_raw_counts: count matrices as CSV as generated by the BD Rhapsody WTA analysis pipeline10_prepare_adata: Load BD Rhapsody WTA analysis pipeline outputs into AnnData objects and add metadata.20_scrnaseq_qc: Use a nextflow pipeline (stored in lib/single-cell-analysis-nf) to perform threshold-based filtering of single-cell data and apply SOLO for doublet detection.30_merge_adata: Merge samples into a single AnnData object, train a scVI model for batch effect removal, and annotate cell-types based on unsupervised clustering40_cluster_analysis: Identify and investigate subclusters representing cell-states that go beyond the major cell-types50_de_analysis: Generate pseudobulk and perform differential gene expression analysis using DESeq2 (based on a wrapper script stored in lib/deseq2_workflow)70_downstream_analysis: Perform pathway analyses and generate figures for publication based on the data generated in the previous stepscontainers: Conda environments used for the analysis packed up as singularity containers.
Authors
- Bohn, Jan-Paul ;
- Sturm, Gregor ;
- Mair, Anna ;
- Scheiber, Alexandra ;
- Kugler, Valentina ;
- Feichtner, Andreas ;
- Fritz, Alexandra ;
- Torres-Quesada, Omar ;
- Jaeger, Ulrich ;
- Brunner, Andrea ;
- Pircher, Andreas ;
- Sopper, Sieghart ;
- Eduard, Stefan ;
- Trajanoski, Zlatko ;
- Salcher, Stefan ;
- Wolf, Dominik
Microwell-based (BD Rhapsody) scRNA-seq of Hairy Cell Leukemia Patients published in Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target, Jan-Paul Bohn et al. Submitted.The files will be made available upon publication. Description of the files01_raw_counts: count matrices as CSV as generated by the BD Rhapsody WTA analysis pipeline10_prepare_adata: Load BD Rhapsody WTA analysis pipeline outputs into AnnData objects and add metadata.20_scrnaseq_qc: Use a nextflow pipeline (stored in lib/single-cell-analysis-nf) to perform threshold-based filtering of single-cell data and apply SOLO for doublet detection.30_merge_adata: Merge samples into a single AnnData object, train a scVI model for batch effect removal, and annotate cell-types based on unsupervised clustering40_cluster_analysis: Identify and investigate subclusters representing cell-states that go beyond the major cell-types50_de_analysis: Generate pseudobulk and perform differential gene expression analysis using DESeq2 (based on a wrapper script stored in lib/deseq2_workflow)70_downstream_analysis: Perform pathway analyses and generate figures for publication based on the data generated in the previous stepscontainers: Conda environments used for the analysis packed up as singularity containers.
Authors
- Bohn, Jan-Paul ;
- Sturm, Gregor ;
- Mair, Anna ;
- Scheiber, Alexandra ;
- Kugler, Valentina ;
- Feichtner, Andreas ;
- Fritz, Alexandra ;
- Torres-Quesada, Omar ;
- Jaeger, Ulrich ;
- Brunner, Andrea ;
- Pircher, Andreas ;
- Sopper, Sieghart ;
- Eduard, Stefan ;
- Trajanoski, Zlatko ;
- Salcher, Stefan ;
- Wolf, Dominik