التفاصيل البيبلوغرافية
| العنوان: |
Distribution of SWAM weights in imputation models for all 44 GTEx v6 tissues. |
| المؤلفون: |
Andrew E. Liu (12024808), Hyun Min Kang (9690325) |
| سنة النشر: |
2022 |
| المجموعة: |
Smithsonian Institution: Digital Repository |
| مصطلحات موضوعية: |
Genetics, Cancer, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, unravel regulatory impacts, single reference cohort, large eqtl study, effective sample size, depression susceptibility genes, combines arbitrary number, regulatory variants identified, impute expression levels, training imputation models, combined 49 tissue, expression data across, integrating multiple tissues, tested imputation accuracy, imputation accuracy given, transcriptome imputation models, leverage multiple tissues, multiple tissues, tissue expression, level data, genetic variants, gene expression, cannot leverage, transcriptome imputation, imputation method, tissue type, target tissue |
| الوصف: |
Here, we used SWAM to derive multi-tissue imputation models for all 44 GTEx v6 tissues. Each cell in this heatmap depicts the number of times each tissue contributed the highest weight to the target tissue. Here, the rows correspond to the target tissue and the columns correspond to the weight contribution of each tissue. For the sake of clarity, the diagonal values were not included as they were consistently much higher than the remaining elements of the matrix. (PDF) |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
unknown |
| Relation: |
https://figshare.com/articles/journal_contribution/Distribution_of_SWAM_weights_in_imputation_models_for_all_44_GTEx_v6_tissues_/19099081 |
| DOI: |
10.1371/journal.pgen.1009571.s007 |
| الاتاحة: |
https://doi.org/10.1371/journal.pgen.1009571.s007 |
| Rights: |
CC BY 4.0 |
| رقم الانضمام: |
edsbas.F64E50F0 |
| قاعدة البيانات: |
BASE |