Comments (5)
Hi, it could be due to missing values. Otherwise, are you able to share the inputs for these specific examples?
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Hi,
I am sorry, but I am not able to share the input data, I only have the summary stats. But it seems to me that the problem is the SNP, although I am not sure if this would be due to missing data, as ma_samples and ma_counts are correct, are you suggesting that these SNP could be coded as NaN in some samples?
Thanks!
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Hi,
I also got NaN for phenotypes with NA. Is it possible that tensorQTL can deal with the missing values of phenotypes? Thank you so much!
All the best,
Diana
from tensorqtl.
Without more information to reproduce this, the most likely reason for NaNs in the output are missing values. Missing genotypes are imputed to the mean, but missing phenotypes are not imputed since the best imputation strategy is likely data-specific. You'll need to impute/remove any missing phenotypes before running tensorQTL.
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Hi François,
Thank you so much for the answer, we will take this into count.
Best,
Ariadna Cilleros
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Related Issues (20)
- missing tss_distance in cis output HOT 1
- Abnormal results of interaction model HOT 4
- scipy.optimize.newton failed to converge HOT 1
- Warning and Error in tensorQTL trans mode HOT 9
- Memory Allocation Issue during trans-QTL Mapping HOT 3
- Issues with TensorQTL in Trans Mode
- AttributeError: 'dict' object has no attribute 'T' tensorqtl HOT 2
- broken link HOT 1
- Tensorqtl installation HOT 3
- No credible sets output from susie.map because pval_nominal=0 HOT 2
- ValueError: array must not contain infs or NaNs
- interaction HOT 1
- [Susie] ValueError: prior variance must be non-negative HOT 4
- [trans] trans.map_permutations(): KeyError: 'r2' HOT 2
- [map_trans] R2 not returned in interaction analysis HOT 2
- [pval post-processing] Interaction output for cis/trans
- Which beta coresponds to which allele in the outputs?
- cis-mapping with interaction
- Errors in the BackgroundGenerator cause main thread to get stuck
- Questions about the `map_permutations` and `apply_permutations` functions
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