Workflow of D3Impute framework.

Workflow of D3Impute framework.

<p>(A) Distribution-aware preprocessing denoiser: Quality control and denoising of raw scRNA-seq (<i>X</i><sub><i>raw</i>−<i>seq</i></sub>) and bulk RNA-seq (<i>X</i><sub><i>raw</i>−<i>bulk</i></sub>...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Siyi Huang (8562174) (author)
مؤلفون آخرون:	Linfeng Jiang (2416375) (author), Ming Yi (15051) (author), Yuan Zhu (148570) (author)
منشور في:	2025
الموضوعات:	Genetics Molecular Biology Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> single uses bulk rna recovers expression values providing clear guidelines including cell clustering false measurements caused extensive practical evaluation essential downstream analyses differential expression detection cell rna sequencing cell &# 8217 three key innovations guided imputation engine distort biological signals aware normalization step d3impute demonstrates consistent accurately identify non true biological zeros seq data analysis biological zeros true transcriptome key hurdle guide imputation biological reference aware modeling aware discrimination seq data inflated data data recovery trajectory inference technical limitations specific characteristics significant improvements oriented solution optimal application network discriminator major challenge introduce d3impute handling zero genuinely absent generalizable framework fundamental issue computational methods comprehensive benchmarking cellular heterogeneity biologically informed also offers 12 state
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