Instructional tutorial for using demuxlet v1
Bibliographic record
Abstract
Genetic multiplexing of barcoded single cell RNA-seq Introduction demuxlet is a software tool to deconvolute sample identity and identify multiplets when multiple samples are pooled by barcoded single cell sequencing.demuxlet takes (1) a SAM/BAM/CRAM file produced by the standard 10x sequencing platform, or any other barcoded single cell RNA-seq (with proper --tag-UMI and --tag-group) options (2) a VCF/BCF file containing the genotype (GT), posterior probability (GP), or genotype likelihood (GL) to assign each barcode to a specific sample (or a pair of samples) in the VCF file. Tutorial for demuxlet This tutorial provides streamlined instructions for using the tool demuxlet. For a more detailed description of all of the options available to use with demuxlet, please refer to the README (attached in .zip file). demuxlet has several dependencies and this tutorial provides steps to use a docker instance to minimize compatibility issues and an alternative of installing demuxlet from source. demuxlet is a software tool to deconvolute sample identity and identify multiplets when multiple samples are pooled by barcoded single cell sequencing. demuxlet requires the following input files: a SAM/BAM/CRAM file produced by the standard 10x sequencing platform, or any other barcoded single cell RNA-seq (with proper --tag-UMI and --tag-group) options. a VCF/BCF file containing the genotype (GT), posterior probability (GP), or genotype likelihood (GL) to assign each barcode to a specific sample (or a pair of samples) in the VCF file.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.504 | 0.376 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".