Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Modelling sequential branching dynamics with a multivariate branching Gaussian process
Authors: Elvijs Sarkans, Sumon Ahmed, Magnus Rattray, Alexis Boukouvalas
TMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We examine the effectiveness of our approach on synthetic data and a single-cell RNA-Seq dataset from mouse haematopoietic stem cells (HSCs). Our approach ensures assignment consistency by design and achieves improved accuracy in branching time inference and assignment accuracy. 5 Experimental results We demonstrate our approach by fitting both MBGP and BGP models to noisy synthetic data. [...] We will also fit the MBGP model to real data, namely, single-cell RNA-seq data from mouse hematopoietic stem cells (Paul et al., 2015) and see how it resolves the cell label inconsistency problem whilst retaining biologically sensible fits. |
| Researcher Affiliation | Collaboration | Elvijs Sarkans EMAIL BIOS Health Sumon Ahmed EMAIL University of Dhaka Magnus Rattray EMAIL University of Manchester Alexis Boukouvalas EMAIL PROWLER.io |
| Pseudocode | No | The paper describes the model architecture and inference procedure using mathematical formulations and textual descriptions, but does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about providing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We examine the effectiveness of our approach on synthetic data and a single-cell RNA-Seq dataset from mouse haematopoietic stem cells (HSCs). [...] We apply the MBGP model on the gene expression of mouse hematopoietic stem cells used in Paul et al. (2015). [...] Franziska Paul, Ya ara Arkin, Amir Giladi, Diego Adhemar Jaitin, Ephraim Kenigsberg, Hadas Keren-Shaul, Deborah Winter, David Lara-Astiaso, Meital Gury, Assaf Weiner, et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell, 163(7):1663 1677, 2015. |
| Dataset Splits | No | To speed up inference, we have randomly sub-sampled the data and have used 1000 cells for the inference and have used the sparse model with 60 inducing points. [...] We have used a sub-sample of 870 cells and 30 inducing points to speed up the model inference. |
| Hardware Specification | Yes | 1System configuration: Intel(R) Core(TM) i7-1065G7 CPU @ 1.30GHz 1.50 GHz, 16 GB RAM, 64-bit operating system, x64-based processor. |
| Software Dependencies | No | The paper mentions methods and algorithms like Monocle and Wishbone, but does not specify any software dependencies with version numbers for their own implementation of MBGP. |
| Experiment Setup | Yes | We initialise data point label probabilities for latent branches by sampling uniformly from [0.5, 1] for the true label. We then set the data point label prior probabilities to 0.5 for inputs in [0, 0.8] (that is, no prior knowledge) and then 0.8 for the true label for inputs in [0.8, 1], which we will refer to as the informative prior. [...] Unless explicitly stated otherwise, the fitting procedure is a multiple restart from 4 different branching points, namely, 0.2, 0.4, 0.6, 0.8 applied uniformly to all outputs. For each restart, we perform gradient descent along the evidence lower bound surface using L-BFGS-B. |