Biology subjects

Kirchgaessner, R.

Publications and source records attributed to Kirchgaessner, R..

3 recordsLinked to original sources

bioRxiv2025

Aggregating multimodal cancer data across unaligned embedding spaces maintains tumor of origin signal

AI based embeddings offer the possibilities of encoding complex biological data into low dimensional spaces, called embedding spaces, that maintain the relationships between entities. There is an open question about the compatibility of embedding spaces that are created without any coordination. It has been assumed that signals in these unaligned embedding spaces would be destroyed if vectors were aggregated into summed values. We trained embedding models across different data modalities and tested aggregating the values together to test this assumption. Our research shows that signal from unaligned embedded values is conserved and able to still be used for learning tasks, such as data modality and tumor of origin recognition.

bioinformatics↗

bioRxiv2023

Imputing Single-Cell Protein Abundance in Multiplex Tissue Imaging

Multiplex tissue imaging are a collection of increasingly popular single-cell spatial proteomics and transcriptomics assays for characterizing biological tissues both compositionally and spatially. However, several technical issues limit the utility of multiplex tissue imaging, including the limited number of molecules (proteins and RNAs) that can be assayed, tissue loss, and protein probe failure. In this work, we demonstrate how machine learning methods can address these limitations by imputing protein abundance at the single-cell level using multiplex tissue imaging datasets from a breast cancer cohort. We first compared machine learning methods strengths and weaknesses for imputing single-cell protein abundance. Machine learning methods used in this work include regularized linear regression, gradient-boosted regression trees, and deep learning autoencoders. We also incorporated cellular spatial information to improve imputation performance. Using machine learning, single-cell protein expression can be imputed with mean absolute error ranging between 0.05-0.3 on a [0,1] scale. Finally, we used imputed data to predict whether single cells were more likely to come from pre-treatment or post-treatment biopsies. Our results demonstrate (1) the feasibility of imputing single-cell abundance levels for many proteins using machine learning; (2) how including cellular spatial information can substantially enhance imputation results; and (3) the use of single-cell protein abundance levels in a use case to demonstrate biological relevance.

cancer biology↗

bioRxiv2022

Computational Pipeline to Identify Gene signatures that Define Cancer Subtypes

MotivationThe heterogeneous nature of cancers with multiple subtypes makes them challenging to treat. However, multi-omics data can be used to identify new therapeutic targets and we established a computational strategy to improve data mining. ResultsUsing our approach we identified genes and pathways specific to cancer subtypes that can serve as biomarkers and therapeutic targets. Using a TCGA breast cancer dataset we applied the ExtraTreesClassifier dimensionality reduction along with logistic regression to select a subset of genes for model training. Applying hyperparameter tuning, increased the model accuracy up to 92%. Finally, we identified 20 significant genes using differential expression. These targetable genes are associated with various cellular processes that impact cancer progression. We then applied our approach to a glioma dataset and again identified subtype specific targetable genes. ConclusionOur research indicates a broader applicability of our strategy to identify specific cancer subtypes and targetable pathways for various cancers.

bioinformatics↗