# Transcriptional Response of SK-N-AS Cells to Methamidophos

## TL;DR

This study investigates the transcriptional response of SK-N-AS cells to methamidophos, revealing upregulated processes, key transcripts, and causal networks using advanced machine learning and statistical methods over multiple time points.

## Contribution

It introduces novel machine learning algorithms for anomaly detection and causal inference in transcriptomics data related to methamidophos exposure.

## Key findings

- Upregulation of unfolded protein response and calcium signaling
- Identification of key transcripts involved in response
- Inferred causal networks of transcript interactions

## Abstract

Transcriptomics response of SK-N-AS cells to methamidophos (an acetylcholine esterase inhibitor) exposure was measured at 10 time points between 0.5 and 48 h. The data was analyzed using a combination of traditional statistical methods and novel machine learning algorithms for detecting anomalous behavior and infer causal relations between time profiles. We identified several processes that appeared to be upregulated in cells treated with methamidophos including: unfolded protein response, response to cAMP, calcium ion response, and cell-cell signaling. The data confirmed the expected consequence of acetylcholine buildup. In addition, transcripts with potentially key roles were identified and causal networks relating these transcripts were inferred using two different computational methods: Siamese convolutional networks and time warp causal inference. Two types of anomaly detection algorithms, one based on Autoencoders and the other one based on Generative Adversarial Networks (GANs), were applied to narrow down the set of relevant transcripts.