News: One paper accepted by EMNLP 2024, see you at Miami! Two papers accepted by NeurIPS 2024, see you in virtual!

Evaluating the Utilities of Large Language Models in Single-cell Data Analysis

Abstract: Large Language Models (LLMs) or foundation models have made significant strides in both industrial and scientific domains. In this paper, we evaluate the performance of LLMs in single-cell sequencing data analysis through comprehensive experiments across eight downstream tasks pertinent to single-cell data. By comparing seven different single-cell LLMs with task-specific methods, we found that single-cell LLMs may not consistently excel in all tasks than task-specific methods. However, the emergent abilities and the successful applications of cross-species/cross-modality transfer learning of LLMs are promising. In addition, we present a systematic evaluation of the effects of hyper-parameters, initial settings, and stability for training single-cell LLMs based on a proposed scEval framework, and provide guidelines for pre-training and fine-tuning. Our work summarizes the current state of single-cell LLMs, and points to their constraints and avenues for future investigation.

CVQVAE: A representation learning based method for multi-omics single cell data integration

Abstract: The rapid development of second-generation sequencing has brought about a significant increase in the amount of omics data. Integrating and analyzing these single-cell datasets is a challenging problem. In this paper, we propose a new model, called as CVQVAE, based on a cross-trained VAE, and strengthened by the Vector Quantization technique for multi-omics data integration. CVQVAE projects data vectors from different omics onto a common latent space in such a way that (1) similar cells are close in the latent space and (2) the original biological information present in each of the omics (including cell cycle and trajectory) are preserved. Our model is trained and optimized solely based on the multi-omics data and requires no additional information such as cell-type labels. We empirically demonstrate the stability and efficiency of our method in data integration (alignment) on datasets from a recent competition on Open Problems in Single Cell Analysis.

ResPAN: a powerful batch correction model for scRNA-seq data through residual adversarial networks

Abstract: In this article, we propose a light-structured deep learning framework called ResPAN for scRNA-seq data integration. ResPAN is based on Wasserstein Generative Adversarial Network (WGAN) combined with random walk mutual nearest neighbor pairing and fully skip-connected autoencoders to reduce the differences among batches. We also discuss the limitations of existing methods and demonstrate the advantages of our model over seven other methods through extensive benchmarking studies on both simulated data under various scenarios and real datasets across different scales. Our model achieves leading performance on both batch correction and biological information conservation and maintains scalable to datasets with over half a million cells link.

Bidirectional Prediction Model for Transcriptomics and Proteomics

Abstract: The major contribution of this work is to initially establish the transformationmodel between transcriptomics data and proteomics data. On the premise that thedata used for training should come from the same tissue, we proposed a modelbased on end-to-end learning strategy and a bidirectional encoder-decoder structure(BiAE) to predict the corresponding Antibody-derived Tag data based on anysingle-cell RNA sequence data and to predict single-cell RNA sequence data basedon its corresponding Antibody-derived Tag data. We utilized the CITE-seq dataand REAP-seq data for experiments and obtained promising results.

Specular Reflection Detection and Removal Based on Deep Neural Network for Endoscope Images

Abstract: Specular reflections have always been undesirable when processing endoscope vision for clinical purpose. Scene afflicted with strong specular reflection could result in visual confusion for the operation of surgical robot. In this paper, we propose a novel model based on deep learning framework, known as Surgical Fix Deep Neural Network (SFDNN). This model can effectively detect and fix the reflection points in different surgical videos hence opening up a whole new approach in handling undesirable specular reflections.

Deep Learning based Scheduling Sequence Generation Algorithm

Abstract: The manpower scheduling problem is a kind of critical combinational optimization problem. Researching solutions to scheduling problems can improve the efficiency of companies, hospitals, and other work units. This paper proposes a new model combined with deep learning to solve the multi-shift manpower scheduling problem based on the existing research. This model first solves the objective function’s optimized value according to the current constraints to find the plan of employee arrangement initially. It will then use the scheduling table generation algorithm to obtain the scheduling result in a short time. Moreover, the most prominent feature we propose is that we will use the neural network training method based on the time series to solve long-term and long-period scheduling tasks and obtain manpower arrangement. The selection criteria of the neural network and the training process are also described in this paper. We demonstrate that our model can make a precise forecast based on the improvement of neural networks. This paper also discusses the challenges in the neural network training process and obtains enlightening results after getting the arrangement plan. Our research shows that neural networks and deep learning strategies have the potential to solve similar problems effectively.

Operation System Competition

I would like to thank my lovely and clever teammates at first: Lian Xinyu, Lou Jiaqi and Zhu Zhongbo.

In ECE 391, we design a operation system (HarmoniOS) from a frame with almost nothing. The process can be divided into five steps: 1. Design Interrupt Descriptor Table， Real Time Clock (RTC) and keyboard response. 2. Design driver components, including terminal, file system and RTC virtualization. 3. Implement 11 system calls (including execute, halt, etc.). 4. Increase the variety of system calls. 5. Complete task scheduling structure (we utilize robin algorithm). After finishing the above five basic tasks, we also take part in a competition for extra credits, which means we can design more interesting functions for our operation system (demo).

Finally, our OS can support the following functions:

HarmoniOS FUNCTION LIST

BASIC FUNCTION

(a) Support basic interrupt (keyboard, rtc, pit, etc.).

(b) Support read-only file system.

(c) Support basic system call.

(d) Support three terminals at most and process switch function.

VGA RELATED

(a) Allow to switch VGA mode 16 color and VGA mode 32 color.

(b) Provide background picture for desktop.

(c) Support animation of system entry.

(d) Support using mouse (by clicking left button) and keyboard (by pressing ALT+F1/F2/F3/F4) to switch different terminals and desktop.

(e) Support status bar display.

(f) Support cursor display.

DEVICE RELATED

(a) Support Peripheral Component Interconnect (PCI) bus.

(b) Support a speaker.

(c) Support mouse.

(d) Support receiving the system time from CMOS and print it on the status bar.

COMMAND RELATED

(a) Support auto-complete function for command (by pressing tab).

(b) Support history information buffer, whose maximum limit is 135 (by using arrow up and arrow down).

SIGNAL RELATED

(a) Provide a common API for idt content.

(b) Support five different signal mechanism.

(c) Enable system call: set signal handler and signal return.

SYSTEM CALL RELATED

(a) Support music play function (beep).

(b) Support random number generation, whose form is a guessing game (random).

(c) Support checking the status of running process.