This repository saves some examples used in the Short Course on Deep Learning in Omics, by Wei Sun and Nancy Zhang.

see folder somatic_mutations/msk_impact

see folder scRNAseq. There are two datasets:

• Allen_BI: single nucleus RNA-seq data of human brains, generated by SMART-seq2 protocol

• Zheng_2017: scRNA-seq data of human blood, generated by 10x droplet-based system. Cells of known cell types were mixed.

• gene_annotation: gene annotation data and summary.

Examples showing hyper-parameter tuning when studying the association between HLA and TCR

Node/neuron: a computational unit in a neural network that has one or more weighted input connections and an activation function that combines the inputs and provide an output.

Weights: when input enters the neuron, it is multiplied by a weight.

Embedding: low dimensional representation of a high dimensional input

Activation function: the output of a neuron is f(Wh + b), where h is input, W is the weight, b is bias, and f is the activation function.

Perceptron: a perceptron is a simple linear binary classifier. For example, y = 1 if WX + b > 0 and y = 0 otherwise.

Feedforward neural network: a neuron network where information goes forward.

MLP (Multi Layer perceptron): in a narrow sense, it refers to multiple layers of perceptrons, but it often refers to any feedforward neural networks.