Bioinformatics

If you ask me: what’s your favorite machine learning algorithm? I would answer logistic regression (with regularization: Lasso, Ridge and Elastic) followed by random forest. In fact, that’s how we try those methods in order. Deep learning can perform well for tabular data with complicated architecture while random forest or boost tree based method usually work well out of the box. Regression and random forest are more interpretable too.

Sign up for my newsletter to not miss a post like this https://divingintogeneticsandgenomics.ck.page/newsletter The Cancer Genome Atlas (TCGA) project is probably one of the most well-known large-scale cancer sequencing project. It sequenced ~10,000 treatment-naive tumors across 33 cancer types. Different data including whole-exome, whole-genome, copy-number (SNP array), bulk RNAseq, protein expression (Reverse-Phase Protein Array), DNA methylation are available. TCGA is a very successful large sequencing project. I highly recommend learning from the organization of it.

The T-cell receptor (TCR) is a special molecule found on the surface of a type of immune cell called a T-cell. Think of T-cells like soldiers in your body’s defense system that help identify and attack foreign invaders like viruses and bacteria. The TCR is like a sensor or antenna that allows T-cells to recognize specific targets, kind of like how a key fits into a lock. When the TCR encounters a target it recognizes, it sends signals to the T-cell telling it to attack and destroy the invader.

What is pseduobulk? Many of you have heard about bulk-RNAseq data. What is pseduobulk? Single-cell RNAseq can profile the gene expression at single-cell resolution. For differential expression, psedobulk seems to perform really well(see paper muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data). To create a pseudobulk, one can artificially add up the counts for cells from the same cell type of the same sample. In this blog post, I’ll guide you through the art of creating pseudobulk data from scRNA-seq experiments.

Disclaimer: This post is sponsored by BigOmics platform. I have personally tested the platform. The opinions and views expressed in this post are solely those of the author and do not represent the views of my employer. A brief description of the platform. What challenges could the platform solve? The BigOmics platform - Omics Playground- provides a simplified approach for the effective processing of bulk RNA-seq data and proteomics data, resolving many issues experienced by scientists in the field.

In the mysterious world of DNA, where the secrets of life are encoded, scientists are harnessing the power of cutting-edge technology to decipher the language of genes. One of the remarkable tools they’re using is the 1D Convolutionary Neural Network, or 1D CNN, which might sound like jargon from a sci-fi movie, but it’s actually a game-changer in DNA sequence analysis. Imagine DNA as a long, intricate string of letters, like a never-ending alphabet book.

Multi-omics data analysis is a cutting-edge approach in biology that involves studying and integrating information from multiple biological “omics” sources. These omics sources include genomics (genes and their variations), transcriptomics (gene expression and RNA data), proteomics (proteins and their interactions), metabolomics (small molecules and metabolites), epigenomics (epigenetic modifications), and more. By analyzing data from various omics levels together, we can gain a more comprehensive and detailed understanding of biological systems and their complexities.

Spatial transcriptome cellular niche analysis using 10x xenium data go to https://www.10xgenomics.com/resources/datasets There is a lung cancer and a breast cancer dataset. Let’s work on the lung cancer one. https://www.10xgenomics.com/resources/datasets/xenium-human-lung-preview-data-1-standard 37G zipped file! wget https://s3-us-west-2.amazonaws.com/10x.files/samples/xenium/1.3.0/Xenium_Preview_Human_Lung_Cancer_With_Add_on_2_FFPE/Xenium_Preview_Human_Lung_Cancer_With_Add_on_2_FFPE_outs.zip unzip Xenium_Preview_Human_Lung_Cancer_With_Add_on_2_FFPE_outs.zip sudo tar xvzf cell_feature_matrix.tar.gz cell_feature_matrix/ cell_feature_matrix/barcodes.tsv.gz cell_feature_matrix/features.tsv.gz cell_feature_matrix/matrix.mtx.gz read in the data with Seurat We really only care about the cell by gene count matrix which is inside the cell_feature_matrix folder, and the cell location x,y coordinates: cells.

Introductioon In scRNA-seq data analysis, one of the most crucial and demanding tasks is determining the optimal resolution and cluster number. Achieving an appropriate balance between over-clustering and under-clustering is often intricate, as it directly impacts the identification of distinct cell populations and biological insights. The clustering algorithms have many parameters to tune and it can generate more clusters if e.g., you increase the resolution parameter. However, whether the newly generated clusters are meaningful or not is a question.

Disclaimer: This post is sponsored by Watershed Omics Bench platform. I have personally tested the platform. The opinions and views expressed in this post are solely those of the author and do not represent the views of my employer Variant calling is the process of identifying and categorizing genetic variants in sequencing data. It is a critical step in the analysis of whole-genome sequencing (WGS) and whole-exome sequencing (WES) data, as it allows researchers to identify potential disease-causing mutations.