Why You Should Never Manually Compensate Your Data

Numbers are all around us.  My personal favorite is ≅1.618 aka ɸ aka ‘the golden ratio’.  It’s found throughout history, where it has influenced architects and artists. We see it in nature, in plants, and it is used in movies to frame shots. It can be approximated by the Fibonacci sequence (another math favorite of mine). However, I have not worked out how to apply this to flow cytometry.  That doesn’t mean numbers aren’t important in flow cytometry. They are central to everything we do, and in this blog, I’m going to flit around numbers-based questions that I have received…

Developing variant calling and analysis pipelines for NGS sequenced data have become a norm in clinical labs. These pipelines include a strategic integration of several tools and techniques to identify molecular and structural variants. That eventually helps in the apt variant annotation and interpretation. This blog will delve into the concepts and intricacies of developing a “variant calling” pipeline using GATK. “Variant calling” can also be performed using tools other than GATK, such as FREEBAYES and SAMTOOLS.  In this blog, I will walk you through variant calling methods on Illumina germline RNASeq data. In the steps, wherever required, I will…

Clinical trials are studies designed to test the novel methods of diagnosing and treating health conditions – by observing the outcomes of human subjects under experimental conditions.  These are interventional studies that are performed under stringent clinical laboratory settings. Contrariwise, non-interventional studies are performed outside the clinical trial settings that provide researchers an opportunity to monitor the effect of drugs in real-life situations. Non-interventional trials are also termed observational studies as they include post-marketing surveillance studies (PMS) and post-authorization safety studies (PASS). Clinical trials are preferred for testing newly developed drugs since interventional studies are conducted in a highly monitored…

In the first blog of this series, we explored the power of sequencing the genome at various levels. We also dealt with how the characterization of the RNA expression levels helps us to understand the changes at the genome level. These changes impact the downstream expression of the target genes. In this blog, we will explore how NGS sequencing can help us comprehend DNA modification that affect the expression pattern of the given genes (epigenetic profiling) as well as characterizing the DNA-protein interactions that allow for the identification of genes that may be regulated by a given protein.  DNA Methylation Profiling…

Why is Next Generation Sequencing so powerful to explore and answer both clinical and research questions. With the ability to sequence whole genomes, identifying novel changes between individuals, to exploring what RNA sequences are being expressed, or to examine DNA modifications and protein-DNA interactions occurring that can help researchers better understand the complex regulation of transcription. This, in turn, allows them to characterize changes during different disease states, which can suggest a way to treat said disease.  Over the next two blogs, I will highlight these different methods along with illustrating how these can help clinical diagnostics as well as…

NGS methodologies have been used to produce high-throughput sequence data. These data with appropriate computational analyses facilitate variant identification and prove to be extremely valuable in pharmaceutical industries and clinical practice for developing drug molecules inhibiting disease progression. Thus, by providing a comprehensive profile of an individual’s variome — particularly that of clinical relevance consisting of pathogenic variants — NGS helps in determining new disease genes. The information thus obtained on genetic variations and the target disease genes can be used by the Pharma companies to develop drugs impeding these variants and their disease-causing effect. However simple this may allude…

As scientists, we need to perform image analysis after we’ve acquired images in the microscope, otherwise, we have just a pretty picture and not data. The vocabulary for image processing and analysis can be a little intimidating to those new to the field. Therefore, in this blog, I’m going to break down 7 terms that are key when post-processing of images. 1. RGB Image Images acquired during microscopy can be grouped into two main categories. Either monochrome (that can be multichannel) or “RGB.” RGB stands for red, green, blue – the primary colors of light. The cameras in our phones…

In the flow cytometry community, SPADE (Spanning-tree Progression Analysis of Density-normalized Events) is a favored algorithm for dealing with highly multidimensional or otherwise complex datasets. Like tSNE, SPADE extracts information across events in your data unsupervised and presents the result in a unique visual format. Given the growing popularity of this kind of algorithm for dealing with complex datasets, we decided to test the SPADE algorithm in 5 software packages, including Cytobank, FCS Express, FlowJo, R, and the original, free software made available by the author of SPADE. Which was the fastest?

FlowJo is a powerful tool for performing and analyzing flow cytometry experiments, if you know how to use it to the fullest. This includes understanding embedding and using keywords, the FlowJo compensation wizard, spillover spreading matrix, FlowJo and R, and creating tables in FlowJo. Extending your use of FJ using these hacks will help organize your data, improve analysis and make your exported data easier to understand and explain to others. Take a few moments and explore all you can do with FJ beyond just gating populations.