R2platform Data Science Annals

  Quite frequently, you may want to provide a quick and easy to grasp plot that shows the expression of a couple of genes, segregated over a few groups. With single cell mRNA data, this also needs to take into account the fact that a gene if frequently not detected in all of the cells. Enter the bubble plot. This visualization will show the data much like a heatmap, where every sub-group is represented by a proportional circle. The dcolor here then reflects the average intensity value, while the circle size denotes a percentage of the cells that show expression. Within R2, we have also got this type of viaualization implemented, making it easy to use such insightful graphs with ease. How the circles are scaled can also be defined in different ways. You can look at this from the perspective of the total data set size, and represent the data as a percentage of all the cells. Alternatively, you can also represent the data as a percentage of the cells represented within a group. This l...

 The R2 platform team tries its best to reach a diverse scientific audience via an array of channels. For one, we maintain this blog, with occasional stories and notifications on what keeps us busy ;) We also use social media applaications such as X (formerly known as Twitter), Facebook, Instagram, Threads, LinkedIn, where we keep our users and others interested up to date on new developments and the latest data sets.  Another channel by which we provide tutorials and documentation is via the readthedocs sites, where we maintain our storytelling manual that spans more than 260 pages. However all of these are text based, which is great in many cases, but does requires persistence from the reader. So in order to reach even more users, we have also created a YouTube channel ( https://www.youtube.com/@R2GenomicsPlatform/ ). On this channel, we provide video demonstrations of common functions within the R2 platform, where we take you through every step to obtain a result. There are...

 When you are performing a differential expression analysis or any other search that can yield many potentially intersting genes, then one often performs a gene ontology search or a gene set enrichment analysis, to identify processes that may be at the root of the result set that was identified. At some point, you may also become interested in a single gene and want to know more about this. This is where the pubsniffer tool, that is embedded in the R2 plaftorm comes in handy.  Pubsniffer is an automated analysis that can identify how many papers are found around a particular gene. It also allows you to immediately launch a pubreminer session that will try to present you with a term frequency analysis, to quickly condense a vast amount of scientific literature into a clickable overview. When used with 2 genes, then pubsniffer will also report sentences from abstracts, where both genes are being mentioned, in an attempt to proide you with concise information to help you identify...

Also 2023, has been quite a successful year for the R2 platform up to now. Citations keep coming in, which are a clear sign to us, that we are still filling a space in the scientific domain, by allowing every researcher to do datamining and datascience analyses and visualizations, without the need for bioinformatics or coding ecpertise.   If there are analyses, ideas or other suggestions on how we could improve the R2 platform, then do not hesitate to let us know via the support address on the R2 website (https://r2.amc.nl) Some recent ctations included in the graph above: 2451 Julson JR  et.al.  (2023). CDK4/6 Inhibition With Lerociclib is a Potential Therapeutic Strategy for the Treatment of Pediatric Sarcomas.  J Pediatr Surg   :. 2450 Chen PD  et.al.  (2023). Decreased B4GALT1 promotes hepatocellular carcinoma cell invasiveness by regulating the laminin-integrin pathway.  Oncogenesis   12 :49. 2449 Ali W  et.al.  (2023). Surviva...

One of the frequently used tools within the versatile R2 genomics analysis and visualization platform is the survival analysis module. Within this option, you are able to perform kaplan meier analyses wtih logrank statistics on categorical group variables in a large number of datasets that are publicly available for the users of the r2 platform. Next to using annotation that is represented for the different data sets, R2 can also assess whether the expression of your gene of interest may be associated with the survival probability. Such gene centric analyses are provided in 2 flavours. 1. the kaplanscan tool, where a dataset is subdivided in a range of 2-groups, where patients are sorted by the expression of a gene of interest. This procedure allows you to find the optimal cutoff with the best kaplan meier separation that can be defined. Next to kaplan scanning, R2 also allows you to use frequently used alternative methods, such as kaplan median and warious other methods. 2. Cox propor...

Imagine you're studying the levels of a particular gene's expression in different samples. Each cell type has its own level of gene expression, and you've collected this data for many samples. Now, you want to visualize this data in a single comprehensive overview. Let's do this using a raincloud plot. Dots or Raindrops: On the raincloud plot, you'll have a bunch of small dots, like raindrops, scattered along a horizontal axis. Each dot represents the gene expression level in a single sample. Samples with higher expression levels will have dots higher on the axis, and samples with lower expression levels will have dots lower on the axis. Cloud-like Shapes: The raindrops will be surrounded by cloud-like shapes. These shapes show you the distribution of the gene expression levels. If the cloud is wide and covers a large area along the axis, it means the gene expression levels are spread out across a wide range of values. If the cloud is narrow and tall, it indicates t...

The R2 Genomics Analysis and Visualization Platform ( https://r2.amc.nl ) has been under constant development since 2005, when our adventure to let biologists be in the driving seat of their analyses, began. We could never have imagined back then that the R2 platform would remain a valuable and viable resource for more than 17 years. By itself, this has been an achievement of which we are very proud. This has only been possible because of the many researchers that have embraced us over the years and kept using our platform.  Nearly all of the tools and options, that are available in R2 Today, have been suggested by / discussed with users of the platform. So in a sense this has truly been a community effort and an achievement of us all. One request, that we have posponed for quite a while has been the implementation of a more modern graph engine to interact with the plethora of plots that are being made available in the platform. We looked into many of the packages that are frequent...