Immugent is going to interpret a special kind of article about student letters today. Let's call it "student letter summary". Because I am a talker, I don't know what to call it. I won't rack my brains to think of this name in the future.
This kind of "life review" article has a history of many years, mainly focusing on all kinds of popular major technologies (leading scientific research), such as single cell sequencing technology, which has been popular in recent years. So today I will take single cell sequencing as the theme to explain how to use this thinking to publish high-scoring articles, paying attention to all those that didn't cost a penny!
The first article I want to talk about is an article entitled "How to Get Started with Single Cell RNA Sequencing Data Analysis" published in J am SOC nephrol (if:10.12) on 202/0. Well, look at the calendar. This year is 2022, so let's not talk about the timeliness of this article. But the full text is really simple, that is, it introduces the basic process of single cell sequencing data analysis.
Look at its abstract: In the past five years, the single cell method has been able to monitor the gene and protein expression, genetic and epigenetic changes of thousands of single cells in one experiment. With the improvement of measurement methods and the reduction of reaction and sequencing costs, the scale of these data sets is increasing rapidly. The key bottleneck is still the analysis of the rich information produced by single cell experiments. In this review, we give a brief overview of analysis pipelines because they are usually used in this field. Our goal is to get researchers to start single cell analysis to get an overview of the challenges and the most commonly used analysis tools. In addition, we hope to help others understand how typical readings of single-cell data sets are presented in published literature. Well, it is indeed a summary!
Although there are seven drawings in the full text, most of them are the most basic drawings, which I think everyone will know. But what Immugent wants to say here is that although this article is a summary, it is actually much easier to write than the real summary. For example, this article, wait until the next time there is a phenomenon-level technology similar to single cell sequencing, is there really a small partner who has a similar article!
The second article to be talked about next is an article entitled "Benchmark Algorithm for Pathway Activity Transformation of Single Cell RNA-seq Data" published in Computer Structural Biotechnology J (IF: 7.27) in 2020. This kind of article is more technical than the last one, at least it feels like a summary!
Just like this article, it summarizes various algorithms of single cell data scoring, compares the advantages and disadvantages of various algorithms with published data, and finally gives its own opinions. what can I say? It is better than the general overview and better than the pure algorithm development article. So if there are more such algorithms in the near future, should we write an updated version for scientific research?
The third article to be talked about next is an article published in Genome Biol (if:13.58) 2021entitled "Super 1000 tool reveals the trend of single cell RNA-Seq analysis pattern". As a conclusion, this article is really not bragging. An article summarizes 1000+' s tools for analyzing single cell data, and I admire the author.
And the author also developed a website: Paris single cell trajectory influence method. The whole genome data of thousands of single cells were analyzed. At present, there are many algorithms to infer the distribution of these cells along the development trajectory. Based on these results, the author developed a set of guidelines to help users choose the best method for their data sets.
In fact, although more than 70 tools have been developed so far to infer the trajectory of single cells, it is challenging to compare their performance because they need very different input and output models. In this paper, the authors benchmark 45 methods on 1 10 real data set and 229 synthetic data set to understand the cell sorting, topology, scalability and usability. The results show that some existing tools are complementary, and the choice of methods should mainly depend on the dimension of data set and trajectory topology.
Finally, the author also provides free evaluation websites for trajectory analysis of various single-cell data (https://benchmark.dynverse.org), which will help to develop more trajectory analysis tools and explore the increasingly large and complex single-cell data set. I don't comment too much on this article, but I think everyone should read it when they are free. No matter the data processing or the discussion of the results, it is incomparable to the previous articles and is a rare high-quality article.
Nowadays, the development of science and technology is changing with each passing day. 2 1 century what is often lacking in making valuable scientific research achievements is not technology, but sensitivity to hot spots and control of the current situation. Tang and Guo all became world-class scientists by virtue of single cell sequencing technology, because they controlled the current situation.
Similarly, the focus of the above-mentioned "health reviews" is also the hot issue of single cell sequencing technology that needs to be solved urgently at that time, so that a series of high-scoring articles can be published without spending half a cent. Moreover, because they are hot scientific issues, the citation rate of these articles is very high up to now, and it will definitely continue to rise in the future. If single cell sequencing is a commanding height, it is better to say that it is a starting point, because there will be many such technologies in the future. I hope this tweet can bring you some thoughts. Welcome friends who recommend similar articles to contact us through the background.