Prior to statistical analysis of mass spectrometry (MS) data, quality control (QC) of the identified biomolecule peak intensities is imperative for reducing process-based sources of variation and extreme biological outliers. Without this step, statistical results can be biased. Additionally, liquid chromatography–MS proteomics data present inherent challenges due to large amounts of missing data that require special consideration during statistical analysis. While a number of R packages exist to address these challenges individually, there is no single R package that addresses all of them. We present pmartR, an open-source R package, for QC (filtering and normalization), exploratory data analysis (EDA), visualization, and statistical analysis robust to missing data. Example analysis using proteomics data from a mouse study comparing smoke exposure to control demonstrates the core functionality of the package and highlights the capabilities for handling missing data. In particular, using a combined quantitative and qualitative statistical test, 19 proteins whose statistical significance would have been missed by a quantitative test alone were identified. The pmartR package provides a single software tool for QC, EDA, and statistical comparisons of MS data that is robust to missing data and includes numerous visualization capabilities.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The three-component reactions of the 16-electron half-sandwich complex CpCo(S2C2B10H10) (Cp = cyclopentadienyl) (1) with ethyl diazoacetate (EDA) and alkynes R1R2 (R1 = Ph, R2 = H; R1 = CO2Me, R2 = H; R1 = R2 = CO2Me; R1 = Fc, R2 = H) at ambient temperature lead to compounds CpCo(S2C2B10H9)(CH2CO2Et) (CHCO2Et)(R1R2) (2–5), CpCo(S2C2B10H9)(CH2CO2Et)(R2–R1–CHCO2Et) (6–9), CpCo(S2C2B10H9)(CH2CO2Et)(CH(Ph)CCHCO2Et) (10), and CpCo(S2C2B10H9)(CH2CO2Et)(CH(Fc)–CH–CCO2Et) (11). In 2–5, one alkyne is stereoselectively inserted into the Co–B bond, one EDA molecule is used to form a sulfide ylide, and the second EDA molecule is inserted into one Co–S bond to form a three-membered metallacyclic ring. At ambient temperature 2–5 undergo rearrangement to 6–9 through migratory insertion of the inserted EDA. Different from 2–5, in 10 phenylacetylene is inserted into the Co–B bond at the terminal carbon and the terminal carbon is coupled with one EDA to afford a six-membered metallacyclic ring with the CO coordination to metal. In 11, a stable Co–B bond is generated, and one EDA and one ethynylferrocene are inserted into the Co–S bond. Moreover, if weakly basic silica is present, 2–4 can lose an apex BH close to the two carbon atoms of o-carborane to give rise to CpCo(S2C2B9H9)(CH2CO2Et)2(R1R2) (12–14) accompanied by the coordination of the two sulfide ylide units to the metal center. The solid-state structures of 2–4, 6–12, and 14 were characterized by X-ray structural analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This event has been computationally inferred from an event that has been demonstrated in another species.

The inference is based on the homology mapping from PANTHER. Briefly, reactions for which all involved PhysicalEntities (in input, output and catalyst) have a mapped orthologue/paralogue (for complexes at least 75% of components must have a mapping) are inferred to the other species. High level events are also inferred for these events to allow for easier navigation.

More details and caveats of the event inference in Reactome. For details on PANTHER see also: http://www.pantherdb.org/about.jsp

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This is a submission for Challenge #24 by Desights User

Click here for Challenge Details Note: This submission is in REVIEW state and is only accessible by Challenge Reviewers. So you might get errors when you try to download this asset directly from Ocean Market.

Submission Description

The cryptocurrency is not just a new form of value store and exchange, it is a revolution of its own. Beginning with its use to provide peer-to-peer payment network (or digital money) like Bitcoin, today’s cryptocurrency, or crypto for short, have evolved way beyond its humble start. Underlying the crypto world, there lies amazing technology called Blockchain. In simple term, blockchain is a decentralized and shared digital ledger that records transactions transparently and immutably across nodes in the network. Today’s Crypto community has slowly turned into industry of its own introducing a whole spectrum of enigmatic pattern, trends, and economic framework. In this report we will explore the trend, correlations, and dynamics related to 20 selected Crypto projects to derived insights and build models that predict the future of crypto. Key Findings: Our exploratory data analysis (EDA) underlines the span and general pattern of the Google Trend and Price related data. The data being analyzed span from the earliest entry on 2014-09-17 up to the latest on 2024-04-07. Time series decomposition was performed to extract trend, seasonal cycle, and residuals that made up the Google Interest Trend data. Analysis on the time-series decomposition help us distinguish cluster (a) with projects on the rise such as Solana, SingularityNet, Fetch.ai, and Ocean Protocol; and cluster (b) containing old project such as Dogecoin, Litecoin Filecoin, Tezos that are facing stagnant/downfall trend. Based on the Google Trends’s Correlation across projects we characterize Highly correlated projects cluster with correlation of about >0.8, and up to 0.92 with Bitcoin-Ethereum-Chainlink-Litecoin-Monero as the prominent group members. By introducing additional Google Trend data to understand Crypto Narrative, we worked toward building interpretable Event/Entity driving the market sentiment to explain our decomposed Time-series data. Based on Lag Characteristics in Correlation of Google Trend and Price/Trade Volume we highlight the tendency for the correlation to accumulate at longer lag time. Using NeuralProphet Framework we build forecasting models for Google Trend and Token Price for all 20 projects investigated here. We deployed these models to predict Trend and Price for all 20 projects for the following 52 weeks (up until April 2025). The developed models performed extraordinarily well with the R^2 value for most fall between the range of 0.75-0.88, while the highest goes up to 0.919. We highlight the correlation between Bitcoin, Ethereum, Ocean, with the rest of other projects. Ocean and Bitcoin, also Ethereum and Solana are the most correlated, both with correlation value of 0.89. The Kucoin’s KCS token is the least correlated with both Ocean and Bitcoin (0.31), while with Ethereum, Filecoin have the least correlation (0.41).

Conclusion This investigative study presents a thorough data analysis and exploration of correlations, time-lag characteristics, and time-series decomposition concerning Google Trends and token prices for 20 selected crypto/blockchain projects. By decomposing the time-series data, we have identified several clusters of crypto projects that is moving up in popularity such as Fetch.ai, SingularityNet, Solana, Ocean and some others that are stuck or in downfall trend, such as Dogecoin and Litecoin. Our analysis also includes a detailed exploration of various factors that contribute to understanding the data better, such as the incorporation of event-driven trends that explain outlier spikes in the residual data from our decomposed time-series.

In addition to our in-depth analysis, we build strong mini-library of forecasting models for predicting the Google Trend as well as price for the upcoming year with R^2 score that goes as high as 0.88 for most cases. Moreover, in order to demonstrate the utility of our exploratory data analysis tools and pipeline in full we also include all the results and analysis output produced in this work.

Looking ahead, we plan to expand our developed forecasting models and the presented data into a "CryptoForecast MiniApp." This application, based on the Streamlit package, will be hosted on a decentralized cloud (Akash) and connected to the Ocean marketplace and Predictoor, enhancing accessibility and utility for users interested in real-time data for Google Trends and Crypto Token Price forecasts.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.