Raw proteomic profile

Example file

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Proteins of interest for missing value inference

Examples:
1. FDA-approved drug targets (n=812)
2. Immune cell markers from NUWAp26 (n=631,a proteomic signature matrix we developed for 26 immune cell types)
3. Immune cell markers from BCIC, LM22 and LM6 (n=1,114)

Quantification method

TMT/iTRAQ ratio

Label-free intensity

Log base

No transformation

Log2

This web server provides online analysis for NUWA-ms, a network-based method for abundance inference of missing proteins in mass spectrometry based proteomic profiles, leveraging information borrowed from the collected cohort proteomic profiles.

Note: To serve more users and avoid a long waiting time, the server has a limit on the number of query proteins and samples for one query (1,200 proteins at most in no more than 12 samples). However, we provide the R package on GitHub for users needing large-scale local analysis without the number limit and more custom functions.

Analysis results

Inferred matrix

The output expression matrix with inferred quantification for missing values.

Download imputed matrix file

Marker-level recall

Scatter plot showing the associations between marker level recall and correlation coefficients for all input samples. Dotted lines indicate a recall of 0.8 (80th percentile).

Sample-level recall

When more than 10 samples provided, Density plot will be generated showing the distributions of correlation coefficients within the same samples or between different samples. Accuracy rate (AR), representing the overall accuracy in the dataset, and the number of comparisons is indicated.

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NUWA-ms is a network-based algorithm for abundance inference of missing proteins (e.g. cell markers, drug targets) in mass spectrometry-based proteomic profiles, by leveraging information borrowed from the cohort profiles. NUWA-ms exhibits superior performance with non-random and precise inference among multi-metric evaluation, and could lead to improved performance of downstream analyses using proteomic profiles, including deconvolution of immune cell composition, and differential expression analysis, etc.

The default underlying cohort profiles are CPTAC proteomic datasets of six cancer types ( breast cancer, clear cell renal cell carcinoma, colon adenocarcinoma, endometrial carcinoma, gastric cancer, lung adenocarcinoma), which could be replaced by users (e.g., using multiple datasets for a specific cancer type), in the local analysis using NUWA R package we provided on GitHub.

Source codes

A command-line version of NUWA pipeline is available as an R package on GitHub for large scale local analysis.

License

NUWA pipeline and this web server is free for academic users of non-commercial research. Commercial use of NUWA requires a license (contact Dr. Jianmin Wu for details). If NUWA was used in your analysis, a citation of our platform will be appreciated.

Metaphor of the package name NUWA

In Chinese mythology, Nuwa is considered to be the first being and has a famous story for saving humanity by mending a hole in the sky.

Developers


Lihua Cao ()		Algorithm design and R package development
Yuhao Xie ()		Algorithm design and R package development
Yang Du ()		Web server development
Jianmin Wu ()		Algorithm design and Project leader