covid_norm_df

<p>A dataframe containing normalized LFQ protein intensity data for 230
proteins in 35 samples (a subset of the original data set)</p>

datasets

A comprehensive, user-friendly package for label-free proteomics data analysis and machine learning-based modeling. Data generated from 'MaxQuant' can be easily used to conduct differential expression analysis, build predictive models with top protein candidates, and assess model performance. promor includes a suite of tools for quality control, visualization, missing data imputation (Lazar et. al. (2016) <doi:10.1021/acs.jproteome.5b00981>), differential expression analysis (Ritchie et. al. (2015) <doi:10.1093/nar/gkv007>), and machine learning-based modeling (Kuhn (2008) <doi:10.18637/jss.v028.i05>).

Chathurani Ranathunge

promor

Proteomics Data Analysis and Modeling Tools

covid_norm_df function

<p>A data frame with 230 rows (proteins) and 35 columns (samples)</p>

covid_norm_df: Suvarna et al 2021 LFQ data (normalized)

Description

Usage

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References