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iSeqQC- An Expression based Quality Control tool


Welcome to iSeqQC

It is an expression-based quality control tool to detect outliers either produced by batch effects or merely due to dissimilarity within a phenotypic group.


Input: 1) Choose the file-type between raw or normalized counts options;

2) Choose the organism of study;

3) Choose between gene symbol or gene id as in your first column of counts file

4) Sample phenotype file in tab-delimited with a minimum of 4 columns, 1) sample names; 2) short name of samples; 3) groups; 4) samples to include in the analysis; 5) any factors that could influence any bias such as library protocol or preparation method. The first 4 columns are mandatory and should strictly match the names and order as mentioned below (names case-sensitive), column 5-11 are optional and could be left blank


5) A counts matrix file in tab-delimited file. This could be a raw or normalized (TPM/FPKM/log2/RPKM/RPM etc.). This file should have gene identifier (either gene symbol or gene id's) in first column as shown below




or




Output: It is presented in two tabs, where in ‘Quality Control’, you are required to input the above-mentioned information and obtain output is in form of an expression summary table and several QC plots such as density plots, PCA, hierchichal clustering, correlation plots. In ‘Expression Plots’ tab, you can choose your gene of interest to plot expression profile among different phenotypic groups.


Please note: To remove any sample from analysis, simply replace 'TRUE' with 'FALSE' in 'include' column in sample manifest file.


Additional information can be obtained at https://github.com/gkumar09/iSeqQC


Citation:

Kumar G, Ertel A, Feldman G, Kupper J, Fortina P (2020). iSeqQC: A Tool for Expression-Based Quality Control in RNA Sequencing. BMC Bioinformatics. Feb 13;21(1):56. doi: 10.1186/s12859-020-3399-8. PMID: 32054449; PMCID: PMC7020508


Contact:

Gaurav Kumar, PhD

Email address: gaurav.kumar@jefferson.edu



Please remember to use correct column names and matched sample names to avoid any errors
Please remember to have official gene symbols or IDs to avoid any errors
Example: Select gene_id- if your file first column looks like ENSG00000111640; Select gene_symbol- if your file first column looks like GAPDH


Sample phenotype file: Download sample phenotype file



Sample phenotype sheet with multi factors: Download sample phenotype file with multifactor columns



Raw counts file with gene IDs: Download raw counts file with gene IDs



Raw counts file with gene symbols: Download raw counts file with gene symbol