If some samples fail quality inspection after submitting for regular transcriptome sequencing, can these samples be re-sent? Can they still be analyzed together with the previously qualified samples?

In standard transcriptome sequencing, if some samples fail quality control, it is entirely possible to resubmit these samples individually. The resubmitted samples can still be combined for analysis with the previously qualified samples in terms of technical processes and data analysis. However, attention should be paid to the following points to ensure consistency in data quality:

 

I. Experimental Level Recommendations

1. RNA Extraction and Purification Process for Resubmitted Samples

(1) Ensure consistency with the extraction methods and reagents used in the initial submission (such as TRIzol, column method, etc.) to avoid batch differences between samples.

(2) RNA integrity (RIN value) and purity indicators (A260/280, A260/230) must still meet library construction standards.

2. Library Construction and Sequencing Arrangement

(1) It is recommended to use the same library construction kit and sequencing platform (such as Illumina PE150) for both resubmitted and initially qualified samples;

(2) If the time interval is short, the platform can generally arrange to use the same batch of reagents for library construction and sequencing to reduce batch effect.

 

II. Data Analysis Level Recommendations

1. Feasibility of Combined Analysis

(1) As long as the sequencing platform and strategy are consistent, joint analysis can be conducted as a single batch (such as differential expression analysis using DESeq2, edgeR);

(2) FastQ files from resubmitted samples can be directly included for unified alignment, quantification, and normalization with the original samples.

 

2. If unavoidable library construction or sequencing batch differences exist, batch effect correction strategies should be introduced in the analysis, such as:

(1) Using ComBat (sva package), removeBatchEffect (limma package);

(2) Or explicitly including the 'batch' factor in the differential analysis model.

 

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