RNA-Seq Core Concepts Challenge

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Brief true/false statements on Bulk RNA-Seq

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Ahmed Mokhtar

Egypt

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RNA-Seq Core Concepts Challenge


RNA-Seq Core Concepts ChallengeOnline version Brief true/false statements on Bulk RNA-Seq by Ahmed Mokhtar 1 The Benjamini-Hochberg procedure guarantees FDR control under any dependence structure among genes, with no conservativeness. Yes No 2 RPKM/FPKM is the preferred method for between-sample differential expression analysis. Yes No 3 Batch effects cannot be confounded with treatment if randomization is used. Yes No 4 If batches are unbalanced, batch effects can be safely ignored during experimental design without including batch terms in the model. Yes No 5 Independent filtering reduces power by removing genes with low mean expression. Yes No 6 LFC shrinkage is rarely used and has no impact on improving the ranking of genes for downstream analysis. Yes No 7 Bulk RNA-Seq pools RNA from thousands to millions of cells. Yes No 8 Poisson model fully accounts for overdispersion in RNA-Seq counts. Yes No 9 Ribo-depletion preserves full-length transcripts and non-polyadenylated RNA. Yes No 10 Transcriptomics is the large-scale study of all RNA molecules produced by a cell, tissue, or organism under defined conditions. Yes No 11 The Poisson model describes count data well for technical replicates but overdispersion is observed with biological replicates. Yes No 12 DESeq2 uses raw counts as input for analysis. Yes No 13 Library size normalization corrects composition bias across samples. Yes No 14 DESeq2, EdgeR, and Limma-voom expect pre-normalized values (like TPM) as input. Yes No 15 RNA-Seq counts are non-negative integers. Yes No 16 Log2 fold change is used to measure effect size. Yes No 17 Bulk RNA-Seq yields population-averaged expression values by pooling RNA from thousands to millions of cells. Yes No 18 The Negative Binomial distribution is used to model RNA-Seq counts because it accounts for gene-specific dispersion parameterization. Yes No 19 The RNA Integrity Number (RIN) is a standard quality metric and values ≥ 7 are generally required for bulk RNA-Seq. Yes No 20 RPKM/FPKM or TPM should be used to compare counts between samples for differential expression analysis. Yes No

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RNA-Seq Core Concepts Challenge

Yes or No

About this activity

Created by

Download the paper version to play

Top Games

Yes or No

Grammys for best song/record

Yes or No

Is it true?

Yes or No

Is it created with Artificial Intelligence? - Complete game

Yes or No

Foot and Ankle Anatomy Quiz

Yes or No

Adjective Checker

RNA-Seq Core Concepts ChallengeOnline version

by Ahmed Mokhtar

The Benjamini-Hochberg procedure guarantees FDR control under any dependence structure among genes, with no conservativeness.

RPKM/FPKM is the preferred method for between-sample differential expression analysis.

Batch effects cannot be confounded with treatment if randomization is used.

If batches are unbalanced, batch effects can be safely ignored during experimental design without including batch terms in the model.

Independent filtering reduces power by removing genes with low mean expression.

LFC shrinkage is rarely used and has no impact on improving the ranking of genes for downstream analysis.

Bulk RNA-Seq pools RNA from thousands to millions of cells.

Poisson model fully accounts for overdispersion in RNA-Seq counts.

Ribo-depletion preserves full-length transcripts and non-polyadenylated RNA.

Transcriptomics is the large-scale study of all RNA molecules produced by a cell, tissue, or organism under defined conditions.

The Poisson model describes count data well for technical replicates but overdispersion is observed with biological replicates.

DESeq2 uses raw counts as input for analysis.

Library size normalization corrects composition bias across samples.

DESeq2, EdgeR, and Limma-voom expect pre-normalized values (like TPM) as input.

RNA-Seq counts are non-negative integers.

Log2 fold change is used to measure effect size.

Bulk RNA-Seq yields population-averaged expression values by pooling RNA from thousands to millions of cells.

The Negative Binomial distribution is used to model RNA-Seq counts because it accounts for gene-specific dispersion parameterization.

The RNA Integrity Number (RIN) is a standard quality metric and values ≥ 7 are generally required for bulk RNA-Seq.

RPKM/FPKM or TPM should be used to compare counts between samples for differential expression analysis.