fix bullet

Author: nkandhari

02-02-ExperimentFactors.Rmd

@@ -66,12 +66,19 @@ Technical replicates help assess the consistency of the sequencing process and r
 Sample pooling in RNA-Seq is used by experimental biologists to reduce sequencing costs, particularly when RNA input is low. While pooling offers practical benefits, it also introduces pitfalls that can negatively affect data quality and the conclusions drawn from the experiment. Some of the key challenges include [(Rajkumar, A.P. et al, Experimental validation of methods for differential gene expression analysis and sample pooling in RNA-seq. 2015)](https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-015-1767-y):
 
 If not done carefully, pooling can lead to several issues:
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 * Samples with varying RNA quality, quantities, or levels of degradation may contribute unevenly to the pool, complicating normalization and affecting downstream analysis.
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 * Unequal amounts of RNA pooled from each sample can result in disproportionate representation, potentially skewing the data.
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 * An outlier among pooled samples can skew the average expression values and introduce bias. 
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 * If different pools of samples are processed, prepared, or sequenced in separate batches, this can lead to batch effects. It is impossible to correct for batch effect if the barcodes are absent or corrupted or poorly designed.
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 * Pooling collapses multiple biological samples into one losing the ability to estimate biological variability.
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 * Pooling averages out differences between samples, which can mask important biological signals. It can lead to loss of biologically meaningful heterogeneity.
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 * Pooling decreases the statistical power and ability to estimate within population variation.