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After pre-processing, the data were filtered to remove unmapped tags, or tags that mapped to pseudogenes, or known ribosomal/mithocondrial genes (list available in file `data/genes_mitrib.txt.gz` or via function `getMITRIB()` provided in the package).
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Moreover, counts from transcripts that mapped to a same gene were averaged.
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Sample phenotypic annotations were also processed to define glycemic groups based on *hba1c* measurements, as per the original publication [@Fadista2014].
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```{r, eval = !local_data}
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# download GEO dataset from Github
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eset <- readRDS('http://shenorrlab.github.io/BSeq-sc/data/GSE50244.rds')
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eset <- readRDS('https://shenorrlab.github.io/bseqsc/data/GSE50244.rds')
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```
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```{r, eset}
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# for this analysis we only look at samples with hba1c data
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### Single cell data
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We generated single cell RNA-seq data of pancreatic islets from 3 healthy individuals, which provided gene expression profiles for 17434 genes in 7729 cells.
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The raw counts for these data are available on the [data download
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page](http://shenorrlab.github.io/BSeq-sc/data), in the form of an
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