Isolated immune cell reconstruction evaluation #5
Conversation
| gs.file <- file.path("data", "expression_data", | ||
| "E-MTAB-2452_hugene11st_SCANfast_with_GeneSymbol.pcl") | ||
| exprs.df <-readr::read_tsv(gs.file) | ||
| exprs.mat <- as.matrix(exprs.df[, 3:ncol(exprs.df)]) |
There was a problem hiding this comment.
why starting at column 3? Maybe add comment about what first 2 columns are
| height = 11, width = 8.5) | ||
| ``` | ||
|
|
||
| ### E-MTAB-2452 Boxplots |
There was a problem hiding this comment.
The x axis tick labels seem to bleed a bit into one another. Is it possible to rename them? For example, when their is an n = in the label, I tend to put on a new line
| ggplot2::ggsave(plot.file, plot = ggplot2::last_plot()) | ||
| ``` | ||
|
|
||
| ## Summary |
There was a problem hiding this comment.
Are all datasets trained together? or are different models trained on each individually? Or, are the models trained using a single dataset and the other datasets are transformed into this space?
There was a problem hiding this comment.
Or, are the models trained using a single dataset and the other datasets are transformed into this space?
This one -- a single PLIER model is trained on the recount2 dataset, which includes SRP045500. E-MTAB-2452 is transformed into the recount2 PLIER space.
I'm working with LVs from this recount2 PLIER model exclusively, but in some cases I'm using only LVs that are significantly associated with a pathway or only those LVs that are not associated with a pathway.
small doc change, relabel boxplot x axis ticks
| dplyr::mutate(MASE = as.numeric(as.character(MASE)), | ||
| `Spearman correlation` = | ||
| as.numeric(as.character(`Spearman correlation`))) | ||
|
|
There was a problem hiding this comment.
Not quite understand why MASE need to do as.numeric(as.character(MASE)) conversion.
There was a problem hiding this comment.
When binding together the columns, MASE and the Spearman correlation end up as factors.
| ggplot2::theme_bw() + | ||
| ggplot2::scale_fill_manual(values = c("white", "gray50", "black")) + | ||
| ggplot2::ggtitle(paste("All, n =", ncol(z.matrix))) | ||
| ``` |
There was a problem hiding this comment.
In the plot, recount2 means reconstructing the gene expression of recount samples based on recount PLIER model ?
There was a problem hiding this comment.
Yes, that is correct.
| "E-MTAB-2452_reconstruction_error_recount2_model.pdf") | ||
| ggplot2::ggsave(plot.file, plot = ggplot2::last_plot()) | ||
| ``` | ||
|
|
There was a problem hiding this comment.
Maybe, add statistics to show the distributions is significant different (t-test or ANNOVA) ? One benefit is it can provide a quantitative way to support the conclusion (e.g. the pre- and post-reconstruction correlation values are much more similar between two datasets), but it is depends on you since the difference is clear from the plot.
There was a problem hiding this comment.
pairwise.t.test coming up in the next commit
This PR adds
04-isolated_immune_cell_reconstruction.In this notebook, I evaluate the reconstruction of the sorted leukocyte microarray dataset introduced in
03-isolated_cell_type_populations(E-MTAB-2452) as compared to a sorted leukocyte RNA-seq dataset that is included in the recount2 dataset (SRP045500), and, therefore, the training set for the PLIER model under consideration.During #3, the idea of using only high-weight genes for reconstruction came up (see #4). I've chosen not explore this at this time because my goal was/is to test how the subset of latent variables used for reconstruction (all vs. only pathway-associated vs. only thoses LVs that are not significantly associated with any gene sets -- I assume these capture variation from technical factors), rather than to improve the reconstruction performance. I think exploration of improving reconstruction performance would probably require a deeper dive than makes sense for this particular project. Please let me know what you think.
Here's the notebook HTML file for easy viewing:
04-isolated_immune_cell_reconstruction.nb.html.zip
I've made a few changes upstream of this notebook in
02-recount2_PLIER_explorationto save the recount2 reconstructed expression data and associated evaluation metrics, as well.