urszulaczerwinska / deconica Goto Github PK

save sample names, it will be important for t-test

It would be necessary to introduce a way to make the enrichment unsensible to the gene threshold
by scanning many thresholds and selecting the most voted result among all thresholds

It would be good to find faster way for enrichment (figure out what is long in Paul's function and rewrite Paul's function)

correlation with LM22 indicates other candidates

write clean function and place in pipeline for FEV

test on Windows and on Unix

implement t.test on A matrix selected samples

I repeated the pipeline on TCGA I downloaded and transformed counts in log2 and results are really not interpretable by enrichment

Sometimes we get different number of immune component candidates (stroma ones don’t always pass the threshold) - possibility: not take them into account for deconvolution

Instead of using enrichment-based test we can use functional interpretation to interpret components

Some errors have slipped into the tutorial, making some commands not usable with a simple Copy-Pasting.
Here is a list of some of which I have found, assigned by categories:

Issue 1:
No folder "data-raw" can be found in deconICA package from R. It is present in the GitHub repository but during the installation in R, the folder is not present.
Some commands to not work, such as:

GE_SDY420 <- read.delim("./data-raw/xCell_ImmPort/GE_SDY420.txt", row.names=1, stringsAsFactors=FALSE)

FACS_SDY420 <- read.delim("../data-raw/xCell_ImmPort/FCS_SDY420.txt", row.names=1, stringsAsFactors=FALSE)

TIMER <- ACSNMineR::format_from_gmt("./data-raw/TIMER_cellTypes.gmt")

data(BEK_ica_overdecompose)

Issue 2:
When assigning metagenes, the whole matrix should be given as input without the "$r".
This is only found in the html version of the tutorial (not the Rmd one).
Here are the cases where this happens:

mix1.assign.20 <- assign_metagenes(mix1_corr.basis.20$r, exclude_name = NULL)

GE_SDY420_ica_39.LM22.reciprocal.corr <- assign_metagenes(GE_SDY420_ica_39.corr.LM22$r, exclude_name = NULL)

reciprocal.corr <- assign_metagenes(corr$r, exclude_name = NULL)

reciprocal.corr.Biton <-assign_metagenes(corr_Biton$r, exclude_name = c("M8_IMMUNE", "M2_GC_CONTENT"))

Issue 3:
When using "kable", no import has been stated prior to the usage, creating an error.
library(kableExtra) should be mentioned at some point in the tutorial.

Issue 4:
When generating html tables with "kable", the command to visualise the table in Rstudio should be added as some cases lack it. For instance, the line

kable(GE_SDY420_ica_39.LM22.reciprocal.corr, "html", row.names = FALSE)

should become

kable(GE_SDY420_ica_39.LM22.reciprocal.corr, "html", row.names = FALSE) %>% kable_styling(font_size = 8)

Issue 5:
Some typos here and there are hidden. Here would be the needed correction imo:

cell_prop <- pData(GSE64385)[ , c(1, 2, 10, 11,12, 13, 14, 15, 16, 17)]

overdecompose parameter that selects number of composed components? needed to perform overdecomposition of the input matrix

An efficient way to in interpret a component is to use correlation with some known profile or as we call it a

type (a metagene), we can then correlate them with obtained components and verify if some of decomposed

They can be considered as a negative control.

#without endothelial cells and Fibroblasts

The idea is to add fastGSEA as an alternative to fisher test

It will be necessary in the final version of the plugin to have the lolypop/radar/rcorr plot the labels for identified components

R check is failing on unit test that works fine from the unit module, it is not seeing data in data/.rda

missing unit tests :

• get_enrichment
• helpes
  *cell_voting_immgen

missing examples :

• assign_metagenes
• identify_immune_ic
• get_enrichment
• cell_voting_immgne