almaaslab/csdr

Efficient CSD implementation in R

csdr's Issues

Quick question

Hi, this is a super useful package, so thank you very much. Sorry for the back and forth - answered some of my own questions. I do have one new one: I get the results where you have a higher C score (and low S/D), and those where you have a higher D score (and low S/D) - how would you interpret those results that have both higher C and S scores (but low D scores)?

The input data format for csdR

Hi Developers

I wish to use your package. I have raw counts for two samples (control vs knockout) each with 2 biological replicate (i.e. 4 feature count files). But I am not sure how to give these 4 files to run_csd function. While viewing the data that's provided with the package I realize the counts are not raw counts but instead some non-integer values with signs.

> sick_expression[1:10,1:10]
                 TMEM187       IKZF1      TRPV1     CYP2A6       RYR2
TCGA.DJ.A13L  0.25886910  1.31385680 -0.1271020 -0.6834881 -0.4114891
TCGA.DE.A4M9  0.59208636 -1.47097100  1.2461969  0.4717787  1.2144106
TCGA.J8.A3O1 -0.19256716 -0.72812927  0.4885138 -0.7809972 -0.3792373
TCGA.ET.A39K  1.31385680  0.05221925 -1.0537053 -0.5627220 -0.4006925
TCGA.4C.A93U -3.02590166  0.74770917  0.9013970  0.8291135 -0.6524206
TCGA.ET.A25I  0.59802017  0.61594986 -0.5053870  0.4114891  0.2588691
TCGA.ET.A2MZ  0.08710257 -0.21795197 -0.1925672 -0.4885138 -0.4829205
TCGA.EM.A3AK -0.30022446  0.20778210 -0.7742712 -0.8082633  0.3158622
TCGA.FK.A4UB -1.40127989 -1.22486871 -0.5395762 -1.7073658 -0.1522012
TCGA.BJ.A28X -0.83614405  2.44768351 -0.6340798 -0.2434780  1.7181407
                   CDK12     OGFRL1       ATAD1      PPDPFL        LATS1
TCGA.DJ.A13L -0.32633023  0.7477092  0.06217758 -0.09958441  0.107080851
TCGA.DE.A4M9  0.78775867 -1.9593282 -0.12710197 -0.09958441  0.057197707
TCGA.J8.A3O1  1.78740479  0.6340798  1.86648724 -0.09958441  3.025901663
TCGA.ET.A39K -1.65625689  0.5224053 -1.99440254 -0.09958441 -1.763368412
TCGA.4C.A93U  0.51671591  1.5323592  0.93936016 -0.09958441  0.947116194
TCGA.ET.A25I -0.61594986 -0.7675800  0.05719771 -0.09958441 -0.460695126
TCGA.ET.A2MZ  0.02237138  0.5338351  0.33157761 -0.09958441  0.499746840
TCGA.EM.A3AK -0.13713116  1.7181407  0.87923079 -0.09958441  0.002485504
TCGA.FK.A4UB -1.74030965 -0.6647705 -0.71522984 -0.09958441 -0.347375322
TCGA.BJ.A28X  1.36253454  1.4283268 -0.30022446 -0.09958441  0.556907718

In the vignette, you say The expression values are coded as continuous numerical values which are comparable between samples. So how do I convert my raw counts to continuous values (Do you suggest using normalised reads from DESEq2)? Besides, how do I handle biological replicates.

Paired samples analysis

Dear authors,

I am grateful for the package and how easy it is to use. I would like to analyse a dataset with paired samples (repeated measures). Can I use the function as-is, i.e. comparing baseline and post-treatment data?

Thank you for your time.

Regards,
Mikhael

Variance and C-, S-, D- scores not showing

Hello,

So I am an undergraduate, so it is possible that my errors are easily an ignorance issue, but I am trying to analyze RNA-seq data through your data. I work in a lab looking for gravitropic genes in the Arabidopsis Thaliana model. We have a dataset of previously examined RNA-seq data that I am trying to run through the code in R but the results I have found are confusing to say the least. When bootstrapped at 10, we have no variance and the C- and D- values cap out at "infinity". When bootstrapped at 100, we get no C-, S-, or D- scores with only numbers showing in the Rho2 and var2.

I am running the analysis on my laptop (16Gb), but despite a longer wait it still runs just fine. Our data also only has 4 samples per treatment and tissue, so the analysis is >27,000 genes but only 4 samples. Would either of these relate to the issues we are finding or could you offer any more advice for this issue?

Thank you,