jsb-ucla / clipper Goto Github PK

Hello :)
In general, is it a good practice to normalize (0,1) and/or log-transform all types of data?
Or it totally depends on the type of data?

Hi dear,

I have my count matrix (featureCounts output - raw counts) with the first column as gene_names and the other two columns as ctrl1 and kd1. For using Clipper I generated two data frames out of the main count matrix (named Control and KD), one data frame with the columns gene_names and ctrl1 and the other data frame with the gene_names column and the kd1 column.

The class of the gene_names column is character and the class of ctrl1 and kd1 columns is numeric.

When I ran Clipper as:
result <- Clipper(KD,control, analysis = "differential", FDR = c(0.01, 0.05, 0.1))

I get this warning: In mean.default(x, na.rm = T) : argument is not numeric or logical: returning NA and in the output I have NAs.

The head of my files looks as:
head(Control)

gene_names ctrl1
4933401J01Rik     0
Gm26206     0
Xkr4     0
Gm18956     0
Gm37180     0
Gm37363     0

head(KD)

gene_names kd1
4933401J01Rik   0
Gm26206   0
Xkr4   5
Gm18956   0
Gm37180   0
Gm37363   0

and sapply(KD, class) gives me:
gene_names kd1
"character" "numeric"

Could you please guide me on where is the potential bug? I really appreciate any help you can provide.

Hi @xcggates

Thanks for your beautiful code!

when I use MACS2 and Clipper to call peak, I follow the steps described in vignettes/Clipper.Rmd.

Mouse TF ChIP-seq data was input data,and this step

re <- Clipper(score.exp = matrix(s1, ncol = 1), 
              score.back = matrix(s2, ncol = 1), 
              analysis = "enrichment")

Error in matrix(s1, ncol = 1) : data is too long

It would be very appreciated if you could give me some help

Hi, I've run Clipper for a differential methylation analysis on a small cohort, and now I'm wondering how to verify that this was an appropriate fit for my data. Normally I'd look at a p-value histogram and QQ plot to assess a linear model fit, but without p values, I'm not sure how to proceed. Thanks for any suggestions!

Hi,
I`m using Clipper to find DEGs in RNAseq data.
I can find the discoveries. But I also need the fold change of these discoveries.
Can Clipper get the fold change?

Hi,

I am trying to use Clipper to do the differential analysis on my data, which is a single-cell dataset collected from multiple subjects. I found the example data included in the package only has 3 columns(replicates). I wonder if this is a bulk-seq data. If this is the case, could you please clarify whether the code in the vignettes remains applicable to analyzing single-cell data collected from multiple subjects?

Thanks!