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fanyue322 / TDEseq

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How to get chart with P-Values. #5

Open abelitesfaye opened 1 month ago

abelitesfaye commented 1 month ago

How to get chart with P-Values.

Screenshot 2024-07-24 at 1 16 29 PM
fanyue322 commented 1 month ago

Hi abelitesfaye,

Would you be able to provide a more detailed explanation regarding charts with P-Values?

abelitesfaye commented 1 month ago

Hello,

Yes, in your tutorial under Association of gene expression with time points you show the chart above with gene names, and corresponding p-values. I was wondering how you got this chart, so I can perform it on my own data.

fanyue322 commented 1 month ago

Hi, Use the following code to obtain the result: result<-GetTDEseqAssayData(tde,slot='tde')
where tde is your object

abelitesfaye commented 1 month ago

Hello,

I am having issues plotting the PatternHeatmap, ad I am getting error: Error in split.default(x = colnames(x = object), f = split.by) : first argument must be a vector after running: PatternHeatmap(obj=tde, stage.id='stage', features.show=features.show, features.num=10). I am confused on where the error might be. Can you provide some information regarding the expected input types for the pattern heatmap?

fanyue322 commented 1 month ago

Hi,

Would you be able to share the object as well as the other arguments you're referring to? Please send the object to my email address: xafanyue@163.com

fanyue322 commented 4 weeks ago

Hi abelitesfaye, I've reviewed your tde object, and it appears that the normalized data has not been configured. To address this, you might consider implementing the following code : downsampled_tde@assays$RNA@data <- downsampled_tde@assays$RNA@counts

However, it seems that there are no (DE) genes identified in your dataset, which would result in the PatternHeatmap function throwing an error.

You may want to consider re-evaluating your data analysis. Here's a suggested approach as an illustrative example:

tde_param <- list(sample.var = "orig.ident", stage.var = "stage", fit.model = "lm", pct = 0.1, tde.thr = 0.5, lfc = 0.1, max.gcells = Inf, min.tcells = 3)

downsampled_tde <- tdeseq(object = downsampled_tde, tde.param=tde_param,num.core=1)

PatternHeatmap(downsampled_tde,stage.id='stage')