What are the effects of adding technical/pseudo replicates to the ones to be deconvolved? I want to perform deconvolution of a small data set (3 samples of one conditions vs. 4 of another). The tool CibersortX can estimate the sample-specific expression of cells, using the bulk expression data provided, and a signature matrix of typical expression by cell type.
I suffer from a low number of samples.  However, I have also a technical replicate for each of the 7.
The biological replicate underwent a biochemical procedure and then was sequenced once again - however the biochemical procedure turned out not to have a strong effect, so it's more of a technical replicate than a biological one.
A large ratio of samples to cell types is good for deconvolution. What would be the effect of adding those technical replicates to the pool, and then deconvolving them using CibersortX High-Resolution mode (which outputs per-sample value)?
What would be the effect of generating pseudo-replicates (taking the replicates I have, and artificially adding noise to them ) to the pool of CibersortX High Resolution mode ?
Some more details : I intend to use a published signature matrix , averaged to two cell-types only (due to the small number of samples I have) : the cell type that which is of my primary interest (for example Monocytes/T-cells) + "All Others". I am interested in finding  differentially expressed genes in that one cell type that is of interest to me.

Could you clarify your experimental setup and in particular what exactly your "technical" replicates are? Was the chemical treatment done on the tissue, was this a treatment of the extracted RNA, or was it a treatment of the sequencing libraries?
The effects of adding in additional samples is difficult to predict, considering these samples were treated. Although the effect was not strong, it is difficult to gauge what exactly this means; I assume it is different from no effect? Generally, I think if your cells / tissues were treated with something that has no effect you can include them as biological replicates. If this treatment was done on nucleic acids, I would rather consider them technical replicates.
Generally, I would expect that adding in pseudo-replicates will not improve your expression profiles in terms of biological insights. In fact I would rather worry that you obtain results that are a biased by the addition of noise. Generally I think that pseudo-replicates cannot improve quality of biological datasets. If you require additional samples you need to actually generate biological samples.
Of course, it is basically impossible to know what the effects would be for your particular dataset, have you actually tried including the samples, or creating pseudo-replicates, and comparing the results to your original dataset?
