scRNA-seq,10x cellranger pipelines,low custom tdtomato gene content! looking for help！ I have a set of scRNA-seq samples expressing TdTomato, which has high content in microscope. I followed the 10x cellranger pipelines to finsh the work, my procedures are as follows:


added TdTomato on an additional chromosome:

added an entry in the gtf 
cellranger mkref
cellranger count


I got the result, which includes:


But strangely, the tdtomato expression content in my three sample are all very low! And cells with tdtomato are also very low and disperse!! Take sample 1 as example, you can see the picture:



It's not normal, but I can't find the reason. Following the 10x genomics help documentation noticed that by executing the CellRanger filtering steps, maybe my tdtomato reads are filtered too much, but because I'm not very skillful at programming and I'm first time to do RNA-seq (and scRNA-seq), I just don't know how to analysis the bam file as suggested by the official website and find the real reason of the problem:


If you can show me the code need to do, I will be very grateful!
A few suggestions: 


Manually inspecting the BAM file with a genome browser like IGV, as @GWW suggested and checking the qualities of the alignments in your BAM/SAM file (SAM is the human readable version of BAM, there are tools to convert one to another);
Using another single cell analysis tool like Seurat so that you can manually set thresholds for QC and not lose to many (tdtomato) expressing cells at the QC step (I don't know about your experiment but if transfected, cells could be at a worse condition than non-transfected ones and hence might not pass QC);
Double checking your tdtomato reference sequence, I don't know about tdtomato but for example GFP has flavors like EGFP, similar but different sequences, might explain low alignment score if you observe in step 1;
Taking the limitations of scRNA-seq (especially droplet-based) technologies into account. The figure below is from the CITE-seq paper (from supp info), Simultaneous epitope and transcriptome measurement in single cells (﻿Stoeckius et al., 2017), showing that the protein - RNA measurements of a given gene might show poor correlation.