Thank you for providing your tool for calling CNVs from Visium data!
I am currently attempting to rerun the analysis for the dataset used in your publication (Patient 4 Rep1). Based on the Leiden Clustering, I generated an annotate.csv file and a Graph_based.csv, and I created a FeatureCSV from the count matrix after converting HUGO Symbols to Ensembl IDs.
Based on cnr files in ~/analysis/spatial/grouped_spots/CNVs/cnr I can observer some changes in log2 coloumn which kind of make
However, I encountered the following error during Step 6:
Step 6, finalizing CNV calling...
During startup - Warning messages:
1: Setting LC_CTYPE failed, using "C"
2: Setting LC_COLLATE failed, using "C"
3: Setting LC_TIME failed, using "C"
4: Setting LC_MESSAGES failed, using "C"
5: Setting LC_MONETARY failed, using "C"
6: Setting LC_PAPER failed, using "C"
7: Setting LC_MEASUREMENT failed, using "C"
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Loading required package: gplots
Attaching package: 'gplots'
The following object is masked from 'package:stats':
lowess
Loading required package: gtools
[1] "No gains and losses provided."
[1] "Grouping by genes."
[1] "Everything is good!"
[1] "All arguments have names"
Error in seq.default(m, 0, length.out = 40) :
'from' must be a finite number
Calls: cnvs -> cnvPlot2 -> seq -> seq.default
Execution halted
Hello,
Thank you for providing your tool for calling CNVs from Visium data!
I am currently attempting to rerun the analysis for the dataset used in your publication (Patient 4 Rep1). Based on the Leiden Clustering, I generated an annotate.csv file and a Graph_based.csv, and I created a FeatureCSV from the count matrix after converting HUGO Symbols to Ensembl IDs.
Based on cnr files in ~/analysis/spatial/grouped_spots/CNVs/cnr I can observer some changes in log2 coloumn which kind of make
However, I encountered the following error during Step 6:
overview of input files: filteredFeatureCSV:
clusterCSV:
annotate file:
tissue_position_list