generate_alpha_trend_test_long error(): error in vec_data() : ! x must be a vector, not a <sample_data> object.

[mariamaseng]

Hi, thanks for a great package. I am not able to generate: generate_alpha_trend_test_long - see photo.

generate_alpha_trend_test_long - error saying: error in vec_data() : ! x must be a vector, not a object.

The time variable is numeric. Do you understand the problem?

Best, Maria

[cafferychen777]

Hi @mariamaseng ,

Thank you for your kind words and for using the MicrobiomeStat package. I'm sorry to hear you're encountering an issue with the generate_alpha_trend_test_long function.

Could you please show me the structure of your current data by running the command str(ms_rare)? Additionally, ensure that the column names in feature.tab match the row names in meta.dat. Both should be consistent and represent sample names.

Your clarification will help us diagnose the problem more effectively.

Best regards, Chen YANG

[mariamaseng]

Thank you. Attached you will find the results for str(ms_rare). I have done mStat_validate_data(ms_rare), and it seems to pass just fine.

Thank you a lot for being so helpful! Best!

[cafferychen777]

Hello @mariamaseng ,

Thank you for reaching out and for providing the details of your issue with the MicrobiomeStat package. I appreciate your feedback and am here to help.

I'm glad to hear that you used mStat_validate_data(ms_rare) and it passed without errors. However, I understand that you are still facing issues with your data object. Based on the screenshots you provided, it seems that feature.tab, meta.dat, and feature.ann may not be in the expected format of base R matrices or data frames.

To address this issue, I recommend the following steps to ensure that your data components are correctly formatted:

1. For feature.tab, please try converting it to a data frame and then to a matrix using the following code:

   ms_rare$feature.tab <- as.data.frame(ms_rare$feature.tab) %>%
   as.matrix()

2. For meta.dat, similarly, convert it to a matrix and then back to a data frame:

   ms_rare$meta.dat <- as.matrix(ms_rare$meta.dat) %>%
   as.data.frame()

3. For feature.ann, apply the same approach:

   ms_rare$feature.ann <- ms_rare$feature.ann %>%
   as.data.frame() %>%
   as.matrix()

By following these steps, you should be able to ensure that your data components are in the expected format for the MicrobiomeStat package. If you have been following the guidelines provided in the package's wiki, these adjustments should help resolve the issue.

However, if you continue to face problems or have further questions, please don't hesitate to reach out. It's essential to ensure that your analysis proceeds smoothly, and I'm here to assist you in any way I can.

If you find that the documentation is challenging to understand or if you encounter any limitations in R, seeking assistance from a bioinformatics expert is a wise step to overcome any obstacles in your analysis.

Thank you for your patience and understanding. I'm committed to helping you make the most of the MicrobiomeStat package for your research.

Best regards, Chen YANG

Note: Given the complexity of microbiome data analysis, I strongly recommend considering the assistance of a bioinformatics expert to ensure the accuracy and reliability of your results.