pathway_pca(): Error in prcomp.default(t(abundance), center = TRUE, scale = TRUE) : cannot rescale a constant/zero column to unit variance about ggpicrust2 HOT 3 CLOSED

Dear mayagabitzsch,

I hope this email finds you well. I am writing in response to your recent inquiry regarding an issue you encountered while attempting to generate Metacyc PCA plots and a question about the annotation of KO abundance.

Regarding the issue with the PCA plots, it appears that after modifying your data files to focus your analysis on a specific set of samples, you encountered an error message stating, "Error in prcomp.default(t(abundance), center = TRUE, scale = TRUE): cannot rescale a constant/zero column to unit variance." This error typically occurs when there are columns in the abundance data that contain constant or zero values, which prevents rescaling to unit variance.

To address this issue, I suggest applying the following code snippet to exclude rows with all-zero values before performing the PCA analysis:

abundance_data <- read_delim(
  "/Users/apple/Microbiome/ggpicrust2总/ggpicrust2测试/ggpicrust2_test/mayagabitzsch/path_abun_unstratroots.copy.txt",
  delim = "\t",
  col_names = TRUE,
  trim_ws = TRUE
)

metadata <- read_delim(
  "/Users/apple/Microbiome/ggpicrust2总/ggpicrust2测试/ggpicrust2_test/mayagabitzsch/MetadataROOTS.copy.txt",
  delim = "\t",
  escape_double = FALSE,
  trim_ws = TRUE
)

pathway_pca(abundance = abundance_data %>% column_to_rownames("pathway") %>% filter(rowSums(. != 0) > 0), metadata = metadata, group = "ENV_ALT")

Please make sure to modify the file paths in the code snippet to reflect the appropriate locations of your data files. This adjustment should help resolve the error by removing rows with all-zero values from the analysis.

As for your query regarding the annotation of KO abundance, if you are observing human diseases in your error bars, it is possible that the KEGG database includes pathway classifications related to human diseases. These annotations are not specific to the sample type but rather reflect the comprehensive nature of the KEGG database, which covers various biological contexts. If you believe that annotations specific to your sample type would be more appropriate, you may consider using a database that aligns better with your research discipline for performing the annotations.

I hope this information proves helpful in resolving the issues you encountered and clarifying the annotation query. If you have any further questions or require additional assistance, please feel free to ask. I am here to help.

Best regards,
Chen YANG

from ggpicrust2.

Thanks so much for your help, I just have 1 last question for you, I have made these error bar graphs and the labels are not aligned correctly for some reason. No matter how I alter the PDF export size, many of my graphs have alignment errors. Here is one of them as an example
[
fixedC.pdf
](url)

Do you have any reccomendation on how to fix this? I also have very hard to read heatmaps
METACYC_HP_KEGG_A.pdf
Is there anyway I can get these heatmaps to be readable? or is the data set to large for heatmaps to be a good visual tool?
Thanks again

from ggpicrust2.

Dear @mayagabitzsch ,

Thank you for reaching out with your additional question regarding alignment issues in the error bar graphs and readability concerns with the heatmaps generated using ggpicrust2. I'll address each of your concerns separately.

1. Alignment Issues in Error Bar Graphs:
   The misalignment of labels in your error bar graphs might be due to the relatively small number of pathways being visualized. To address this, you can manually adjust the alignment using Adobe's Illustrator software or any other vector graphics editor. By opening the PDF file in such software, you can make precise adjustments to the label positions to ensure proper alignment.

2. Readability of Heatmaps:
   If you are experiencing difficulties in reading the heatmaps, there are a few steps you can take to improve their readability. Firstly, I would recommend referring to the pathway_heatmap section of the ggpicrust2 tutorial, specifically the link provided here. This section provides guidelines on how to filter the pathways before generating the heatmap. By selectively including relevant pathways and excluding less informative ones, you can enhance the clarity and interpretability of the heatmap.

Additionally, you can try adjusting the color scale used in the heatmap to improve visibility. Experimenting with different color palettes or adjusting the color contrast can make a significant difference in the readability of the heatmap. You can refer to the heatmap documentation in ggpicrust2 for further details on customizing the color scale.

I hope these recommendations help in addressing the alignment issues in the error bar graphs and improving the readability of the heatmaps. If you have any further questions or require additional assistance, please feel free to let me know. I'm here to support you.

Thank you for your patience and for being a valuable user of ggpicrust2.

Best regards,
Chen

from ggpicrust2.