Hello!
I am trying to visualise the output from q2-picrust2 plugin predictions of 16s amplicon data.
And I am having troubles
First issue is that when I try use official instructions I got next error:
results_file_input <- ggpicrust2(file = abundance_file,
metadata = metadata,
group = "Layers", # For example dataset, group = "Environment"
reference = "5",
pathway = "KO",
daa_method = "LinDA",
ko_to_kegg = TRUE,
order = "pathway_class",
p_values_bar = TRUE,
x_lab = "pathway_name")
Starting the ggpicrust2 analysis...
Converting KO to KEGG...
Loading data from file...
Rows: 10543 Columns: 22
── Column specification ────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr (1): #OTU ID
dbl (21): 1A, 1B, 1C, 2A, 2B, 2C, 3AA, 3AB, 3AC, 3BA, 3BB, 3BC, 4A, 4B, 4C, 5A, 5B, 5C, 5D, 5E, 5F
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Loading KEGG reference data. This might take a while...
Performing KO to KEGG conversion. Please be patient, this might take a while...
|==========================================================================================| 100%
KO to KEGG conversion completed. Time elapsed: 28.57 seconds.
Removing KEGG pathways with zero abundance across all samples...
KEGG abundance calculation completed successfully.
Performing pathway differential abundance analysis...
Sample names extracted.
Identifying matching columns in metadata...
Matching columns identified: #SampleID . This is important for ensuring data consistency.
Using all columns in abundance.
Converting abundance to a matrix...
Reordering metadata...
Converting metadata to a matrix and data frame...
Extracting group information...
Running LinDA analysis...
Performing LinDA analysis...
0 features are filtered!
The filtered data has 21 samples and 299 features will be tested!
Imputation approach is used.
Fit linear models ...
Completed.
Processing LinDA results...
LinDA analysis is complete.
Success: Found 933 statistically significant biomarker(s) in the dataset.
Annotating pathways...
Starting pathway annotation...
DAA results data frame is not null. Proceeding...
KO to KEGG is set to TRUE. Proceeding with KEGG pathway annotations...
We are connecting to the KEGG database to get the latest results, please wait patiently.
The number of statistically significant pathways exceeds the database's query limit. Please consider breaking down the analysis into smaller queries or selecting a subset of pathways for further investigation.
Returning DAA results filtered annotation data frame...
Creating pathway error bar plots...
The following pathways are missing annotations and have been excluded: ko05340, ko00564, ko00680, ko00562, ko00563, ko00561, ko00440, ko04062, ko04060, ko00740, ko04111, ko04940, ko05412, ko04650, ko00310, ko00600, ko04140, ko04141, ko04142, ko00604, ko04260, ko03040, ko04110, ko05142, ko04710, ko00513, ko04973, ko00510, ko05110, ko04974, ko04976, ko01051, ko00565, ko00904, ko00524, ko00514, ko00260, ko00190, ko05140, ko00592, ko00591, ko00590, ko00062, ko00061, ko00253, ko04370, ko04730, ko04740, ko00380, ko00500, ko05120, ko04666, ko04964, ko04962, ko04960, ko04660, ko00624, ko00627, ko00621, ko00620, ko00623, ko00622, ko00940, ko00941, ko01053, ko00943, ko00100, ko00945, ko01057, ko01056, ko04670, ko04145, ko00071, ko00072, ko04360, ko05217, ko05216, ko05215, ko05214, ko05213, ko05212, ko05211, ko00902, ko00534, ko04910, ko00531, ko04916, ko00532, ko00633, ko00630, ko00363, ko05130, ko04114, ko04914, ko00130, ko04330, ko03050, ko00361, ko00730, ko00362, ko01040, ko00603, ko04270, ko00280, ko03013, ko05200, ko00601, ko03015, ko00312, ko05020, ko00523, ko05146, ko05144, ko00051, ko00401, ko00400, ko04020, ko00480, ko00643, ko00642, ko00640, ko00960, ko02020, ko00120, ko03008, ko05414, ko04614, ko04610, ko04340, ko00980, ko00983, ko05131, ko04711, ko00020, ko00710, ko00196, ko04130, ko00650, ko05223, ko05220, ko05221, ko02030, ko04744, ko04745, ko04530, ko04612, ko04621, ko04620, ko04623, ko04622, ko00910, ko04971, ko00460, ko04970, ko00511, ko00970, ko04972, ko04120, ko05014, ko03022, ko03020, ko00982, ko03010, ko05100, ko00860, ko05410, ko00331, ko00330, ko04080, ko03430, ko00906, ko04520, ko00903, ko00472, ko05111, ko04810, ko00240, ko04012, ko04010, ko04011, ko00944, ko04113, ko04070, ko04640, ko04310, ko03420, ko02040, ko04912, ko00670, ko04150, ko05160, ko04112, ko04720, ko04722, ko04075, ko05340, ko00564, ko00680, ko00562, ko00563, ko03030, ko00561, ko00440, ko04060, ko00740, ko04664, ko04111, ko04940, ko05412, ko03450, ko00760, ko00920, ko00311, ko00310, ko00600, ko04140, ko04141, ko04142, ko00604, ko04260, ko04110, ko05142, ko04540, ko04710, ko00909, ko00513, ko04973, ko00510, ko05110, ko04974, ko04976, ko00450, ko01051, ko00565, ko00904, ko00524, ko00300, ko05222, ko00514, ko05416, ko00260, ko00190, ko00750, ko05140, ko00592, ko00591, ko00590, ko00062, ko00061, ko00253, ko03060, ko04370, ko04730, ko04740, ko00380, ko00500, ko05120, ko05322, ko04964, ko04962, ko04960, ko04660, ko00625, ko00624, ko00627, ko00626, ko00620, ko00623, ko00622, ko04380, ko00940, ko00941, ko00943, ko00945, ko01057, ko01056, ko05016, ko04670, ko04145, ko00071, ko00072, ko04360, ko05217, ko05216, ko05215, ko05214, ko05213, ko05212, ko05211, ko05210, ko00902, ko00534, ko04910, ko00531, ko04916, ko00532, ko00360, ko00633, ko00630, ko00363, ko00364, ko05130, ko04115, ko04914, ko04330, ko03050, ko00361, ko00040, ko00730, ko00362, ko01040, ko00603, ko03018, ko04270, ko00281, ko00280, ko03013, ko04626, ko05200, ko00601, ko03015, ko00312, ko05020, ko05143, ko00523, ko00520, ko05145, ko05144, ko00052, ko00051, ko00401, ko00400, ko04020, ko00350, ko00480, ko00643, ko00642, ko00640, ko02020, ko00965, ko03008, ko05414, ko04614, ko04610, ko04340, ko05010, ko05012, ko00980, ko00410, ko00983, ko05150, ko00791, ko00790, ko04711, ko00020, ko00710, ko00196, ko02060, ko00785, ko00650, ko05223, ko05220, ko05221, ko02030, ko03320, ko04744, ko04745, ko04530, ko00522, ko04612, ko04621, ko04620, ko04623, ko04622, ko00910, ko04971, ko04970, ko00830, ko00780, ko00511, ko00970, ko00030, ko04972, ko00232, ko00230, ko04350, ko00540, ko03022, ko00660, ko04512, ko00982, ko03010, ko00140, ko05100, ko00860, ko05410, ko00331, ko00330, ko04080, ko04514, ko00908, ko04320, ko03430, ko00906, ko04520, ko00903, ko00472, ko00473, ko05111, ko04510, ko04210, ko00240, ko04012, ko04013, ko04010, ko04011, ko00944, ko04113, ko04640, ko04310, ko03420, ko02040, ko04912, ko04150, ko05160, ko04144, ko00930, ko04112, ko04722, ko04075, ko05340, ko00564, ko00680, ko00562, ko00563, ko00561, ko00440, ko04060, ko00740, ko04664, ko04111, ko04940, ko00311, ko00600, ko04140, ko04142, ko00604, ko04260, ko03040, ko04110, ko05142, ko04710, ko00513, ko04973, ko00510, ko04974, ko04976, ko01051, ko00565, ko00904, ko00524, ko00514, ko00190, ko00950, ko05140, ko00592, ko00591, ko00590, ko00062, ko00253, ko04370, ko04730, ko04740, ko00380, ko00500, ko05120, ko04966, ko05322, ko04964, ko04962, ko04960, ko04660, ko00626, ko00621, ko00620, ko00623, ko04380, ko00940, ko00941, ko01053, ko00100, ko00945, ko01056, ko05016, ko04670, ko04145, ko04360, ko05219, ko05217, ko05216, ko05215, ko05214, ko05213, ko05212, ko05211, ko01055, ko00902, ko00534, ko04910, ko04916, ko00360, ko00633, ko00363, ko00364, ko05130, ko04114, ko00121, ko04914, ko02010, ko00130, ko04330, ko03050, ko00361, ko00730, ko01040, ko00603, ko04270, ko00281, ko03013, ko04626, ko00601, ko03015, ko00312, ko05020, ko05143, ko00521, ko05146, ko05144, ko00051, ko00401, ko04020, ko00480, ko00643, ko00120, ko03008, ko05414, ko04614, ko04340, ko05010, ko05012, ko00980, ko00410, ko00983, ko00790, ko04711, ko00710, ko00196, ko02060, ko00340, ko04130, ko05223, ko05220, ko05221, ko02030, ko04744, ko04745, ko04530, ko04612, ko04621, ko04620, ko04623, ko00910, ko04971, ko00460, ko04970, ko00780, ko00511, ko04972, ko04120, ko00540, ko03022, ko04512, ko00982, ko05100, ko00860, ko05410, ko00331, ko00330, ko04080, ko04514, ko00908, ko04930, ko04320, ko00906, ko00900, ko04520, ko00903, ko05111, ko04210, ko04012, ko04013, ko04011, ko00944, ko04070, ko04640, ko04310, ko02040, ko04912, ko00670, ko04150, ko05160, ko04144, ko00930, ko04112, ko04720, ko04722, ko04075, ko05340, ko00564, ko00680, ko00562, ko00563, ko00440, ko04062, ko04060, ko00740, ko04664, ko04111, ko04940, ko05412, ko00195, ko00310, ko04146, ko00600, ko04141, ko04142, ko00604, ko04260, ko03040, ko05142, ko04710, ko00909, ko00513, ko04973, ko00510, ko05110, ko04974, ko04976, ko01051, ko00565, ko00904, ko00524, ko00300, ko00514, ko00190, ko00950, ko05140, ko00591, ko00062, ko00061, ko00253, ko03060, ko04370, ko04730, ko04740, ko00500, ko05120, ko04666, ko04966, ko05322, ko04964, ko04960, ko04660, ko00625, ko00624, ko00627, ko00626, ko00621, ko00623, ko00622, ko04380, ko00940, ko00941, ko01053, ko00943, ko00100, ko01057, ko01056, ko05016, ko04670, ko04145, ko00071, ko05219, ko05217, ko05216, ko05215, ko05214, ko05213, ko05212, ko01055, ko00902, ko04910, ko00531, ko04916, ko00360, ko00633, ko00364, ko04114, ko04914, ko00130, ko04330, ko03050, ko00361, ko00040, ko00362, ko00603, ko03018, ko04270, ko00281, ko03013, ko05200, ko00601, ko00312, ko05020, ko05143, ko00523, ko00521, ko00053, ko00051, ko00401, ko04020, ko00350, ko00643, ko00960, ko02020, ko00120, ko00965, ko03008, ko05414, ko04614, ko04610, ko04340, ko05010, ko05012, ko00410, ko00791, ko00790, ko05131, ko04711, ko00710, ko00196, ko00550, ko05223, ko05220, ko05221, ko03320, ko04744, ko04745, ko04530, ko00522, ko04612, ko04621, ko04620, ko04623, ko04622, ko00910, ko04971, ko04970, ko00830, ko00780, ko00511, ko00970, ko04972, ko00232, ko04122, ko04120, ko04350, ko05014, ko03020, ko04512, ko05100, ko05410, ko00331, ko04080, ko04514, ko00908, ko04930, ko04320, ko00906, ko00901, ko04520, ko00903, ko00471, ko00472, ko05111, ko04510, ko04810, ko04012, ko04013, ko04010, ko04011, ko00944, ko04070, ko04640, ko04310, ko02040, ko04912, ko00670, ko04920, ko00930, ko04112, ko04720, ko04722, ko04075, ko00680, ko00440, ko04062, ko04664, ko05412, ko00600, ko04142, ko00604, ko04260, ko03040, ko04110, ko05142, ko04710, ko00513, ko00510, ko05110, ko04974, ko00565, ko00514, ko00950, ko05140, ko00062, ko00253, ko04370, ko04740, ko00500, ko05120, ko04666, ko04966, ko05322, ko04964, ko04962, ko04960, ko04660, ko00625, ko00624, ko00940, ko01053, ko00943, ko00945, ko01057, ko04670, ko04145, ko04360, ko05217, ko05216, ko05214, ko05213, ko05212, ko05211, ko00534, ko00531, ko04916, ko00532, ko00633, ko00363, ko00364, ko05130, ko04114, ko00121, ko00430, ko02010, ko04330, ko00040, ko04270, ko03013, ko03015, ko00312, ko05143, ko00520, ko00052, ko04020, ko00643, ko00642, ko00120, ko00965, ko05010, ko00980, ko05150, ko00791, ko05131, ko00196, ko05220, ko05221, ko02030, ko04744, ko04745, ko04530, ko04620, ko04971, ko04970, ko00830, ko00511, ko04972, ko04350, ko00540, ko05014, ko03022, ko00982, ko05100, ko05410, ko00331, ko04080, ko00906, ko00901, ko04520, ko00471, ko05111, ko04013, ko04011, ko02040, ko04912, ko04150, ko04920, ko05160, ko04144, ko04720, ko04722, ko04075
You can use the 'pathway_annotation' function to add annotations for these pathways.
The 'method' column in the 'daa_results_df' data frame contains more than one method. Please filter it to contain only one method.
The 'group1' or 'group2' column in the 'daa_results_df' data frame contains more than one group. Please filter each to contain only one group.
Error in pathway_errorbar(abundance = abundance, daa_results_df = daa_sub_method_results_df, :
Visualization with 'pathway_errorbar' cannot be performed because there are no features with statistical significance. For possible solutions, please check the FAQ section of the tutorial.
So, I have 5 layers of permafrost, and I tried to compare two of them, but I need to visualize every layer (each contains three replicates as samples)
So comparing any 2 layers provide me with the insignificant results, and I got stuck here for a while.
I thought just to get top 50 or top 100 most distributed pathways and put them on the heatmap...but without this piece - "results_file_input ", its not working.
Can anyone suggest what I might do with it?
I have found this code also
pathway_annotation(
file = NULL,
pathway = NULL,
daa_results_df = NULL,
ko_to_kegg = FALSE
)
But without daa_results_df I cant get - pathway_name, pathway_description, pathway_class, pathway_map
I can get only pathway_description, that looks like -
E1.1.1.1, adh; alcohol dehydrogenase [EC:1.1.1.1]
But it's kind of messy - I can't visualize it.
Has anyone come across anything like this?
Any suggestions on how to get a full df without statistical tests?
I need full information of pathway_name, pathway_description, pathway_class, pathway_map to put on the heatmap or some kind of visualisation.
I would appreciate any help
Thank you
Best
Alla