Comments (12)
cafferychen777
commented on September 17, 2024
Hello,
Thank you for bringing this issue to my attention. In order to troubleshoot this problem effectively, I would need a bit more context. Could you possibly provide the dataset you are working with? This would allow me to replicate the issue on my end and hopefully identify a solution more swiftly.
Just to remind you, if your dataset contains sensitive or private information, please make sure to anonymize or de-identify it before sharing.
Thank you for your cooperation and patience while we work to resolve this issue.
Best regards.
from ggpicrust2.
cafferychen777
commented on September 17, 2024
Dear @Lacona ,
Thank you for reaching out regarding the error you encountered while creating pathway error bar plots using ggpicrust2.
I noticed that you're using the "LinDA" method in your code. When dealing with multiple groups in the metadata, I recommend switching to the "ALDEx2" method for creating pathway error bar plots. From my experience, LinDA can sometimes fail to produce accurate visualizations in cases of comparisons involving multiple groups.
Please try modifying your code as follows:
results_file <- ggpicrust2(file = abundance_file,
metadata = metadata,
group = "treatment",
pathway = "KO",
daa_method = "ALDEx2",
ko_to_kegg = TRUE,
order = "pathway_class",
p_values_bar = TRUE,
x_lab = "pathway_name")Please let me know if this resolves the issue or if you have any further questions.
Best regards,
Chen
from ggpicrust2.
Lacona
commented on September 17, 2024
Hello Chen,
thank you for getting back to me so quickly.
I needed some time to try ALDEx2. Unfortunately I can't figure out how to do multiple group comparisons with the ALDEx2 method. Is there any way? The $results don't show them.
Thank you,
Andrea
…________________________________
Von: Caffery Yang ***@***.***>
Gesendet: Dienstag, 20. Juni 2023 06:33
An: cafferychen777/ggpicrust2 ***@***.***>
Cc: Andrea Pietruska ***@***.***>; Mention ***@***.***>
Betreff: [EXT] Re: [cafferychen777/ggpicrust2] ggpicrust2 error in levels (Issue #34)
CAUTION: Email Originated Outside of Auburn.
Dear @Lacona<https://github.com/Lacona> ,
Thank you for reaching out regarding the error you encountered while creating pathway error bar plots using ggpicrust2.
I noticed that you're using the "LinDA" method in your code. When dealing with multiple groups in the metadata, I recommend switching to the "ALDEx2" method for creating pathway error bar plots. From my experience, LinDA can sometimes fail to produce accurate visualizations in cases of comparisons involving multiple groups.
Please try modifying your code as follows:
results_file <- ggpicrust2(file = abundance_file,
metadata = metadata,
group = "treatment",
pathway = "KO",
daa_method = "ALDEx2",
ko_to_kegg = TRUE,
order = "pathway_class",
p_values_bar = TRUE,
x_lab = "pathway_name")
Please let me know if this resolves the issue or if you have any further questions.
Best regards,
Chen
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cafferychen777
commented on September 17, 2024
Dear Andrea,
Thank you for reaching out and for your patience. I understand that you have been exploring the ALDEx2 method and are having trouble figuring out how to perform multiple group comparisons. Unfortunately, the $results output does not provide the desired information.
To address this issue, you can try the following code snippet:
library(readr)
library(ggpicrust2)
library(tibble)
library(tidyverse)
library(ggprism)
library(patchwork)
# Load metadata as a tibble
metadata <- read_delim("/Users/apple/Microbiome/ggpicrust2总/ggpicrust2测试/ggpicrust2_test/Lacona/Salmonella22_metadata_42R.tsv", delim = "\t", escape_double = FALSE, trim_ws = TRUE)
# Load KEGG pathway abundance
kegg_abundance <- ko2kegg_abundance("/Users/apple/Microbiome/ggpicrust2总/ggpicrust2测试/ggpicrust2_test/Lacona/pred_metagenome_unstrat (2).tsv")
# Perform pathway differential abundance analysis (DAA) using ALDEx2 method
daa_results_df <- pathway_daa(abundance = kegg_abundance, metadata = metadata, group = "treatment", daa_method = "ALDEx2", select = NULL, reference = NULL)
# Filter results for ALDEx2_Welch's t-test method
daa_sub_method_results_df <- daa_results_df[daa_results_df$method == "ALDEx2_Kruskal-Wallace test", ]
# Annotate pathway results using KO to KEGG conversion
daa_annotated_sub_method_results_df <- pathway_annotation(pathway = "KO", daa_results_df = daa_sub_method_results_df, ko_to_kegg = TRUE)
# Generate pathway error bar plot
pathway_errorbar(abundance = kegg_abundance, daa_results_df = daa_annotated_sub_method_results_df %>%
filter(!is.na(pathway_class)), Group = metadata$treatment, p_values_threshold = 0.05, order = "pathway_class", select = c("ko00561", "ko00310", "ko00604", "ko04973", "ko01051", "ko00300", "ko00591", "ko00253", "ko00380", "ko00625", "ko00627", "ko00270", "ko00941", "ko04910", "ko00363", "ko02010", "ko00361", "ko00362", "ko01040", "ko04626", "ko05020", "ko05146", "ko00051"), ko_to_kegg = TRUE, p_value_bar = FALSE, colors = NULL, x_lab = "pathway_name")
# Perform pathway PCA
pathway_pca(abundance = kegg_abundance, metadata = metadata, group = "treatment")
# Filter features with p-value < 0.05
feature_with_p_0.05 <- daa_annotated_sub_method_results_df %>% filter(p_adjust < 0.05) %>% filter(!is.na(pathway_class))
# Generate pathway heatmap
pathway_heatmap(abundance = kegg_abundance %>% filter(rownames(.) %in% feature_with_p_0.05$feature) %>% rownames_to_column("feature") %>% left_join(daa_annotated_sub_method_results_df %>% select(all_of(c("feature","pathway_name"))), by
= "feature") %>% column_to_rownames("pathway_name") %>% select(-"feature"), metadata = metadata, group = "treatment")Please ensure that you modify the file paths in the code snippet to match your actual data file locations. This code will help you perform multiple group comparisons using the ALDEx2 method and generate pathway error bar plots, pathway PCA plots, and pathway heatmaps based on your metadata and KEGG abundance data.
If you have any further questions or need additional assistance, please don't hesitate to ask. I'm here to help.
Best regards,
Chen
from ggpicrust2.
Lacona
commented on September 17, 2024
OMG!!!! You sent the whole code!
That's amazing, thank you so much!
I'll try that and hope I don't have to bother you again.
Will you add multiple comparisons into your package? That would be so great.
…________________________________
Von: Caffery Yang ***@***.***>
Gesendet: Donnerstag, 29. Juni 2023 11:08
An: cafferychen777/ggpicrust2 ***@***.***>
Cc: Andrea Pietruska ***@***.***>; Mention ***@***.***>
Betreff: [EXT] Re: [cafferychen777/ggpicrust2] ggpicrust2 error in levels (Issue #34)
CAUTION: Email Originated Outside of Auburn.
@Lacona<https://github.com/Lacona>
Dear Andrea,
Thank you for reaching out and for your patience. I understand that you have been exploring the ALDEx2 method and are having trouble figuring out how to perform multiple group comparisons. Unfortunately, the $results output does not provide the desired information.
To address this issue, you can try the following code snippet:
library(readr)
library(ggpicrust2)
library(tibble)
library(tidyverse)
library(ggprism)
library(patchwork)
# Load metadata as a tibble
metadata <- read_delim("/Users/apple/Microbiome/ggpicrust2总/ggpicrust2测试/ggpicrust2_test/Lacona/Salmonella22_metadata_42R.tsv", delim = "\t", escape_double = FALSE, trim_ws = TRUE)
# Load KEGG pathway abundance
kegg_abundance <- ko2kegg_abundance("/Users/apple/Microbiome/ggpicrust2总/ggpicrust2测试/ggpicrust2_test/Lacona/pred_metagenome_unstrat (2).tsv")
# Perform pathway differential abundance analysis (DAA) using ALDEx2 method
daa_results_df <- pathway_daa(abundance = kegg_abundance, metadata = metadata, group = "treatment", daa_method = "ALDEx2", select = NULL, reference = NULL)
# Filter results for ALDEx2_Welch's t-test method
daa_sub_method_results_df <- daa_results_df[daa_results_df$method == "ALDEx2_Kruskal-Wallace test", ]
# Annotate pathway results using KO to KEGG conversion
daa_annotated_sub_method_results_df <- pathway_annotation(pathway = "KO", daa_results_df = daa_sub_method_results_df, ko_to_kegg = TRUE)
# Generate pathway error bar plot
pathway_errorbar(abundance = kegg_abundance, daa_results_df = daa_annotated_sub_method_results_df %>%
filter(!is.na(pathway_class)), Group = metadata$treatment, p_values_threshold = 0.05, order = "pathway_class", select = c("ko00561", "ko00310", "ko00604", "ko04973", "ko01051", "ko00300", "ko00591", "ko00253", "ko00380", "ko00625", "ko00627", "ko00270", "ko00941", "ko04910", "ko00363", "ko02010", "ko00361", "ko00362", "ko01040", "ko04626", "ko05020", "ko05146", "ko00051"), ko_to_kegg = TRUE, p_value_bar = FALSE, colors = NULL, x_lab = "pathway_name")
# Perform pathway PCA
pathway_pca(abundance = kegg_abundance, metadata = metadata, group = "treatment")
# Filter features with p-value < 0.05
feature_with_p_0.05 <- daa_annotated_sub_method_results_df %>% filter(p_adjust < 0.05) %>% filter(!is.na(pathway_class))
# Generate pathway heatmap
pathway_heatmap(abundance = kegg_abundance %>% filter(rownames(.) %in% feature_with_p_0.05$feature) %>% rownames_to_column("feature") %>% left_join(daa_annotated_sub_method_results_df %>% select(all_of(c("feature","pathway_name"))), by
= "feature") %>% column_to_rownames("pathway_name") %>% select(-"feature"), metadata = metadata, group = "treatment")
Please ensure that you modify the file paths in the code snippet to match your actual data file locations. This code will help you perform multiple group comparisons using the ALDEx2 method and generate pathway error bar plots, pathway PCA plots, and pathway heatmaps based on your metadata and KEGG abundance data.
If you have any further questions or need additional assistance, please don't hesitate to ask. I'm here to help.
Best regards,
Chen
[Screenshot 2023-06-30 at 00 07 56]<https://user-images.githubusercontent.com/82987086/249836041-aece1792-e868-4dcc-9145-dac35a360999.png>
[Screenshot 2023-06-30 at 00 08 16]<https://user-images.githubusercontent.com/82987086/249836113-e518d270-e239-485a-b3a7-86f87ee9a1c3.png>
[Screenshot 2023-06-30 at 00 08 31]<https://user-images.githubusercontent.com/82987086/249836183-c88af54f-08bd-4970-812e-c28be78d6f53.png>
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cafferychen777
commented on September 17, 2024
Haha, no worries! Actually multiple comparisons have always been supported
in my package. 😂 Although my example didn't include multiple
comparisons, you can definitely use them. If you have any questions or need
assistance with implementing multiple comparisons in your code, feel free
to ask. I'm here to help!
…On Fri, 30 Jun 2023 at 00:11, Lacona ***@***.***> wrote:
OMG!!!! You sent the whole code!
That's amazing, thank you so much!
I'll try that and hope I don't have to bother you again.
Will you add multiple comparisons into your package? That would be so
great.
________________________________
Von: Caffery Yang ***@***.***>
Gesendet: Donnerstag, 29. Juni 2023 11:08
An: cafferychen777/ggpicrust2 ***@***.***>
Cc: Andrea Pietruska ***@***.***>; Mention ***@***.***>
Betreff: [EXT] Re: [cafferychen777/ggpicrust2] ggpicrust2 error in levels
(Issue #34)
CAUTION: Email Originated Outside of Auburn.
@Lacona<https://github.com/Lacona>
Dear Andrea,
Thank you for reaching out and for your patience. I understand that you
have been exploring the ALDEx2 method and are having trouble figuring out
how to perform multiple group comparisons. Unfortunately, the $results
output does not provide the desired information.
To address this issue, you can try the following code snippet:
library(readr)
library(ggpicrust2)
library(tibble)
library(tidyverse)
library(ggprism)
library(patchwork)
# Load metadata as a tibble
metadata <-
read_delim("/Users/apple/Microbiome/ggpicrust2总/ggpicrust2测试/ggpicrust2_test/Lacona/Salmonella22_metadata_42R.tsv",
delim = "\t", escape_double = FALSE, trim_ws = TRUE)
# Load KEGG pathway abundance
kegg_abundance <-
ko2kegg_abundance("/Users/apple/Microbiome/ggpicrust2总/ggpicrust2测试/ggpicrust2_test/Lacona/pred_metagenome_unstrat
(2).tsv")
# Perform pathway differential abundance analysis (DAA) using ALDEx2
method
daa_results_df <- pathway_daa(abundance = kegg_abundance, metadata =
metadata, group = "treatment", daa_method = "ALDEx2", select = NULL,
reference = NULL)
# Filter results for ALDEx2_Welch's t-test method
daa_sub_method_results_df <- daa_results_df[daa_results_df$method ==
"ALDEx2_Kruskal-Wallace test", ]
# Annotate pathway results using KO to KEGG conversion
daa_annotated_sub_method_results_df <- pathway_annotation(pathway = "KO",
daa_results_df = daa_sub_method_results_df, ko_to_kegg = TRUE)
# Generate pathway error bar plot
pathway_errorbar(abundance = kegg_abundance, daa_results_df =
daa_annotated_sub_method_results_df %>%
filter(!is.na(pathway_class)), Group = metadata$treatment,
p_values_threshold = 0.05, order = "pathway_class", select = c("ko00561",
"ko00310", "ko00604", "ko04973", "ko01051", "ko00300", "ko00591",
"ko00253", "ko00380", "ko00625", "ko00627", "ko00270", "ko00941",
"ko04910", "ko00363", "ko02010", "ko00361", "ko00362", "ko01040",
"ko04626", "ko05020", "ko05146", "ko00051"), ko_to_kegg = TRUE, p_value_bar
= FALSE, colors = NULL, x_lab = "pathway_name")
# Perform pathway PCA
pathway_pca(abundance = kegg_abundance, metadata = metadata, group =
"treatment")
# Filter features with p-value < 0.05
feature_with_p_0.05 <- daa_annotated_sub_method_results_df %>%
filter(p_adjust < 0.05) %>% filter(!is.na(pathway_class))
# Generate pathway heatmap
pathway_heatmap(abundance = kegg_abundance %>% filter(rownames(.) %in%
feature_with_p_0.05$feature) %>% rownames_to_column("feature") %>%
left_join(daa_annotated_sub_method_results_df %>%
select(all_of(c("feature","pathway_name"))), by
= "feature") %>% column_to_rownames("pathway_name") %>%
select(-"feature"), metadata = metadata, group = "treatment")
Please ensure that you modify the file paths in the code snippet to match
your actual data file locations. This code will help you perform multiple
group comparisons using the ALDEx2 method and generate pathway error bar
plots, pathway PCA plots, and pathway heatmaps based on your metadata and
KEGG abundance data.
If you have any further questions or need additional assistance, please
don't hesitate to ask. I'm here to help.
Best regards,
Chen
[Screenshot 2023-06-30 at 00 07 56]<
https://user-images.githubusercontent.com/82987086/249836041-aece1792-e868-4dcc-9145-dac35a360999.png>
[Screenshot 2023-06-30 at 00 08 16]<
https://user-images.githubusercontent.com/82987086/249836113-e518d270-e239-485a-b3a7-86f87ee9a1c3.png>
[Screenshot 2023-06-30 at 00 08 31]<
https://user-images.githubusercontent.com/82987086/249836183-c88af54f-08bd-4970-812e-c28be78d6f53.png>
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Lacona
commented on September 17, 2024
I'm very sorry, but I have to come back to you about the multiple group comparisons.
Your code worked perfectly fine and I got a nice heatmap, but I still struggle with identifying which groups are significantly different.
Usually the output of a post hoc test is something like this for all possible group comparisons:
groups p-adj sign
C - ST 0.00015 **
C - Ctrl 0.3 ns
... ... ...
The output of pathway_daa with ALDEx2 is:
feature method group1 group 2 group3 group4 p-value
KO: KW C CST Ctrl ST 0.005
How do I figure out if there is significant difference between the groups and the p-values for each comparison?
________________________________
Von: Caffery Yang ***@***.***>
Gesendet: Donnerstag, 29. Juni 2023 11:23
An: cafferychen777/ggpicrust2 ***@***.***>
Cc: Andrea Pietruska ***@***.***>; Mention ***@***.***>
Betreff: [EXT] Re: [cafferychen777/ggpicrust2] ggpicrust2 error in levels (Issue #34)
CAUTION: Email Originated Outside of Auburn.
Haha, no worries! Actually multiple comparisons have always been supported
in my package. 😂 Although your example didn't include multiple
comparisons, you can definitely use them. If you have any questions or need
assistance with implementing multiple comparisons in your code, feel free
to ask. I'm here to help!
On Fri, 30 Jun 2023 at 00:11, Lacona ***@***.***> wrote:
OMG!!!! You sent the whole code!
That's amazing, thank you so much!
I'll try that and hope I don't have to bother you again.
Will you add multiple comparisons into your package? That would be so
great.
________________________________
Von: Caffery Yang ***@***.***>
Gesendet: Donnerstag, 29. Juni 2023 11:08
An: cafferychen777/ggpicrust2 ***@***.***>
Cc: Andrea Pietruska ***@***.***>; Mention ***@***.***>
Betreff: [EXT] Re: [cafferychen777/ggpicrust2] ggpicrust2 error in levels
(Issue #34)
CAUTION: Email Originated Outside of Auburn.
@Lacona<https://github.com/Lacona>
Dear Andrea,
Thank you for reaching out and for your patience. I understand that you
have been exploring the ALDEx2 method and are having trouble figuring out
how to perform multiple group comparisons. Unfortunately, the $results
output does not provide the desired information.
To address this issue, you can try the following code snippet:
library(readr)
library(ggpicrust2)
library(tibble)
library(tidyverse)
library(ggprism)
library(patchwork)
# Load metadata as a tibble
metadata <-
read_delim("/Users/apple/Microbiome/ggpicrust2总/ggpicrust2测试/ggpicrust2_test/Lacona/Salmonella22_metadata_42R.tsv",
delim = "\t", escape_double = FALSE, trim_ws = TRUE)
# Load KEGG pathway abundance
kegg_abundance <-
ko2kegg_abundance("/Users/apple/Microbiome/ggpicrust2总/ggpicrust2测试/ggpicrust2_test/Lacona/pred_metagenome_unstrat
(2).tsv")
# Perform pathway differential abundance analysis (DAA) using ALDEx2
method
daa_results_df <- pathway_daa(abundance = kegg_abundance, metadata =
metadata, group = "treatment", daa_method = "ALDEx2", select = NULL,
reference = NULL)
# Filter results for ALDEx2_Welch's t-test method
daa_sub_method_results_df <- daa_results_df[daa_results_df$method ==
"ALDEx2_Kruskal-Wallace test", ]
# Annotate pathway results using KO to KEGG conversion
daa_annotated_sub_method_results_df <- pathway_annotation(pathway = "KO",
daa_results_df = daa_sub_method_results_df, ko_to_kegg = TRUE)
# Generate pathway error bar plot
pathway_errorbar(abundance = kegg_abundance, daa_results_df =
daa_annotated_sub_method_results_df %>%
filter(!is.na(pathway_class)), Group = metadata$treatment,
p_values_threshold = 0.05, order = "pathway_class", select = c("ko00561",
"ko00310", "ko00604", "ko04973", "ko01051", "ko00300", "ko00591",
"ko00253", "ko00380", "ko00625", "ko00627", "ko00270", "ko00941",
"ko04910", "ko00363", "ko02010", "ko00361", "ko00362", "ko01040",
"ko04626", "ko05020", "ko05146", "ko00051"), ko_to_kegg = TRUE, p_value_bar
= FALSE, colors = NULL, x_lab = "pathway_name")
# Perform pathway PCA
pathway_pca(abundance = kegg_abundance, metadata = metadata, group =
"treatment")
# Filter features with p-value < 0.05
feature_with_p_0.05 <- daa_annotated_sub_method_results_df %>%
filter(p_adjust < 0.05) %>% filter(!is.na(pathway_class))
# Generate pathway heatmap
pathway_heatmap(abundance = kegg_abundance %>% filter(rownames(.) %in%
feature_with_p_0.05$feature) %>% rownames_to_column("feature") %>%
left_join(daa_annotated_sub_method_results_df %>%
select(all_of(c("feature","pathway_name"))), by
= "feature") %>% column_to_rownames("pathway_name") %>%
select(-"feature"), metadata = metadata, group = "treatment")
Please ensure that you modify the file paths in the code snippet to match
your actual data file locations. This code will help you perform multiple
group comparisons using the ALDEx2 method and generate pathway error bar
plots, pathway PCA plots, and pathway heatmaps based on your metadata and
KEGG abundance data.
If you have any further questions or need additional assistance, please
don't hesitate to ask. I'm here to help.
Best regards,
Chen
[Screenshot 2023-06-30 at 00 07 56]<
https://user-images.githubusercontent.com/82987086/249836041-aece1792-e868-4dcc-9145-dac35a360999.png>
[Screenshot 2023-06-30 at 00 08 16]<
https://user-images.githubusercontent.com/82987086/249836113-e518d270-e239-485a-b3a7-86f87ee9a1c3.png>
[Screenshot 2023-06-30 at 00 08 31]<
https://user-images.githubusercontent.com/82987086/249836183-c88af54f-08bd-4970-812e-c28be78d6f53.png>
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cafferychen777
commented on September 17, 2024
Dear,
I apologize for the inconvenience. To determine if there are significant
differences between the groups and obtain the p-values for each comparison,
I recommend following these steps:
1. Subset the dataset: Extract the two groups you want to compare from your
original dataset.
2. Run the pipeline again: Apply the necessary statistical tests or post
hoc analyses specifically on the subsetted data. This will allow you to
obtain the p-values and identify significant differences between the
selected groups.
By performing these steps, you will be able to focus on the specific group
comparisons and obtain the relevant statistical information. If you have
any further questions or need assistance with the process, please feel free
to ask.
Best regards,
…On Fri, 30 Jun 2023 at 16:07, Lacona ***@***.***> wrote:
I'm very sorry, but I have to come back to you about the multiple group
comparisons.
Your code worked perfectly fine and I got a nice heatmap, but I still
struggle with identifying which groups are significantly different.
Usually the output of a post hoc test is something like this for all
possible group comparisons:
groups p-adj sign
C - ST 0.00015 **
C - Ctrl 0.3 ns
... ... ...
The output of pathway_daa with ALDEx2 is:
feature method group1 group 2 group3 group4 p-value
KO: KW C CST Ctrl ST 0.005
How do I figure out if there is significant difference between the groups
and the p-values for each comparison?
________________________________
Von: Caffery Yang ***@***.***>
Gesendet: Donnerstag, 29. Juni 2023 11:23
An: cafferychen777/ggpicrust2 ***@***.***>
Cc: Andrea Pietruska ***@***.***>; Mention ***@***.***>
Betreff: [EXT] Re: [cafferychen777/ggpicrust2] ggpicrust2 error in levels
(Issue #34)
CAUTION: Email Originated Outside of Auburn.
Haha, no worries! Actually multiple comparisons have always been supported
in my package. 😂 Although your example didn't include multiple
comparisons, you can definitely use them. If you have any questions or
need
assistance with implementing multiple comparisons in your code, feel free
to ask. I'm here to help!
On Fri, 30 Jun 2023 at 00:11, Lacona ***@***.***> wrote:
> OMG!!!! You sent the whole code!
> That's amazing, thank you so much!
> I'll try that and hope I don't have to bother you again.
>
> Will you add multiple comparisons into your package? That would be so
> great.
> ________________________________
> Von: Caffery Yang ***@***.***>
> Gesendet: Donnerstag, 29. Juni 2023 11:08
> An: cafferychen777/ggpicrust2 ***@***.***>
> Cc: Andrea Pietruska ***@***.***>; Mention ***@***.***>
> Betreff: [EXT] Re: [cafferychen777/ggpicrust2] ggpicrust2 error in
levels
> (Issue #34)
>
> CAUTION: Email Originated Outside of Auburn.
>
> @Lacona<https://github.com/Lacona>
>
> Dear Andrea,
>
> Thank you for reaching out and for your patience. I understand that you
> have been exploring the ALDEx2 method and are having trouble figuring
out
> how to perform multiple group comparisons. Unfortunately, the $results
> output does not provide the desired information.
>
> To address this issue, you can try the following code snippet:
>
> library(readr)
> library(ggpicrust2)
> library(tibble)
> library(tidyverse)
> library(ggprism)
> library(patchwork)
>
> # Load metadata as a tibble
> metadata <-
>
read_delim("/Users/apple/Microbiome/ggpicrust2总/ggpicrust2测试/ggpicrust2_test/Lacona/Salmonella22_metadata_42R.tsv",
> delim = "\t", escape_double = FALSE, trim_ws = TRUE)
>
> # Load KEGG pathway abundance
> kegg_abundance <-
>
ko2kegg_abundance("/Users/apple/Microbiome/ggpicrust2总/ggpicrust2测试/ggpicrust2_test/Lacona/pred_metagenome_unstrat
> (2).tsv")
>
> # Perform pathway differential abundance analysis (DAA) using ALDEx2
> method
> daa_results_df <- pathway_daa(abundance = kegg_abundance, metadata =
> metadata, group = "treatment", daa_method = "ALDEx2", select = NULL,
> reference = NULL)
>
> # Filter results for ALDEx2_Welch's t-test method
> daa_sub_method_results_df <- daa_results_df[daa_results_df$method ==
> "ALDEx2_Kruskal-Wallace test", ]
>
> # Annotate pathway results using KO to KEGG conversion
> daa_annotated_sub_method_results_df <- pathway_annotation(pathway =
"KO",
> daa_results_df = daa_sub_method_results_df, ko_to_kegg = TRUE)
>
> # Generate pathway error bar plot
> pathway_errorbar(abundance = kegg_abundance, daa_results_df =
> daa_annotated_sub_method_results_df %>%
> filter(!is.na(pathway_class)), Group = metadata$treatment,
> p_values_threshold = 0.05, order = "pathway_class", select =
c("ko00561",
> "ko00310", "ko00604", "ko04973", "ko01051", "ko00300", "ko00591",
> "ko00253", "ko00380", "ko00625", "ko00627", "ko00270", "ko00941",
> "ko04910", "ko00363", "ko02010", "ko00361", "ko00362", "ko01040",
> "ko04626", "ko05020", "ko05146", "ko00051"), ko_to_kegg = TRUE,
p_value_bar
> = FALSE, colors = NULL, x_lab = "pathway_name")
>
> # Perform pathway PCA
> pathway_pca(abundance = kegg_abundance, metadata = metadata, group =
> "treatment")
>
> # Filter features with p-value < 0.05
> feature_with_p_0.05 <- daa_annotated_sub_method_results_df %>%
> filter(p_adjust < 0.05) %>% filter(!is.na(pathway_class))
>
> # Generate pathway heatmap
> pathway_heatmap(abundance = kegg_abundance %>% filter(rownames(.) %in%
> feature_with_p_0.05$feature) %>% rownames_to_column("feature") %>%
> left_join(daa_annotated_sub_method_results_df %>%
> select(all_of(c("feature","pathway_name"))), by
>
> = "feature") %>% column_to_rownames("pathway_name") %>%
> select(-"feature"), metadata = metadata, group = "treatment")
>
> Please ensure that you modify the file paths in the code snippet to
match
> your actual data file locations. This code will help you perform
multiple
> group comparisons using the ALDEx2 method and generate pathway error bar
> plots, pathway PCA plots, and pathway heatmaps based on your metadata
and
> KEGG abundance data.
>
> If you have any further questions or need additional assistance, please
> don't hesitate to ask. I'm here to help.
>
> Best regards,
> Chen
> [Screenshot 2023-06-30 at 00 07 56]<
>
https://user-images.githubusercontent.com/82987086/249836041-aece1792-e868-4dcc-9145-dac35a360999.png>
>
> [Screenshot 2023-06-30 at 00 08 16]<
>
https://user-images.githubusercontent.com/82987086/249836113-e518d270-e239-485a-b3a7-86f87ee9a1c3.png>
>
> [Screenshot 2023-06-30 at 00 08 31]<
>
https://user-images.githubusercontent.com/82987086/249836183-c88af54f-08bd-4970-812e-c28be78d6f53.png>
>
>
> ―
> Reply to this email directly, view it on GitHub<
>
#34 (comment)>,
> or unsubscribe<
>
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Lacona
commented on September 17, 2024
Thank you for your patience!
Well, how do I extract the treatment groups from my kegg_abundance, when there is no specification for treatments in the dataset? or from the daa_results where there are already all 4 groups mentioned for every KO? I can't see any chance of subsetting my data for the groups I want to compare.
________________________________
Von: Caffery Yang ***@***.***>
Gesendet: Freitag, 30. Juni 2023 03:41
An: cafferychen777/ggpicrust2 ***@***.***>
Cc: Andrea Pietruska ***@***.***>; Mention ***@***.***>
Betreff: [EXT] Re: [cafferychen777/ggpicrust2] ggpicrust2 error in levels (Issue #34)
CAUTION: Email Originated Outside of Auburn.
Dear,
I apologize for the inconvenience. To determine if there are significant
differences between the groups and obtain the p-values for each comparison,
I recommend following these steps:
1. Subset the dataset: Extract the two groups you want to compare from your
original dataset.
2. Run the pipeline again: Apply the necessary statistical tests or post
hoc analyses specifically on the subsetted data. This will allow you to
obtain the p-values and identify significant differences between the
selected groups.
By performing these steps, you will be able to focus on the specific group
comparisons and obtain the relevant statistical information. If you have
any further questions or need assistance with the process, please feel free
to ask.
Best regards,
On Fri, 30 Jun 2023 at 16:07, Lacona ***@***.***> wrote:
I'm very sorry, but I have to come back to you about the multiple group
comparisons.
Your code worked perfectly fine and I got a nice heatmap, but I still
struggle with identifying which groups are significantly different.
Usually the output of a post hoc test is something like this for all
possible group comparisons:
groups p-adj sign
C - ST 0.00015 **
C - Ctrl 0.3 ns
... ... ...
The output of pathway_daa with ALDEx2 is:
feature method group1 group 2 group3 group4 p-value
KO: KW C CST Ctrl ST 0.005
How do I figure out if there is significant difference between the groups
and the p-values for each comparison?
________________________________
Von: Caffery Yang ***@***.***>
Gesendet: Donnerstag, 29. Juni 2023 11:23
An: cafferychen777/ggpicrust2 ***@***.***>
Cc: Andrea Pietruska ***@***.***>; Mention ***@***.***>
Betreff: [EXT] Re: [cafferychen777/ggpicrust2] ggpicrust2 error in levels
(Issue #34)
CAUTION: Email Originated Outside of Auburn.
Haha, no worries! Actually multiple comparisons have always been supported
in my package. 😂 Although your example didn't include multiple
comparisons, you can definitely use them. If you have any questions or
need
assistance with implementing multiple comparisons in your code, feel free
to ask. I'm here to help!
On Fri, 30 Jun 2023 at 00:11, Lacona ***@***.***> wrote:
> OMG!!!! You sent the whole code!
> That's amazing, thank you so much!
> I'll try that and hope I don't have to bother you again.
>
> Will you add multiple comparisons into your package? That would be so
> great.
> ________________________________
> Von: Caffery Yang ***@***.***>
> Gesendet: Donnerstag, 29. Juni 2023 11:08
> An: cafferychen777/ggpicrust2 ***@***.***>
> Cc: Andrea Pietruska ***@***.***>; Mention ***@***.***>
> Betreff: [EXT] Re: [cafferychen777/ggpicrust2] ggpicrust2 error in
levels
> (Issue #34)
>
> CAUTION: Email Originated Outside of Auburn.
>
> @Lacona<https://github.com/Lacona>
>
> Dear Andrea,
>
> Thank you for reaching out and for your patience. I understand that you
> have been exploring the ALDEx2 method and are having trouble figuring
out
> how to perform multiple group comparisons. Unfortunately, the $results
> output does not provide the desired information.
>
> To address this issue, you can try the following code snippet:
>
> library(readr)
> library(ggpicrust2)
> library(tibble)
> library(tidyverse)
> library(ggprism)
> library(patchwork)
>
> # Load metadata as a tibble
> metadata <-
>
read_delim("/Users/apple/Microbiome/ggpicrust2总/ggpicrust2测试/ggpicrust2_test/Lacona/Salmonella22_metadata_42R.tsv",
> delim = "\t", escape_double = FALSE, trim_ws = TRUE)
>
> # Load KEGG pathway abundance
> kegg_abundance <-
>
ko2kegg_abundance("/Users/apple/Microbiome/ggpicrust2总/ggpicrust2测试/ggpicrust2_test/Lacona/pred_metagenome_unstrat
> (2).tsv")
>
> # Perform pathway differential abundance analysis (DAA) using ALDEx2
> method
> daa_results_df <- pathway_daa(abundance = kegg_abundance, metadata =
> metadata, group = "treatment", daa_method = "ALDEx2", select = NULL,
> reference = NULL)
>
> # Filter results for ALDEx2_Welch's t-test method
> daa_sub_method_results_df <- daa_results_df[daa_results_df$method ==
> "ALDEx2_Kruskal-Wallace test", ]
>
> # Annotate pathway results using KO to KEGG conversion
> daa_annotated_sub_method_results_df <- pathway_annotation(pathway =
"KO",
> daa_results_df = daa_sub_method_results_df, ko_to_kegg = TRUE)
>
> # Generate pathway error bar plot
> pathway_errorbar(abundance = kegg_abundance, daa_results_df =
> daa_annotated_sub_method_results_df %>%
> filter(!is.na(pathway_class)), Group = metadata$treatment,
> p_values_threshold = 0.05, order = "pathway_class", select =
c("ko00561",
> "ko00310", "ko00604", "ko04973", "ko01051", "ko00300", "ko00591",
> "ko00253", "ko00380", "ko00625", "ko00627", "ko00270", "ko00941",
> "ko04910", "ko00363", "ko02010", "ko00361", "ko00362", "ko01040",
> "ko04626", "ko05020", "ko05146", "ko00051"), ko_to_kegg = TRUE,
p_value_bar
> = FALSE, colors = NULL, x_lab = "pathway_name")
>
> # Perform pathway PCA
> pathway_pca(abundance = kegg_abundance, metadata = metadata, group =
> "treatment")
>
> # Filter features with p-value < 0.05
> feature_with_p_0.05 <- daa_annotated_sub_method_results_df %>%
> filter(p_adjust < 0.05) %>% filter(!is.na(pathway_class))
>
> # Generate pathway heatmap
> pathway_heatmap(abundance = kegg_abundance %>% filter(rownames(.) %in%
> feature_with_p_0.05$feature) %>% rownames_to_column("feature") %>%
> left_join(daa_annotated_sub_method_results_df %>%
> select(all_of(c("feature","pathway_name"))), by
>
> = "feature") %>% column_to_rownames("pathway_name") %>%
> select(-"feature"), metadata = metadata, group = "treatment")
>
> Please ensure that you modify the file paths in the code snippet to
match
> your actual data file locations. This code will help you perform
multiple
> group comparisons using the ALDEx2 method and generate pathway error bar
> plots, pathway PCA plots, and pathway heatmaps based on your metadata
and
> KEGG abundance data.
>
> If you have any further questions or need additional assistance, please
> don't hesitate to ask. I'm here to help.
>
> Best regards,
> Chen
> [Screenshot 2023-06-30 at 00 07 56]<
>
https://user-images.githubusercontent.com/82987086/249836041-aece1792-e868-4dcc-9145-dac35a360999.png>
>
> [Screenshot 2023-06-30 at 00 08 16]<
>
https://user-images.githubusercontent.com/82987086/249836113-e518d270-e239-485a-b3a7-86f87ee9a1c3.png>
>
> [Screenshot 2023-06-30 at 00 08 31]<
>
https://user-images.githubusercontent.com/82987086/249836183-c88af54f-08bd-4970-812e-c28be78d6f53.png>
>
>
> ―
> Reply to this email directly, view it on GitHub<
>
#34 (comment)>,
> or unsubscribe<
>
https://github.com/notifications/unsubscribe-auth/ART6PUWIJWXJOCPFFCFRZVTXNWSBJANCNFSM6AAAAAAZJ37UHA>.
>
> You are receiving this because you were mentioned.Message ID:
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>
> —
> Reply to this email directly, view it on GitHub
> <
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> or unsubscribe
> <
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from ggpicrust2.
Lacona
commented on September 17, 2024
Maybe there is kind of a solurion in ALDEx2: you can get the expected clr value for each feature by setting the parameter: include.sample.summary = FALSE using aldex.effect()
But how can I apply that chunk of code to ggpicrust?
________________________________
Von: Andrea Pietruska ***@***.***>
Gesendet: Freitag, 30. Juni 2023 10:57
An: cafferychen777/ggpicrust2 ***@***.***>
Betreff: AW: [EXT] Re: [cafferychen777/ggpicrust2] ggpicrust2 error in levels (Issue #34)
Thank you for your patience!
Well, how do I extract the treatment groups from my kegg_abundance, when there is no specification for treatments in the dataset? or from the daa_results where there are already all 4 groups mentioned for every KO? I can't see any chance of subsetting my data for the groups I want to compare.
________________________________
Von: Caffery Yang ***@***.***>
Gesendet: Freitag, 30. Juni 2023 03:41
An: cafferychen777/ggpicrust2 ***@***.***>
Cc: Andrea Pietruska ***@***.***>; Mention ***@***.***>
Betreff: [EXT] Re: [cafferychen777/ggpicrust2] ggpicrust2 error in levels (Issue #34)
CAUTION: Email Originated Outside of Auburn.
Dear,
I apologize for the inconvenience. To determine if there are significant
differences between the groups and obtain the p-values for each comparison,
I recommend following these steps:
1. Subset the dataset: Extract the two groups you want to compare from your
original dataset.
2. Run the pipeline again: Apply the necessary statistical tests or post
hoc analyses specifically on the subsetted data. This will allow you to
obtain the p-values and identify significant differences between the
selected groups.
By performing these steps, you will be able to focus on the specific group
comparisons and obtain the relevant statistical information. If you have
any further questions or need assistance with the process, please feel free
to ask.
Best regards,
On Fri, 30 Jun 2023 at 16:07, Lacona ***@***.***> wrote:
I'm very sorry, but I have to come back to you about the multiple group
comparisons.
Your code worked perfectly fine and I got a nice heatmap, but I still
struggle with identifying which groups are significantly different.
Usually the output of a post hoc test is something like this for all
possible group comparisons:
groups p-adj sign
C - ST 0.00015 **
C - Ctrl 0.3 ns
... ... ...
The output of pathway_daa with ALDEx2 is:
feature method group1 group 2 group3 group4 p-value
KO: KW C CST Ctrl ST 0.005
How do I figure out if there is significant difference between the groups
and the p-values for each comparison?
________________________________
Von: Caffery Yang ***@***.***>
Gesendet: Donnerstag, 29. Juni 2023 11:23
An: cafferychen777/ggpicrust2 ***@***.***>
Cc: Andrea Pietruska ***@***.***>; Mention ***@***.***>
Betreff: [EXT] Re: [cafferychen777/ggpicrust2] ggpicrust2 error in levels
(Issue #34)
CAUTION: Email Originated Outside of Auburn.
Haha, no worries! Actually multiple comparisons have always been supported
in my package. 😂 Although your example didn't include multiple
comparisons, you can definitely use them. If you have any questions or
need
assistance with implementing multiple comparisons in your code, feel free
to ask. I'm here to help!
On Fri, 30 Jun 2023 at 00:11, Lacona ***@***.***> wrote:
> OMG!!!! You sent the whole code!
> That's amazing, thank you so much!
> I'll try that and hope I don't have to bother you again.
>
> Will you add multiple comparisons into your package? That would be so
> great.
> ________________________________
> Von: Caffery Yang ***@***.***>
> Gesendet: Donnerstag, 29. Juni 2023 11:08
> An: cafferychen777/ggpicrust2 ***@***.***>
> Cc: Andrea Pietruska ***@***.***>; Mention ***@***.***>
> Betreff: [EXT] Re: [cafferychen777/ggpicrust2] ggpicrust2 error in
levels
> (Issue #34)
>
> CAUTION: Email Originated Outside of Auburn.
>
> @Lacona<https://github.com/Lacona>
>
> Dear Andrea,
>
> Thank you for reaching out and for your patience. I understand that you
> have been exploring the ALDEx2 method and are having trouble figuring
out
> how to perform multiple group comparisons. Unfortunately, the $results
> output does not provide the desired information.
>
> To address this issue, you can try the following code snippet:
>
> library(readr)
> library(ggpicrust2)
> library(tibble)
> library(tidyverse)
> library(ggprism)
> library(patchwork)
>
> # Load metadata as a tibble
> metadata <-
>
read_delim("/Users/apple/Microbiome/ggpicrust2总/ggpicrust2测试/ggpicrust2_test/Lacona/Salmonella22_metadata_42R.tsv",
> delim = "\t", escape_double = FALSE, trim_ws = TRUE)
>
> # Load KEGG pathway abundance
> kegg_abundance <-
>
ko2kegg_abundance("/Users/apple/Microbiome/ggpicrust2总/ggpicrust2测试/ggpicrust2_test/Lacona/pred_metagenome_unstrat
> (2).tsv")
>
> # Perform pathway differential abundance analysis (DAA) using ALDEx2
> method
> daa_results_df <- pathway_daa(abundance = kegg_abundance, metadata =
> metadata, group = "treatment", daa_method = "ALDEx2", select = NULL,
> reference = NULL)
>
> # Filter results for ALDEx2_Welch's t-test method
> daa_sub_method_results_df <- daa_results_df[daa_results_df$method ==
> "ALDEx2_Kruskal-Wallace test", ]
>
> # Annotate pathway results using KO to KEGG conversion
> daa_annotated_sub_method_results_df <- pathway_annotation(pathway =
"KO",
> daa_results_df = daa_sub_method_results_df, ko_to_kegg = TRUE)
>
> # Generate pathway error bar plot
> pathway_errorbar(abundance = kegg_abundance, daa_results_df =
> daa_annotated_sub_method_results_df %>%
> filter(!is.na(pathway_class)), Group = metadata$treatment,
> p_values_threshold = 0.05, order = "pathway_class", select =
c("ko00561",
> "ko00310", "ko00604", "ko04973", "ko01051", "ko00300", "ko00591",
> "ko00253", "ko00380", "ko00625", "ko00627", "ko00270", "ko00941",
> "ko04910", "ko00363", "ko02010", "ko00361", "ko00362", "ko01040",
> "ko04626", "ko05020", "ko05146", "ko00051"), ko_to_kegg = TRUE,
p_value_bar
> = FALSE, colors = NULL, x_lab = "pathway_name")
>
> # Perform pathway PCA
> pathway_pca(abundance = kegg_abundance, metadata = metadata, group =
> "treatment")
>
> # Filter features with p-value < 0.05
> feature_with_p_0.05 <- daa_annotated_sub_method_results_df %>%
> filter(p_adjust < 0.05) %>% filter(!is.na(pathway_class))
>
> # Generate pathway heatmap
> pathway_heatmap(abundance = kegg_abundance %>% filter(rownames(.) %in%
> feature_with_p_0.05$feature) %>% rownames_to_column("feature") %>%
> left_join(daa_annotated_sub_method_results_df %>%
> select(all_of(c("feature","pathway_name"))), by
>
> = "feature") %>% column_to_rownames("pathway_name") %>%
> select(-"feature"), metadata = metadata, group = "treatment")
>
> Please ensure that you modify the file paths in the code snippet to
match
> your actual data file locations. This code will help you perform
multiple
> group comparisons using the ALDEx2 method and generate pathway error bar
> plots, pathway PCA plots, and pathway heatmaps based on your metadata
and
> KEGG abundance data.
>
> If you have any further questions or need additional assistance, please
> don't hesitate to ask. I'm here to help.
>
> Best regards,
> Chen
> [Screenshot 2023-06-30 at 00 07 56]<
>
https://user-images.githubusercontent.com/82987086/249836041-aece1792-e868-4dcc-9145-dac35a360999.png>
>
> [Screenshot 2023-06-30 at 00 08 16]<
>
https://user-images.githubusercontent.com/82987086/249836113-e518d270-e239-485a-b3a7-86f87ee9a1c3.png>
>
> [Screenshot 2023-06-30 at 00 08 31]<
>
https://user-images.githubusercontent.com/82987086/249836183-c88af54f-08bd-4970-812e-c28be78d6f53.png>
>
>
> ―
> Reply to this email directly, view it on GitHub<
>
#34 (comment)>,
> or unsubscribe<
>
https://github.com/notifications/unsubscribe-auth/ART6PUWIJWXJOCPFFCFRZVTXNWSBJANCNFSM6AAAAAAZJ37UHA>.
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> <
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cafferychen777
commented on September 17, 2024
Subsetting the dataset is a very easy thing. Maybe you guys should exercise your R skills instead of keeping telling something that is not related to ggpicrust.
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Lacona
commented on September 17, 2024
Well, thank you for your help!
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An: cafferychen777/ggpicrust2 ***@***.***>
Cc: Andrea Pietruska ***@***.***>; Mention ***@***.***>
Betreff: [EXT] Re: [cafferychen777/ggpicrust2] ggpicrust2 error in levels (Issue #34)
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Subsetting the dataset is a very easy thing. Maybe you guys should exercise your R skills instead of keeping telling something that is not related to ggpicrust.
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Related Issues (20)
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- Error in switch(file_format, .txt = abundance <- readr::read_delim(file, : EXPR must be a length 1 vector** HOT 2
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