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Independent Study to consolidate this week

Answer - don’t look until you have tried!
# Carry out the post-hoc test
-library(emmeans)
+library(emmeans)
 
-emmeans(mod, ~ spray) |> pairs()
+emmeans(mod, ~ spray) |> pairs()
 
 # the signifcant comparisons are:
 # contrast         estimate   SE df t.ratio p.value
diff --git a/pgt52m/week-8/study_after_workshop_files/figure-html/unnamed-chunk-14-1.png b/pgt52m/week-8/study_after_workshop_files/figure-html/unnamed-chunk-14-1.png
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The ANOVA is significant but this only tells us that species matters, meaning at least two of the means differ. To find out which means differ, we need a post-hoc test. A post-hoc (“after this”) test is done after a significant ANOVA test. There are several possible post-hoc tests and we will be using Tukey’s HSD (honestly significant difference) test (Tukey 1949) implemented in the emmeans (Lenth 2023) package.

Load the package

Carry out the post-hoc test

-
emmeans(mod, ~ species) |> pairs()
+
emmeans(mod, ~ species) |> pairs()
 contrast                        estimate   SE df t.ratio p.value
  Bladdernose Seal - Harbour Seal    -6.69 2.08 87  -3.224  0.0050
@@ -753,7 +753,7 @@ 

Workshop

A plot can be used to visualise the result of the post-hoc which can be especially useful when there are very many comparisons.

Plot the results of the post-hoc test:

-
emmeans(mod, ~ species) |> plot()
+
emmeans(mod, ~ species) |> plot()

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Independent Study to consolidate this week

Answer - don’t look until you have tried!
# Carry out the post-hoc test
-library(emmeans)
+library(emmeans)
 
-emmeans(mod, ~ spray) |> pairs()
+emmeans(mod, ~ spray) |> pairs()
 
 # the signifcant comparisons are:
 # contrast         estimate   SE df t.ratio p.value
diff --git a/r4babs2/week-4/study_after_workshop_files/figure-html/unnamed-chunk-14-1.png b/r4babs2/week-4/study_after_workshop_files/figure-html/unnamed-chunk-14-1.png
index 94bf7bf7bc4b4647c2cb15b255fb9138fd0112a3..c661ce0b9ed3b47acaec70ba8a2cff14424c9a77 100644
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The ANOVA is significant but this only tells us that species matters, meaning at least two of the means differ. To find out which means differ, we need a post-hoc test. A post-hoc (“after this”) test is done after a significant ANOVA test. There are several possible post-hoc tests and we will be using Tukey’s HSD (honestly significant difference) test (Tukey 1949) implemented in the emmeans (Lenth 2023) package.

Load the package

Carry out the post-hoc test

-
emmeans(mod, ~ species) |> pairs()
+
emmeans(mod, ~ species) |> pairs()
 contrast                        estimate   SE df t.ratio p.value
  Bladdernose Seal - Harbour Seal    -6.69 2.08 87  -3.224  0.0050
@@ -621,7 +621,7 @@ 

Workshop

A plot can be used to visualise the result of the post-hoc which can be especially useful when there are very many comparisons.

Plot the results of the post-hoc test:

-
emmeans(mod, ~ species) |> plot()
+
emmeans(mod, ~ species) |> plot()

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--git a/r4babs2/week-5/study_after_workshop.html b/r4babs2/week-5/study_after_workshop.html index e758379..e6c4966 100644 --- a/r4babs2/week-5/study_after_workshop.html +++ b/r4babs2/week-5/study_after_workshop.html @@ -417,7 +417,7 @@

Independent Study to consolidate this week

If you have just opened RStudio you will want to load the tidyverse and emmeans packages.

Exercises

    @@ -502,7 +502,7 @@

    Independent Study to consolidate this week

-
Answer - don’t look until you have tried!
emmeans(mod, ~ spp * region) |> pairs()
+
Answer - don’t look until you have tried!
emmeans(mod, ~ spp * region) |> pairs()
 
 # As we suspected all the difference are between  F.concocti north
 # and the other three groups. 
diff --git a/r4babs2/week-5/workshop.html b/r4babs2/week-5/workshop.html
index 68a5a39..c379e36 100644
--- a/r4babs2/week-5/workshop.html
+++ b/r4babs2/week-5/workshop.html
@@ -654,11 +654,11 @@ 

Workshop

We need a post-hoc test to see which comparisons are significant and can again use then emmeans (Lenth 2023) package.

Load the package

Carry out the post-hoc test

-
emmeans(mod, ~ BrainRegion * Diet) |> pairs()
+
emmeans(mod, ~ BrainRegion * Diet) |> pairs()
 contrast                     estimate  SE df t.ratio p.value
  DB Control - MSN Control        5.695 1.5 36   3.786  0.0030
@@ -675,7 +675,7 @@ 

Workshop

A plot can be used to visualise the result of the post hoc which can be especially useful when there are very many comparisons.

Plot the results of the post-hoc test:

-
emmeans(mod, ~ BrainRegion * Diet) |> plot()
+
emmeans(mod, ~ BrainRegion * Diet) |> plot()

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zmbLmqUAs1v!&?e;?k@IzAJrPYs1sItZu5u!1bLsa;O=J=d;e&3uex&a{@RnegwU*j z5pW>uHo?@A_B3Nm-ev*HYs`QCLvEp-VKv`{tQJE0nOYI4W`(6#^JBvO^pv%?g#yU Dbx(2& diff --git a/r4babs4/week-2/workshop.html b/r4babs4/week-2/workshop.html index d6c5440..7332a59 100644 --- a/r4babs4/week-2/workshop.html +++ b/r4babs4/week-2/workshop.html @@ -779,34 +779,46 @@

Workshop

🎬 Run the automated quality control:

-
Quality control for the file: ECOLIGreen_ISOTYPE
-43.33% of anomalous cells detected in the flow rate check. 
+
Quality control for the file: ECOLIGreen_ISOTYPE
+
+
+
43.33% of anomalous cells detected in the flow rate check. 
 0% of anomalous cells detected in signal acquisition check. 
 0% of anomalous cells detected in the dynamic range check. 
-Quality control for the file: ECOLIGreen_TNFAPC
-44.96% of anomalous cells detected in the flow rate check. 
+Quality control for the file: ECOLIGreen_TNFAPC
+
+
+
44.96% of anomalous cells detected in the flow rate check. 
 0% of anomalous cells detected in signal acquisition check. 
 0% of anomalous cells detected in the dynamic range check. 
-Quality control for the file: LPS_ISOTYPE
-6.39% of anomalous cells detected in the flow rate check. 
+Quality control for the file: LPS_ISOTYPE
+
+
+
6.39% of anomalous cells detected in the flow rate check. 
 0% of anomalous cells detected in signal acquisition check. 
 0% of anomalous cells detected in the dynamic range check. 
-Quality control for the file: LPS_TNFAPC
-46.56% of anomalous cells detected in the flow rate check. 
+Quality control for the file: LPS_TNFAPC
+
+
+
46.56% of anomalous cells detected in the flow rate check. 
 0% of anomalous cells detected in signal acquisition check. 
 0% of anomalous cells detected in the dynamic range check. 
-Quality control for the file: MEDIA_ISOTYPE
-45.73% of anomalous cells detected in the flow rate check. 
+Quality control for the file: MEDIA_ISOTYPE
+
+
+
45.73% of anomalous cells detected in the flow rate check. 
 0% of anomalous cells detected in signal acquisition check. 
 0% of anomalous cells detected in the dynamic range check. 
-Quality control for the file: MEDIA_TNFAPC
-45.85% of anomalous cells detected in the flow rate check. 
+Quality control for the file: MEDIA_TNFAPC
+
+
+
45.85% of anomalous cells detected in the flow rate check. 
 0% of anomalous cells detected in signal acquisition check. 
 0% of anomalous cells detected in the dynamic range check. 
-
fs_clean <- flow_auto_qc(fs, 
+
fs_clean <- flow_auto_qc(fs, 
                          folder_results = "sample-QC")

❓ What has been removed through the QC process?

@@ -816,7 +828,7 @@

Workshop

Plotting the distributions of our variables is very useful for getting an overview of our data. We’ll just look at the first flowFrame to get over the principles.

🎬 Plot the distribution of the TNFa_APC signal in the first flowFrame in the flowSet:

-
exprs(fs_clean[[1]]) |> 
+
exprs(fs_clean[[1]]) |> 
   data.frame() |> 
   ggplot(aes(x = TNFa_APC_Lin)) +
   geom_histogram(bins = 100)
@@ -830,7 +842,7 @@

Workshop

This is a very skewed distribution which makes visualisation hard. It is common to log skewed distributions to improve visualisation. We have a column called TNFa_APC_Log so let’s look at that.

🎬 Plot the distribution of TNFa_APC_Log :

-
exprs(fs_clean[[1]]) |> 
+
exprs(fs_clean[[1]]) |> 
   data.frame() |> 
   ggplot(aes(x = TNFa_APC_Log)) +
   geom_histogram(bins = 100)
@@ -844,7 +856,7 @@

Workshop

Hmmmm, The column labelled Log, does not seem to be.

🎬 Plot the distribution of the logged TNFa_APC_Lin column:

-
exprs(fs_clean[[1]]) |> 
+
exprs(fs_clean[[1]]) |> 
   data.frame() |> 
   ggplot(aes(x = log(TNFa_APC_Lin))) +
   geom_histogram(bins = 100)
@@ -862,17 +874,17 @@

Workshop

We need to apply the transformation only to apply it to the TNFa_APC_Lin and E_coli_FITC_Lin columns. These in the 10th and 13th columns of the flowFrames.

🎬 Estimate the transformation from the data:

-
trans <- estimateLogicle(fs_clean[[1]],
+
trans <- estimateLogicle(fs_clean[[1]],
                          colnames(fs_clean[,c(10, 13)]))

🎬 Apply the transformation to all theTNFa_APC_Lin and E_coli_FITC_Lin columns the flowSet:

-
# apply the transformation
+
# apply the transformation
 fs_clean_trans <- transform(fs_clean, trans)

🎬 Examine the effect:

-
exprs(fs_clean_trans[[1]]) |> 
+
exprs(fs_clean_trans[[1]]) |> 
   data.frame() |> 
   ggplot(aes(x = TNFa_APC_Lin)) +
   geom_histogram(bins = 100)
@@ -889,7 +901,7 @@

Workshop

We will be using familiar tools like group_by() and summarise(), filter() and ggplot()

🎬 Put the transformed data into a data frame:

-
# Put into a data frame for ease of use
+
# Put into a data frame for ease of use
 clean_trans <- fsApply(fs_clean_trans, exprs) |> data.frame() 

fsApply(flowSet, function) applies a function to every flowFrame in a flowSet. Here we are accessed the data matirx in each with exprs().

@@ -901,7 +913,7 @@

Workshop

🎬 Add the sample name to each row:

-
clean_trans <- clean_trans |> 
+
clean_trans <- clean_trans |> 
   dplyr::mutate(sample = rep(sampleNames(fs_clean_trans),
                              times = c(dim(fs_clean_trans[[1]])["events"],
                                        dim(fs_clean_trans[[2]])["events"],
@@ -913,7 +925,7 @@ 

Workshop

The sample name contains the information about which antibody and treatment the sample was. Our analysis will be a little easier if we add this information to separate columns. We can extract the antibody and treatment from the sample name using the extract() function and two “regular expressions”.

🎬 Add columns for treatment and antibody by extracting that information from the sample name:

-
clean_trans <- clean_trans |> 
+
clean_trans <- clean_trans |> 
   extract(sample, 
           remove = FALSE,
           c("treatment", "antibody"),
@@ -938,7 +950,7 @@ 

Workshop

🎬 View the dataframe to see the new columns.

Our treatments have an order. Media is the control, LPS should be next and ECOLIGreen should be last. We can use the fct_relevel() function to put groups in order so that our graphs are better to interpret.

-
clean_trans <- clean_trans |> 
+
clean_trans <- clean_trans |> 
   mutate(treatment = fct_relevel(treatment, c("MEDIA",
                                               "LPS",
                                               "ECOLIGreen")))
@@ -947,7 +959,7 @@

Workshop

We have cleaned and transform our data. It is a good idea to save it at this point so that we can start from here if we need to.

🎬 Save the data:

-
write_csv(clean_trans, "data-processed/ai_clean_logicle_trans.csv")
+
write_csv(clean_trans, "data-processed/ai_clean_logicle_trans.csv")

Quality control 2: ‘Gating’ to Removing debris

Some of the cells will have died and broken during the experiment. We need to perform quality control on our data to remove observations that have that have the size and granularity that is not typical of a live cell. This is done by filtering on the forward scatter (size) and side scatter (granularity) channels.

@@ -956,7 +968,7 @@

Workshop

We could use geom_point() but many points will overlap so it is difficult to see the density of points. geom_hex() puts the points in bins and colours the bin with the number of points.

🎬 Plot the forward scatter and side scatter for each sample

-
ggplot(clean_trans, aes(x = FS_Lin, y = SS_Lin)) +
+
ggplot(clean_trans, aes(x = FS_Lin, y = SS_Lin)) +
   geom_hex(bins = 128) +
   scale_fill_viridis_c() +
   facet_grid(antibody ~ treatment) +
@@ -973,19 +985,19 @@ 

Workshop

Use the zoom on the plot window to better see the cloud of points and the axis values. You need to use minimum and maximum \(x\) and \(y\) values to define the rectangle. I chose the the values below but you might judge the gate differently.

🎬 Define the minimum and maximum \(x\) and \(y\) values for the gate:

-
xmin <- 15000
+
xmin <- 15000
 xmax <- 35000
 ymin <- 7500
 ymax <- 28000

🎬 Put those values in a dataframe so we can plot the gate on the hexbin plot:

-
box <- data.frame(x = c(xmin, xmin, xmax, xmax),
+
box <- data.frame(x = c(xmin, xmin, xmax, xmax),
                   y = c(ymin, ymax, ymax, ymin))

🎬 Plot the forward scatter and side scatter for each sample with the gate:

-
ggplot(clean_trans, aes(x = FS_Lin, y = SS_Lin)) +
+
ggplot(clean_trans, aes(x = FS_Lin, y = SS_Lin)) +
   geom_hex(bins = 128) +
   scale_fill_viridis_c() +
   geom_polygon(data = box, aes(x = x, y = y), 
@@ -1005,7 +1017,7 @@ 

Workshop

We have drawn the gate on the plot but we need to use it to filter out the debris (rows) from our data. We want only the cells with FS_Lin values that are between the xmin and xmax and SS_Lin that are between ymin and ymax values we chose.

🎬 Filter the data to remove the debris:

-
# filter out the debris
+
# filter out the debris
 clean_trans_nondebris <- clean_trans |> 
   filter(between(FS_Lin, xmin, xmax),
          between(SS_Lin, ymin, ymax)) 
@@ -1013,7 +1025,7 @@

Workshop

clean_trans_nondebris now has 161010 cells. We should report how many cells were in the gate for each sample.

🎬 Find the number of cells in each sample after flowAI cleaning:

-
# 
+
# 
 clean_trans_n <-  clean_trans |> 
   group_by(antibody, treatment) |> 
   summarise(n = n()) 
@@ -1063,7 +1075,7 @@

Workshop

🎬 Find the number of cells in each sample after flowAI cleaning and removing debris

-
# number of cells in each sample
+
# number of cells in each sample
 clean_trans_nondebris_n <-  clean_trans_nondebris |> 
   group_by(antibody, treatment) |> 
   summarise(n_nondebris = n()) 
@@ -1113,7 +1125,7 @@

Workshop

🎬 Join two data frames together using the combination of the treatment and the antibody and calculate what % cells were removed from each sample:

-
clean_trans_nondebris_n <- clean_trans_nondebris_n |> 
+
clean_trans_nondebris_n <- clean_trans_nondebris_n |> 
   left_join(clean_trans_n, by = c("antibody", "treatment")) |> 
   mutate(perc_nondebris = round(n_nondebris/n * 100, 1) )
@@ -1176,7 +1188,7 @@

Workshop

🎬 Plot the forward scatter and side scatter for each sample with the gate and the number of cells in each sample

-
ggplot(clean_trans, aes(x = FS_Lin, y = SS_Lin)) +
+
ggplot(clean_trans, aes(x = FS_Lin, y = SS_Lin)) +
   geom_hex(bins = 128) +
   scale_fill_viridis_c() +
   geom_polygon(data = box, aes(x = x, y = y), 
@@ -1200,7 +1212,7 @@ 

Workshop

🎬 Plot the TNFa_APC_Lin and E_coli_FITC_Lin signals for these live cells:

-
ggplot(clean_trans_nondebris, aes(x = E_coli_FITC_Lin, 
+
ggplot(clean_trans_nondebris, aes(x = E_coli_FITC_Lin, 
                                   y = TNFa_APC_Lin)) +
   geom_hex(bins = 128) +
   scale_fill_viridis_c() +
@@ -1226,11 +1238,11 @@ 

Workshop

We can use this to set a threshold for the TNFa_APC_Lin signal and label the cells as positive or negative for TNF-α.

🎬 Define the threshold for the TNFa_APC_Lin signal:

-
apc_cut <- 2
+
apc_cut <- 2

🎬 Plot the TNFa_APC_Lin and E_coli_FITC_Lin with the threshold for the TNFa_APC_Lin signal:

-
ggplot(clean_trans_nondebris, aes(x = E_coli_FITC_Lin, 
+
ggplot(clean_trans_nondebris, aes(x = E_coli_FITC_Lin, 
                                   y = TNFa_APC_Lin)) +
   geom_hex(bins = 128) +
   geom_hline(yintercept = apc_cut, 
@@ -1247,13 +1259,13 @@ 

Workshop

🎬 Add a label, tnfa, to the data to indicate if the cell is positive or negative for TNF-α

-
clean_trans_nondebris <- clean_trans_nondebris |> 
+
clean_trans_nondebris <- clean_trans_nondebris |> 
   mutate(tnfa = case_when(TNFa_APC_Lin < apc_cut ~ "TNF-α -'ve",
                                TNFa_APC_Lin >= apc_cut ~ "TNF-α +'ve"))

🎬 Summarise each group by finding the number of TNF-α positive cells and the mean TNFa_APC_Lin signal for each group:

-
clean_trans_nondebris_tfna_pos <- clean_trans_nondebris |> 
+
clean_trans_nondebris_tfna_pos <- clean_trans_nondebris |> 
   filter(tnfa == "TNF-α +'ve") |>
   group_by(antibody, treatment) |>
   summarise(n_pos = n(),
@@ -1311,7 +1323,7 @@ 

Workshop

🎬 We can add the number of non-debris cells to this data frame and calculate the percentage of non-debris cells that are positive for TNF-α:

-
clean_trans_nondebris_tfna_pos <- 
+
clean_trans_nondebris_tfna_pos <- 
   clean_trans_nondebris_tfna_pos |> 
   left_join(clean_trans_nondebris_n, by = c("antibody", "treatment")) |> 
   mutate(perc_tfna_pos = round(n_pos/n_nondebris * 100, 1) )
@@ -1409,11 +1421,11 @@

Workshop

For either antibody when the treatment is media or LPS there is no E.coli so that level of E_coli_FITC_Lin signal is the control. When E_coli_FITC_Lin is < 2 then cells are negative for E.coli; when E_coli_FITC_Lin is > 2 then cells are positive for E.coli. The fact that this is also 2 is a coincidence - for your own data your values for both of these are likely to differ.

🎬 Define the threshold for the E_coli_FITC_Lin signal:

-
fitc_cut <- 2
+
fitc_cut <- 2

🎬 Plot the TNFa_APC_Lin and E_coli_FITC_Lin with the thresholds for both signals:

-
ggplot(clean_trans_nondebris, aes(x = E_coli_FITC_Lin, 
+
ggplot(clean_trans_nondebris, aes(x = E_coli_FITC_Lin, 
                                   y = TNFa_APC_Lin)) +
   geom_hex(bins = 128) +
   geom_hline(yintercept = apc_cut, 
@@ -1432,7 +1444,7 @@ 

Workshop

🎬 Add a label, fitc, to the data to indicate if the cell is positive or negative for FITC

-
clean_trans_nondebris <- clean_trans_nondebris |> 
+
clean_trans_nondebris <- clean_trans_nondebris |> 
   mutate(fitc = case_when(E_coli_FITC_Lin < fitc_cut ~ "FITC -'ve",
                                E_coli_FITC_Lin >= fitc_cut ~ "FITC +'ve"))
@@ -1440,7 +1452,7 @@

Workshop

We have filtered out the dead cells and debris and added labels to the data to indicate if the cells are positive or negative for TNF-α and E. coli. It would be a good idea to save this data to file so we can start from importing this data in the future rather than having to repeat all of the steps we have done so far.

🎬 Save the data:

-
write_csv(clean_trans_nondebris, "data-processed/live_labelled.csv")
+
write_csv(clean_trans_nondebris, "data-processed/live_labelled.csv")

Look after future you!

🎬 This workshop is a template for the analysis of your own data. Go back through your script and check you understand what you have done. Can you identify where you will need to edit? Do you understand what you will need to assess in order to make edits needed for your own data? Examine all of the objects in the environment to check you understand what they are and why they are there.

diff --git a/search.json b/search.json index 45b64bf..b964885 100644 --- a/search.json +++ b/search.json @@ -416,7 +416,7 @@ "href": "r4babs4/week-2/workshop.html#quality-control-1-automated-instrument-issues", "title": "Workshop", "section": "Quality control 1: Automated instrument issues", - "text": "Quality control 1: Automated instrument issues\nflowAI is a package that provides a set of tools for automated QC of flow cytometry data. It checks for:\n\nchanges in instrument speed,\nchanges signal intensity\ncells that are outside the dynamic range\n\nIt generates a new flowSet with the problematic events removed along with a folder containing filtered .fcs files and a quality control report in html format. It will take a few minutes to do all files.\n🎬 Run the automated quality control:\n\n\nQuality control for the file: ECOLIGreen_ISOTYPE\n43.33% of anomalous cells detected in the flow rate check. \n0% of anomalous cells detected in signal acquisition check. \n0% of anomalous cells detected in the dynamic range check. \nQuality control for the file: ECOLIGreen_TNFAPC\n44.96% of anomalous cells detected in the flow rate check. \n0% of anomalous cells detected in signal acquisition check. \n0% of anomalous cells detected in the dynamic range check. \nQuality control for the file: LPS_ISOTYPE\n6.39% of anomalous cells detected in the flow rate check. \n0% of anomalous cells detected in signal acquisition check. \n0% of anomalous cells detected in the dynamic range check. \nQuality control for the file: LPS_TNFAPC\n46.56% of anomalous cells detected in the flow rate check. \n0% of anomalous cells detected in signal acquisition check. \n0% of anomalous cells detected in the dynamic range check. \nQuality control for the file: MEDIA_ISOTYPE\n45.73% of anomalous cells detected in the flow rate check. \n0% of anomalous cells detected in signal acquisition check. \n0% of anomalous cells detected in the dynamic range check. \nQuality control for the file: MEDIA_TNFAPC\n45.85% of anomalous cells detected in the flow rate check. \n0% of anomalous cells detected in signal acquisition check. \n0% of anomalous cells detected in the dynamic range check. \n\n\n\nfs_clean <- flow_auto_qc(fs, \n folder_results = \"sample-QC\")\n\n❓ What has been removed through the QC process?\n❓ What is in the folder “sample-QC”?\nThis is an example report: ECOLIGreen_TNFAPC flowAI report\nGet a feel of your data: Plot distributions\nPlotting the distributions of our variables is very useful for getting an overview of our data. We’ll just look at the first flowFrame to get over the principles.\n🎬 Plot the distribution of the TNFa_APC signal in the first flowFrame in the flowSet:\n\nexprs(fs_clean[[1]]) |> \n data.frame() |> \n ggplot(aes(x = TNFa_APC_Lin)) +\n geom_histogram(bins = 100)\n\n\n\n\n\n\n\nThis is a very skewed distribution which makes visualisation hard. It is common to log skewed distributions to improve visualisation. We have a column called TNFa_APC_Log so let’s look at that.\n🎬 Plot the distribution of TNFa_APC_Log :\n\nexprs(fs_clean[[1]]) |> \n data.frame() |> \n ggplot(aes(x = TNFa_APC_Log)) +\n geom_histogram(bins = 100)\n\n\n\n\n\n\n\nHmmmm, The column labelled Log, does not seem to be.\n🎬 Plot the distribution of the logged TNFa_APC_Lin column:\n\nexprs(fs_clean[[1]]) |> \n data.frame() |> \n ggplot(aes(x = log(TNFa_APC_Lin))) +\n geom_histogram(bins = 100)\n\n\n\n\n\n\n\nNow our data are easier to see.", + "text": "Quality control 1: Automated instrument issues\nflowAI is a package that provides a set of tools for automated QC of flow cytometry data. It checks for:\n\nchanges in instrument speed,\nchanges signal intensity\ncells that are outside the dynamic range\n\nIt generates a new flowSet with the problematic events removed along with a folder containing filtered .fcs files and a quality control report in html format. It will take a few minutes to do all files.\n🎬 Run the automated quality control:\n\n\nQuality control for the file: ECOLIGreen_ISOTYPE\n\n\n43.33% of anomalous cells detected in the flow rate check. \n0% of anomalous cells detected in signal acquisition check. \n0% of anomalous cells detected in the dynamic range check. \nQuality control for the file: ECOLIGreen_TNFAPC\n\n\n44.96% of anomalous cells detected in the flow rate check. \n0% of anomalous cells detected in signal acquisition check. \n0% of anomalous cells detected in the dynamic range check. \nQuality control for the file: LPS_ISOTYPE\n\n\n6.39% of anomalous cells detected in the flow rate check. \n0% of anomalous cells detected in signal acquisition check. \n0% of anomalous cells detected in the dynamic range check. \nQuality control for the file: LPS_TNFAPC\n\n\n46.56% of anomalous cells detected in the flow rate check. \n0% of anomalous cells detected in signal acquisition check. \n0% of anomalous cells detected in the dynamic range check. \nQuality control for the file: MEDIA_ISOTYPE\n\n\n45.73% of anomalous cells detected in the flow rate check. \n0% of anomalous cells detected in signal acquisition check. \n0% of anomalous cells detected in the dynamic range check. \nQuality control for the file: MEDIA_TNFAPC\n\n\n45.85% of anomalous cells detected in the flow rate check. \n0% of anomalous cells detected in signal acquisition check. \n0% of anomalous cells detected in the dynamic range check. \n\n\n\nfs_clean <- flow_auto_qc(fs, \n folder_results = \"sample-QC\")\n\n❓ What has been removed through the QC process?\n❓ What is in the folder “sample-QC”?\nThis is an example report: ECOLIGreen_TNFAPC flowAI report\nGet a feel of your data: Plot distributions\nPlotting the distributions of our variables is very useful for getting an overview of our data. We’ll just look at the first flowFrame to get over the principles.\n🎬 Plot the distribution of the TNFa_APC signal in the first flowFrame in the flowSet:\n\nexprs(fs_clean[[1]]) |> \n data.frame() |> \n ggplot(aes(x = TNFa_APC_Lin)) +\n geom_histogram(bins = 100)\n\n\n\n\n\n\n\nThis is a very skewed distribution which makes visualisation hard. It is common to log skewed distributions to improve visualisation. We have a column called TNFa_APC_Log so let’s look at that.\n🎬 Plot the distribution of TNFa_APC_Log :\n\nexprs(fs_clean[[1]]) |> \n data.frame() |> \n ggplot(aes(x = TNFa_APC_Log)) +\n geom_histogram(bins = 100)\n\n\n\n\n\n\n\nHmmmm, The column labelled Log, does not seem to be.\n🎬 Plot the distribution of the logged TNFa_APC_Lin column:\n\nexprs(fs_clean[[1]]) |> \n data.frame() |> \n ggplot(aes(x = log(TNFa_APC_Lin))) +\n geom_histogram(bins = 100)\n\n\n\n\n\n\n\nNow our data are easier to see.", "crumbs": [ "BABS 4", "Week 2: DA 2 Immunobiology",