Flow Analysis
Kelsey Murt
2025-12-05
Flow Analysis
Introduction:
Load libraries
## Warning: package 'tidyverse' was built under R version 4.3.3## Warning: package 'tidyr' was built under R version 4.3.3## Warning: package 'readr' was built under R version 4.3.3## Warning: package 'purrr' was built under R version 4.3.3## Warning: package 'dplyr' was built under R version 4.3.3## Warning: package 'stringr' was built under R version 4.3.3## Warning: package 'lubridate' was built under R version 4.3.3## Warning: package 'janitor' was built under R version 4.3.3## Warning: package 'readxl' was built under R version 4.3.3Importing Data
Note: Need to fix the size of 07142023 file in FlowJO
Import old data
Combining data
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## (3): ...1, Double pos Median Egfp SYBR Green-A, SRBR Median Egfp SYBR G... dbl
## (28): All Events Count, All Events % Total, All Events % Parent, All Eve...
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## • `` -> `...1`Combining for Set 2
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## • `` -> `...1`Combining for Set 3
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## (3): ...1, Double pos Median Egfp SYBR Green-A, SRBR Median Egfp SYBR G... dbl
## (28): All Events Count, All Events % Total, All Events % Parent, All Eve...
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## Rows: 54 Columns: 31
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## (3): ...1, Double pos Median Egfp SYBR Green-A, SRBR Median Egfp SYBR G... dbl
## (28): All Events Count, All Events % Total, All Events % Parent, All Eve...
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## New names:
## Rows: 54 Columns: 31
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## (3): ...1, Double pos Median Egfp SYBR Green-A, SRBR Median Egfp SYBR G... dbl
## (28): All Events Count, All Events % Total, All Events % Parent, All Eve...
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## Rows: 54 Columns: 31
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## (3): ...1, Double pos Median Egfp SYBR Green-A, SRBR Median Egfp SYBR G... dbl
## (28): All Events Count, All Events % Total, All Events % Parent, All Eve...
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## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## New names:
## Rows: 54 Columns: 31
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## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (3): ...1, Double pos Median Egfp SYBR Green-A, SRBR Median Egfp SYBR G... dbl
## (28): All Events Count, All Events % Total, All Events % Parent, All Eve...
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## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`Separating Columns
Merging with IDs
Getting summary tables
Adding Biraj Samples
Plots
## # A tibble: 3 × 6
## Sample_ID pfcrt pmII Day pct_dd2_mean pct_dd2_se
## <chr> <chr> <chr> <fct> <dbl> <dbl>
## 1 IVC17-OM-0651 I218F 2 Day4 83.2 0.386
## 2 IVC17-OM-0751 I218F 2 Day4 95.3 0.450
## 3 IVC17-OM-809 I218F 2 Day4 86.7 1.17
##
## === Day 2 Model ===##
## Call:
## lm(formula = pct_dd2_mean ~ pfcrt + pmII, data = day_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.1097 -4.9731 -0.4386 5.7076 23.0704
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 80.5186 4.2729 18.844 < 2e-16 ***
## pfcrtG353V -8.3491 5.7866 -1.443 0.16100
## pfcrtH97Y -16.1513 4.7733 -3.384 0.00228 **
## pfcrtI218F -6.2570 4.9451 -1.265 0.21698
## pmII2 0.2415 4.1755 0.058 0.95432
## pmII>2 6.0612 4.4383 1.366 0.18374
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.708 on 26 degrees of freedom
## Multiple R-squared: 0.3413, Adjusted R-squared: 0.2147
## F-statistic: 2.695 on 5 and 26 DF, p-value: 0.04321##
## Significant terms:## Estimate Std. Error t value Pr(>|t|) term sig_label
## (Intercept) 80.51859 4.272942 18.84383 1.111198e-16 (Intercept) ***
## pfcrtH97Y -16.15128 4.773345 -3.38364 2.276748e-03 pfcrtH97Y **##
## === Day 4 Model ===##
## Call:
## lm(formula = pct_dd2_mean ~ pfcrt + pmII, data = day_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.1127 -2.9150 0.4722 3.2993 9.1894
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 93.517 2.466 37.927 < 2e-16 ***
## pfcrtG353V -3.831 3.339 -1.147 0.26172
## pfcrtH97Y -8.873 2.754 -3.221 0.00342 **
## pfcrtI218F -3.677 2.854 -1.289 0.20889
## pmII2 -1.201 2.409 -0.498 0.62237
## pmII>2 1.846 2.561 0.721 0.47754
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.602 on 26 degrees of freedom
## Multiple R-squared: 0.3168, Adjusted R-squared: 0.1854
## F-statistic: 2.411 on 5 and 26 DF, p-value: 0.06355##
## Significant terms:## Estimate Std. Error t value Pr(>|t|) term sig_label
## (Intercept) 93.516606 2.465702 37.926978 2.721141e-24 (Intercept) ***
## pfcrtH97Y -8.873483 2.754459 -3.221498 3.415609e-03 pfcrtH97Y **##
## === Day 6 Model ===##
## Call:
## lm(formula = pct_dd2_mean ~ pfcrt + pmII, data = day_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.8326 -1.5648 0.4128 2.0571 5.1892
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 97.3262 1.5392 63.231 < 2e-16 ***
## pfcrtG353V -0.2216 2.0845 -0.106 0.91615
## pfcrtH97Y -5.1903 1.7195 -3.019 0.00563 **
## pfcrtI218F -1.1921 1.7813 -0.669 0.50924
## pmII2 -1.5708 1.5041 -1.044 0.30595
## pmII>2 -0.2301 1.5988 -0.144 0.88665
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.497 on 26 degrees of freedom
## Multiple R-squared: 0.3552, Adjusted R-squared: 0.2313
## F-statistic: 2.865 on 5 and 26 DF, p-value: 0.03436##
## Significant terms:## Estimate Std. Error t value Pr(>|t|) term sig_label
## (Intercept) 97.326210 1.539209 63.231315 5.313805e-30 (Intercept) ***
## pfcrtH97Y -5.190312 1.719465 -3.018562 5.625854e-03 pfcrtH97Y **
## Warning: package 'ggpubr' was built under R version 4.3.3##
## === Day 2 Linear Model ===##
## Call:
## lm(formula = pct_dd2_mean ~ pfcrt + pmII, data = day_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.1097 -4.9731 -0.4386 5.7076 23.0704
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 80.5186 4.2729 18.844 < 2e-16 ***
## pfcrtG353V -8.3491 5.7866 -1.443 0.16100
## pfcrtH97Y -16.1513 4.7733 -3.384 0.00228 **
## pfcrtI218F -6.2570 4.9451 -1.265 0.21698
## pmII2 0.2415 4.1755 0.058 0.95432
## pmII>2 6.0612 4.4383 1.366 0.18374
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.708 on 26 degrees of freedom
## Multiple R-squared: 0.3413, Adjusted R-squared: 0.2147
## F-statistic: 2.695 on 5 and 26 DF, p-value: 0.04321##
## === Day 2 Kruskal-Wallis Tests (pmII within each pfcrt) ===## Day pfcrt chi_squared p_value sig_label
## Kruskal-Wallis chi-squared Day2 F145I 1.1428571 0.5647181 ns
## Kruskal-Wallis chi-squared1 Day2 G353V 0.4000000 0.8187308 ns
## Kruskal-Wallis chi-squared2 Day2 H97Y 1.3939394 0.4980924 ns
## Kruskal-Wallis chi-squared3 Day2 I218F 0.6222222 0.7326325 ns##
## === Day 4 Linear Model ===##
## Call:
## lm(formula = pct_dd2_mean ~ pfcrt + pmII, data = day_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.1127 -2.9150 0.4722 3.2993 9.1894
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 93.517 2.466 37.927 < 2e-16 ***
## pfcrtG353V -3.831 3.339 -1.147 0.26172
## pfcrtH97Y -8.873 2.754 -3.221 0.00342 **
## pfcrtI218F -3.677 2.854 -1.289 0.20889
## pmII2 -1.201 2.409 -0.498 0.62237
## pmII>2 1.846 2.561 0.721 0.47754
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.602 on 26 degrees of freedom
## Multiple R-squared: 0.3168, Adjusted R-squared: 0.1854
## F-statistic: 2.411 on 5 and 26 DF, p-value: 0.06355##
## === Day 4 Kruskal-Wallis Tests (pmII within each pfcrt) ===## Day pfcrt chi_squared p_value sig_label
## Kruskal-Wallis chi-squared Day4 F145I 0.5714286 0.7514773 ns
## Kruskal-Wallis chi-squared1 Day4 G353V 0.0000000 1.0000000 ns
## Kruskal-Wallis chi-squared2 Day4 H97Y 1.3030303 0.5212554 ns
## Kruskal-Wallis chi-squared3 Day4 I218F 1.1555556 0.5611440 ns##
## === Day 6 Linear Model ===##
## Call:
## lm(formula = pct_dd2_mean ~ pfcrt + pmII, data = day_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.8326 -1.5648 0.4128 2.0571 5.1892
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 97.3262 1.5392 63.231 < 2e-16 ***
## pfcrtG353V -0.2216 2.0845 -0.106 0.91615
## pfcrtH97Y -5.1903 1.7195 -3.019 0.00563 **
## pfcrtI218F -1.1921 1.7813 -0.669 0.50924
## pmII2 -1.5708 1.5041 -1.044 0.30595
## pmII>2 -0.2301 1.5988 -0.144 0.88665
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.497 on 26 degrees of freedom
## Multiple R-squared: 0.3552, Adjusted R-squared: 0.2313
## F-statistic: 2.865 on 5 and 26 DF, p-value: 0.03436##
## === Day 6 Kruskal-Wallis Tests (pmII within each pfcrt) ===## Day pfcrt chi_squared p_value sig_label
## Kruskal-Wallis chi-squared Day6 F145I 0.57142857 0.7514773 ns
## Kruskal-Wallis chi-squared1 Day6 G353V 0.00000000 1.0000000 ns
## Kruskal-Wallis chi-squared2 Day6 H97Y 0.65909091 0.7192506 ns
## Kruskal-Wallis chi-squared3 Day6 I218F 0.08888889 0.9565287 ns
##
## === Summary of Kruskal-Wallis Tests ===## Day pfcrt chi_squared p_value sig_label
## Kruskal-Wallis chi-squared Day2 F145I 1.14285714 0.5647181 ns
## Kruskal-Wallis chi-squared1 Day2 G353V 0.40000000 0.8187308 ns
## Kruskal-Wallis chi-squared2 Day2 H97Y 1.39393939 0.4980924 ns
## Kruskal-Wallis chi-squared3 Day2 I218F 0.62222222 0.7326325 ns
## Kruskal-Wallis chi-squared4 Day4 F145I 0.57142857 0.7514773 ns
## Kruskal-Wallis chi-squared11 Day4 G353V 0.00000000 1.0000000 ns
## Kruskal-Wallis chi-squared21 Day4 H97Y 1.30303030 0.5212554 ns
## Kruskal-Wallis chi-squared31 Day4 I218F 1.15555556 0.5611440 ns
## Kruskal-Wallis chi-squared5 Day6 F145I 0.57142857 0.7514773 ns
## Kruskal-Wallis chi-squared12 Day6 G353V 0.00000000 1.0000000 ns
## Kruskal-Wallis chi-squared22 Day6 H97Y 0.65909091 0.7192506 ns
## Kruskal-Wallis chi-squared32 Day6 I218F 0.08888889 0.9565287 ns##
## === Kruskal-Wallis Test Results ===## Day chi_squared df p_value sig_label
## Kruskal-Wallis chi-squared Day2 8.300177 3 0.04019870 *
## Kruskal-Wallis chi-squared1 Day4 7.256880 3 0.06414555 ns
## Kruskal-Wallis chi-squared2 Day6 11.773685 3 0.00820003 **##
## === Day 2 Detailed Results ===##
## Kruskal-Wallis rank sum test
##
## data: pct_dd2_mean by pfcrt
## Kruskal-Wallis chi-squared = 8.3002, df = 3, p-value = 0.0402##
## === Day 4 Detailed Results ===##
## Kruskal-Wallis rank sum test
##
## data: pct_dd2_mean by pfcrt
## Kruskal-Wallis chi-squared = 7.2569, df = 3, p-value = 0.06415##
## === Day 6 Detailed Results ===##
## Kruskal-Wallis rank sum test
##
## data: pct_dd2_mean by pfcrt
## Kruskal-Wallis chi-squared = 11.774, df = 3, p-value = 0.0082## Registered S3 methods overwritten by 'FSA':
## method from
## confint.boot car
## hist.boot car## ## FSA v0.10.0. See citation('FSA') if used in publication.
## ## Run fishR() for related website and fishR('IFAR') for related book.##
## === Day 2 Post-hoc Dunn Test ===## Dunn (1964) Kruskal-Wallis multiple comparison## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 F145I - G353V 1.1183364 0.263423327 1.00000000
## 2 F145I - H97Y 2.7774662 0.005478454 0.03287072
## 3 G353V - H97Y 1.2756916 0.202064575 1.00000000
## 4 F145I - I218F 1.0173515 0.308986249 1.00000000
## 5 G353V - I218F -0.2548236 0.798859350 1.00000000
## 6 H97Y - I218F -1.8470576 0.064738788 0.38843273##
## === Day 6 Post-hoc Dunn Test ===## Dunn (1964) Kruskal-Wallis multiple comparison
## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 F145I - G353V 0.2808845 0.778798987 1.00000000
## 2 F145I - H97Y 3.0781198 0.002083112 0.01249867
## 3 G353V - H97Y 2.4543587 0.014113613 0.08468168
## 4 F145I - I218F 1.2053769 0.228057854 1.00000000
## 5 G353V - I218F 0.7942002 0.427078888 1.00000000
## 6 H97Y - I218F -1.9596539 0.050036259 0.30021755
##
## === Kruskal-Wallis Test Results for pmII ===## Day chi_squared df p_value sig_label
## Kruskal-Wallis chi-squared Day2 0.95454545 2 0.6204733 ns
## Kruskal-Wallis chi-squared1 Day4 0.53333333 2 0.7659283 ns
## Kruskal-Wallis chi-squared2 Day6 0.08238636 2 0.9596437 ns##
## === Day 2 Detailed Results ===##
## Kruskal-Wallis rank sum test
##
## data: pct_dd2_mean by pmII
## Kruskal-Wallis chi-squared = 0.95455, df = 2, p-value = 0.6205##
## === Day 4 Detailed Results ===##
## Kruskal-Wallis rank sum test
##
## data: pct_dd2_mean by pmII
## Kruskal-Wallis chi-squared = 0.53333, df = 2, p-value = 0.7659##
## === Day 6 Detailed Results ===##
## Kruskal-Wallis rank sum test
##
## data: pct_dd2_mean by pmII
## Kruskal-Wallis chi-squared = 0.082386, df = 2, p-value = 0.9596
##
## === Summary Statistics by pmII ===## # A tibble: 9 × 6
## Day pmII n mean median sd
## <fct> <fct> <int> <dbl> <dbl> <dbl>
## 1 Day2 1 10 73.0 74.4 9.05
## 2 Day2 2 12 72.4 74.6 11.5
## 3 Day2 >2 10 76.6 77.7 12.5
## 4 Day4 1 10 89.4 89.2 4.82
## 5 Day4 2 12 87.8 89.2 7.82
## 6 Day4 >2 10 89.9 91.0 5.62
## 7 Day6 1 10 95.4 95.3 2.12
## 8 Day6 2 12 93.7 95.0 5.48
## 9 Day6 >2 10 94.6 95.1 3.44